From 26219f0076f0c147b09f1c0619f9484eb4698e94 Mon Sep 17 00:00:00 2001 From: Milind Srivastava Date: Mon, 26 Oct 2026 18:16:05 -0400 Subject: [PATCH 1/3] refactor: move rqe-optimizer to ASAPQuery The RQE deployment optimizer is ASAPQuery's planner, so it moves there (ProjectASAP/ASAPQuery, rqe-optimizer/) with its history: the crate, its AutoSketch-vs-ASAP runner, the scripts that run, plot and summarize it, its committed results and its design docs (now rqe-optimizer/docs/). sketch-bench keeps the benchmarks and the analysis on top of them, including what turns measurements into solver input: the saturation study, the cost table, and the eval-table exporters (export_autosketch_eval_table.py, case_study_tables.py, export_case_studies.py, autosketch_benchmark_time.py). - The exporter test's committed tables move to scripts/testdata/, a copy of ASAPQuery's rqe-optimizer/results/autosketch-vs-asap-inputs/tables. end-of-file-fixer skips them: the test compares them byte for byte. - References to the moved docs point to ASAPQuery. - good_lp leaves the workspace; rqe-optimizer was its only user. 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scripts/testdata/autosketch-vs-asap-tables}/synthetic-templatesall-shared1-metrics16.json (100%) rename {rqe-optimizer/results/autosketch-vs-asap-inputs/tables => scripts/testdata/autosketch-vs-asap-tables}/synthetic-templatesall-shared1-metrics8.json (100%) rename {rqe-optimizer/results/autosketch-vs-asap-inputs/tables => scripts/testdata/autosketch-vs-asap-tables}/synthetic-templatesall-shared1.json (100%) rename {rqe-optimizer/results/autosketch-vs-asap-inputs/tables => scripts/testdata/autosketch-vs-asap-tables}/synthetic-templatesall-shared8.json (100%) diff --git a/.gitignore b/.gitignore index c17c5126..2bd4ea9c 100644 --- a/.gitignore +++ b/.gitignore @@ -10,11 +10,6 @@ # data *.csv -# The AutoSketch-vs-ASAP eval's accuracy curves are inputs, committed so it -# reruns from the repo alone. -!rqe-optimizer/results/autosketch-vs-asap-inputs/saturation/**/*.csv -# The Hydra eval's study table is an input, committed so it reruns from the repo. -!rqe-optimizer/results/autosketch-vs-asap-hydra/inputs/**/*.csv *.pcap # Rust target directories diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index bcd23f44..b2c2e8f9 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -4,6 +4,8 @@ repos: hooks: - id: trailing-whitespace - id: end-of-file-fixer + # Exported tables the exporter test compares byte for byte. + exclude: ^scripts/testdata/ - id: check-yaml - id: check-added-large-files - id: check-case-conflict diff --git a/Cargo.lock b/Cargo.lock index 2945fd27..c70dbeca 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -343,26 +343,6 @@ dependencies = [ "serde", ] -[[package]] -name = "bindgen" -version = "0.72.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "993776b509cfb49c750f11b8f07a46fa23e0a1386ffc01fb1e7d343efc387895" -dependencies = [ - "bitflags", - "cexpr", - "clang-sys", - "itertools", - "log", - "prettyplease", - "proc-macro2", - "quote", - "regex", - "rustc-hash", - "shlex 1.3.0", - "syn 2.0.117", -] - [[package]] name = "bitflags" version = "2.13.0" @@ -447,16 +427,7 @@ dependencies = [ "find-msvc-tools", "jobserver", "libc", - "shlex 2.0.1", -] - -[[package]] -name = "cexpr" -version = "0.6.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "6fac387a98bb7c37292057cffc56d62ecb629900026402633ae9160df93a8766" -dependencies = [ - "nom", + "shlex", ] [[package]] @@ -503,17 +474,6 @@ dependencies = [ "phf", ] -[[package]] -name = "clang-sys" -version = "1.9.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "157a8ba7b480713b56f4c09fd13fc3e0a22a5dfab8097ba61cbc5feef950788a" -dependencies = [ - "glob", - "libc", - "libloading", -] - [[package]] name = "clap" version = "4.6.1" @@ -555,15 +515,6 @@ version = "1.1.0" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "c8d4a3bb8b1e0c1050499d1815f5ab16d04f0959b233085fb31653fbfc9d98f9" -[[package]] -name = "cmake" -version = "0.1.58" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c0f78a02292a74a88ac736019ab962ece0bc380e3f977bf72e376c5d78ff0678" -dependencies = [ - "cc", -] - [[package]] name = "colorchoice" version = "1.0.5" @@ -983,16 +934,6 @@ version = "0.3.3" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "0cc23270f6e1808e30a928bdc84dea0b9b4136a8bc82338574f23baf47bbd280" -[[package]] -name = "good_lp" -version = "1.15.3" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c4255eb1d65eea322f73a326038f2bcec19dae13ca50fa557da19b03274b4d56" -dependencies = [ - "fnv", - "highs", -] - [[package]] name = "hashbrown" version = "0.14.5" @@ -1050,26 +991,6 @@ version = "0.4.3" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "7f24254aa9a54b5c858eaee2f5bccdb46aaf0e486a595ed5fd8f86ba55232a70" -[[package]] -name = "highs" -version = "2.4.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7dfc791427e78fdc1a0a37983f79c20f86a0b7b7dfdb5d6a57a6f72d77cd9074" -dependencies = [ - "highs-sys", - "log", -] - -[[package]] -name = "highs-sys" -version = "1.15.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e4da0c52322648e2360a5a8896d23301fa5ed99ed7607c5faf229bd0bbb30d3f" -dependencies = [ - "bindgen", - "cmake", -] - [[package]] name = "home" version = "0.5.12" @@ -1181,16 +1102,6 @@ version = "0.2.186" source = "registry+https://github.com/rust-lang/crates.io-index" checksum = "68ab91017fe16c622486840e4c83c9a37afeff978bd239b5293d61ece587de66" -[[package]] -name = "libloading" -version = "0.8.9" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "d7c4b02199fee7c5d21a5ae7d8cfa79a6ef5bb2fc834d6e9058e89c825efdc55" -dependencies = [ - "cfg-if", - "windows-link", -] - [[package]] name = "libm" version = "0.2.16" @@ -2298,22 +2209,6 @@ dependencies = [ "serde", ] -[[package]] -name = "rqe-optimizer" -version = "0.1.0" -dependencies = [ - "aqpbm-core", - "csv", - "good_lp", - "serde_json", -] - -[[package]] -name = "rustc-hash" -version = "2.1.3" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "6b1e7f9a428571be2dc5bc0505c13fb6bf936822b894ec87abf8a08a4e51742d" - [[package]] name = "rustix" version = "1.1.4" @@ -2463,12 +2358,6 @@ dependencies = [ "unsafe-libyaml-norway", ] -[[package]] -name = "shlex" -version = "1.3.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0fda2ff0d084019ba4d7c6f371c95d8fd75ce3524c3cb8fb653a3023f6323e64" - [[package]] name = "shlex" version = "2.0.1" diff --git a/Cargo.toml b/Cargo.toml index 949c9204..cc0806e1 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -7,7 +7,6 @@ members = [ "sketch-bench", "aqpbm-cli", "aqpbm-planeval", - "rqe-optimizer", ] exclude = ["sketch-runtime"] @@ -46,7 +45,6 @@ clap = { version = "4", features = ["derive", "wrap_help"] } # Errors. thiserror = "2" anyhow = "1" -good_lp = { version = "1.15", default-features = false, features = ["highs"] } # Bitflags for MetricsMask. bitflags = "2" @@ -61,7 +59,6 @@ asap_sketchlib = { version = "0.3.0", features = ["experimental"] } aqpbm-datagen = { path = "aqpbm-datagen" } aqpbm-core = { path = "aqpbm-core", default-features = false } sketch-bench = { path = "sketch-bench" } -rqe-optimizer = { path = "rqe-optimizer" } # sketch-runtime = { path = "sketch-runtime" } # parked; [profile.release] diff --git a/README.md b/README.md index d24ed43a..ba9df32f 100644 --- a/README.md +++ b/README.md @@ -519,4 +519,5 @@ separate by crates may not be necessary at this moments --> [Sketch benchmark wrapper](./docs/sketch-bench.md) -[End-to-end planning flow: traces to an rqe-optimizer plan](./docs/e2e_planning_flow.md) +[End-to-end planning flow: traces to an rqe-optimizer plan](https://github.com/ProjectASAP/ASAPQuery/blob/main/rqe-optimizer/docs/e2e_planning_flow.md) +(rqe-optimizer, its evaluation runner and its results are in ProjectASAP/ASAPQuery) diff --git a/docs/e2e_planning_flow.md b/docs/e2e_planning_flow.md deleted file mode 100644 index 741ea1d0..00000000 --- a/docs/e2e_planning_flow.md +++ /dev/null @@ -1,306 +0,0 @@ -# End-to-end planning flow: from traces to an rqe-optimizer plan - -How to plan sketch deployments for a query workload over a real trace: - -1. describe the workload as RAQEs; -2. fit the trace's parameters; -3. measure the sketches; -4. run the optimizer; -5. read the plan. - -This page follows what exists on `main` (through #183). It names the -steps that are still manual. The model itself (candidates, cost model, -eligibility) is in [`rqe_sketch_deployment_v1.md`](rqe_sketch_deployment_v1.md), -and the saturation study is in [`saturation_study.md`](saturation_study.md). - -```text -query workload ──► RAQEs (capability, S, T, G, accuracy SLA, latency SLA) - │ -trace ── fit_skew.py ─► skew_summary.csv ── (by hand) ─► WorkloadFacts - (ASAPQuery) θ, K, N, a per query per metric: labels, scrape, - card(X), value range, - data_shape per grouping - │ -study_saturation.py ─► SATURATION_DIR/ │ - accuracy grid (+ merge curves) out_grid_1e7_cost/ │ - 1e9 subset out_1e9/ │ - --phase optimizer-cost optimizer_cost/rqe_atomic_costs.json - │ │ - ▼ ▼ - rqe-optimizer: SaturationCurves::load + cost table - candidates ─► eligibility (accuracy, latency) ─► MILP ─► plan -``` - -## 1. The workload as RAQEs - -The optimizer plans repeating atomic query expressions (`rqe_optimizer::Raqe`). -Each one is a single statistic that is evaluated every `T` over the last `S`: - -| Field | Meaning | -|---|---| -| `id` | Name used in the plan output | -| `capability` | `Sum`, `Count`, `Min`, `Max`, `RateOrIncrease`, `Quantile`, `Cardinality`, `TopKByValue`, `TopKByCount` | -| `lookback_ms` | `S`, the range of the query (`[5m]` is 300,000) | -| `interval_ms` | `T`, how often it is evaluated | -| `metric` | The metric it reads; must have an entry in `WorkloadFacts` | -| `spatial_filter` | Must be empty for now. `validate_facts` rejects filters until facts carry per-filter cardinality | -| `grouping_labels` | `G`, the `by (...)` labels; empty for a whole-metric aggregate | -| `accuracy_sla` | Bound on the family's own accuracy metric. It is a ceiling for errors and a floor for top-k precision (#172) | -| `latency_sla_ms` | Optional ceiling on the modeled query latency. Only the MILP enforces it | - -A PromQL query expands into one RAQE per statistic. For example, a query -asking for 3 quantiles is 3 RAQEs, and `a / b` is the RAQEs of `a` plus those of `b`. -This expansion is done by hand today. - -Where workloads are written down: - -- **sketch-bench:** `raqes()` in - [`rqe-optimizer/examples/small_problem.rs`](../rqe-optimizer/examples/small_problem.rs) - is Rust code; edit it and rebuild. -- **ASAPQuery:** `asap-optimizer-cli --milp` reads the planner's workload YAML - (`--input_config`, the same format as `asap-planner --input_config`). Its - `metrics:` hints supply each metric's labels. ASAPQuery#804 (open) adapts - this path to the curve-based accuracy below. - -## 2. Parameters from the trace - -The optimizer needs, per metric, a `rqe_optimizer::MetricFacts`: - -| Field | What it is | Where it comes from | -|---|---|---| -| `labels` | Every label the metric's series carry | The trace's schema | -| `scrape_interval_ms` | One sample per series per scrape | The trace's sampling period (`step_s` in `skew_summary.csv`) | -| `cardinality` | `card(X)` for each label set `X` in use, including all labels (the series count) | Counted on the trace | -| `value_range` | `(lo, hi)` of positive values; sizes DDSketch | The trace's value column | -| `data_shape[G]` | Per grouping `G`: Zipf skew `zipf_s`, distinct keys per group per window `distinct_keys`, Pareto tail index `tail_index` | `skew_summary.csv` (below) | - -`data_shape` is what keys the saturation curves. A grouping without one gets -no sketch accuracy, so only exact accumulators can serve it. - -### Fitting the shape: ASAPQuery `asap-tools/dataset-analysis` - -`fit_skew.py` evaluates each query of `queries/.yaml` at every step -over the whole trace, as Prometheus would. It fits the key skew θ and the value -tail α per evaluation (see that directory's README): - -```bash -cd ASAPQuery/asap-tools/dataset-analysis -pip install -r requirements.txt -./fetch_data.sh /path/to/trace-data -python fit_skew.py --data-root /path/to/trace-data # writes results/skew_summary.csv -``` - -`results/skew_summary.csv` has one row per (dataset, query_id, kind, weight, -range). Those three key columns are: -- **kind**: `keys` or `values`. A key query (group-by keys: sums, top-k, - distinct counts) gets the Zipf fit, which gives `zipf_s` (θ) and - `distinct_keys` (K). A value query (quantiles) gets the power-law tail fit, - which gives `tail_index` (a). -- **weight** (key queries only): what each key's rank-frequency counts. With - `count` it is the row count per key; with `value`, the value sum per key, - negatives clipped. Use the one that matches how the sketch is fed: once per - sample (`count`, e.g. `topk` over `count_over_time`) or weighted by the - sample value (`value`, e.g. `topk` over `sum` or `rate`). -- **range**: the query's lookback (`instant`, `5m`, `1h`, ...), i.e. the - RAQE's `S`. Each row is that query evaluated at every step with that window. - -`data_shape` is keyed by (metric, grouping), not by range. When RAQEs on one -grouping have different ranges, take the worst case over them: the smallest θ, -the largest K and the smallest a. Use the worst case over the whole trace too, -not an average, since longer samples expose worse cases -(`saturation_conclusions.md`). The columns map onto `data_shape` as follows: - -| `skew_summary.csv` | `DataShape` / facts | Note | -|---|---|---| -| `worst_theta_cms` | `zipf_s` | Key queries. Flatter (smaller) is harder | -| `worst_K` | `distinct_keys` | Distinct keys per evaluation window | -| `worst_alpha_rank` | `tail_index` | Value queries. Steepest tail, hardest for rank error | -| `worst_alpha_memory` | — | Heaviest tail; used for DDSketch cost by `recommend_config.py`, not by the optimizer | -| `min_N`, `max_N` | — | Items per evaluation; the optimizer computes its own from `cardinality`, `scrape_interval_ms` and `S` | -| `range_s`, `step_s` | `lookback_ms`, `interval_ms` | `range_s` is empty for instant queries | -| `target_*` | `accuracy_sla` | Per-family targets from the analysis | - -Copying these numbers into `MetricFacts` is **manual**. sketch-bench's -[`scripts/recommend_config.py`](../scripts/recommend_config.py) reads the same -file, but it recommends one config per query from the curves; it doesn't write -facts for the optimizer. - -## 3. Measuring the sketches: `scripts/study_saturation.py` - -The optimizer reads two things from one directory, `SATURATION_DIR`: the -saturation curves, for accuracy, and the cost table, for CPU and memory. Both -come from `scripts/study_saturation.py`, and its configs are one grid (#174). -Run it on a quiet machine. We use CloudLab `clnode109`. Cost and timing runs -must be the only job on the machine. - -```bash -cargo build -p aqpbm-cli --release -D=SATURATION_DIR - -# Accuracy grid to N = 1e7, with merge curves (KLL needs them, #158; top-k -# merges with a heap of m·k and reads its plain curve, #182). -python3 scripts/study_saturation.py --phase accuracy --n-max 1e7 --seeds 3 --jobs 48 \ - --merge-shards-list 1,4,16,64 --out $D/out_grid_1e7_cost - -# Larger N for the points that need it: list them in a CSV with -# sketch,config,dist,param,cardinality columns. -python3 scripts/study_saturation.py --phase accuracy --n-max 1e9 --seeds 3 --jobs 16 \ - --points-from points.csv --out $D/out_1e9 - -# The cost table (serial). -python3 scripts/study_saturation.py --phase optimizer-cost --out $D/optimizer_cost -``` - -- **Grid:** `--thetas`, `--cardinalities` and `--alphas` set the data shapes, - and `SKETCHES` sets the configs. `--cost-shape` (on by default) adds the cost - table's shape (`COST_THETA` = 1.1, `COST_KEYS` = 1e4, `COST_PARETO_ALPHA` = 2) - to every config, so each cost-table row is a point on a curve. -- **Merge curves:** `--merge-shards-list` also scores the sketch merged from - `m` shards, into `saturation_merge_curve.csv`. List every `m` the workload's - merges need (`m = S / x` for the windows `x` the optimizer may pick). - Past the largest measured `m`, a merged KLL answer falls back to its - guarantee (#180). Restrict `--merge-shards-list` to the quantile family with - `--families quantile` and `--resume`. -- **Cost table:** `--phase optimizer-cost` measures each config of the families - the optimizer plans (`OPTIMIZER_FAMILIES`: top-k, cardinality, quantile) and - the exact accumulators, once each at the `COST_*` shape with `COST_N` = 1e6 - items, 5 runs after 3 warm-ups. `atomic-costs` reduces them to - `rqe_atomic_costs.json`, and every row names its `accuracy_metric`. The - phase fails if any row is skipped. -- **Resuming:** `--resume` keeps complete curves after an interruption, and - with a narrower grid it keeps the other points' rows (#182). - -### Fanning out over machines - -Accuracy runs split cleanly across machines; only the cost table needs a -machine to itself. -1. Build `approxbench` once and copy it, with `scripts/` and `configs/`, to - each machine. The machines must share the OS and libc. -2. Give each machine a disjoint set of points: whole families with - `--families`, or a CSV of points with `--points-from`. Each machine writes - its own run directory. -3. Concatenate the parts into one run directory: - - ```bash - python3 scripts/merge_study_parts.py $D/out_grid_1e7_cost part_a/grid part_b/grid - ``` - - It refuses a point that appears in two parts and mismatched headers. -4. Run `--phase optimizer-cost` alone on one machine. - -On asap_sketchlib 0.3.0, five 56-core nodes run the default grid and the -targeted 1e9 points in about 10 minutes. - -`SaturationCurves::load` expects exactly this layout: - -```text -SATURATION_DIR/ - out_grid_1e7_cost/ saturation.csv saturation_curve.csv saturation_merge_curve.csv - out_1e9/ saturation.csv saturation_curve.csv - optimizer_cost/ rqe_atomic_costs.json -``` - -A point present in both runs is taken from `out_1e9` and keeps the merge -curves of `out_grid_1e7_cost`. - -## 4. Running the optimizer - -### sketch-bench: `small_problem` - -```bash -cargo run --release -p rqe-optimizer --example small_problem -- \ - --saturation-dir $D --milp [--w-cpu 1 --w-mem 0] [--latency-sla RAQE_ID=MS ...] -``` - -Other modes: -- `--candidates-only` prints candidate counts before and after pruning; -- `--sample-mappings N` prints N feasible mappings; -- `--streaming` and `--progress-every`/`--print-first` enumerate mappings; -- `--allow-undeployable-families` also plans with families ASAPQuery can't - deploy. - -Do not run it with no mode flag on a large workload: eager enumeration keeps -every mapping in memory. - -What is checked before planning: -- `SaturationCurves::load` refuses a candidate family's curve whose - `error_metric` isn't the family's accuracy metric. That is a stale study, so - rerun it. -- It also refuses a study with no merge curves for a candidate KLL sketch. -- `check_cost_table` refuses to plan when a cost row's `accuracy_metric` isn't - its family's or its curve's, or when the row disagrees with the curve at its - `measured_at`. Rows with no grid point at their shape are printed as - unchecked. - -### ASAPQuery: `asap-optimizer-cli --milp` - -With ASAPQuery#804 (open, pinned to sketch-bench main once it merges): - -```bash -asap-optimizer-cli --milp --input_config workload.yaml \ - --data-ingestion-interval-ms 15000 \ - --workload-facts facts.yaml \ - --atomic-costs $D/optimizer_cost/rqe_atomic_costs.json \ - --saturation-dir $D --output-dir out/ -``` - -`facts.yaml` gives, per metric, the cardinality of each label set and the -`shape` of each grouping sketches may serve: - -```yaml -metrics: - - metric: http_request_duration_seconds - groups: - - labels: [service, endpoint, pod] # all labels: the series count - cardinality: 30000 - - labels: [service] - cardinality: 5 - shape: {zipf_s: 1.1, distinct_keys: 10000, tail_index: 1.5} -``` - -This path doesn't run `check_cost_table`. - -### How accuracy is read - -For each candidate deployment, at `n` = items one group receives over the -lookback (series per group × scrapes per lookback): - -- **Exact accumulators:** the cost table's value (`relative_error` 0). -- **Sketches:** the curve of the config at the grouping's `data_shape`, the - worse of the bracketing grid points. Between checkpoints it takes the worse - neighbour. Below the first checkpoint there is no accuracy. Past the last, - it uses the plateau if the point saturated by then, else there is no accuracy. -- **Merging `m = S / x` windows:** CMS, CountSketch, HLL and DDSketch merge - exactly, so they read the plain curve. KLL reads the merge curve at `m`, - taking the worse of the measured counts either side. Top-k deployments keep - a heap of `m·k` (#182), so a merged top-k answer reads the plain curve. -- **Unmeasured:** past an unsaturated curve, past the measured `m` or N, or - with no merge curve, the accuracy falls back to the sketch's published - guarantee at 95% (#180), never better than the last measurement it extends. -- **No accuracy** (below the first checkpoint, or a shape outside the grid) - means the candidate is not eligible. - -## 5. Reading the plan - -`small_problem --milp` prints: -- the objective and its totals: CPU in CPU-seconds per second, memory in MB; -- CPU and memory per phase (ingest, merge, query, storage); -- each active deployment: family, config, metric, grouping, window `x` and - slide `y`, and retained instances; -- each RAQE's deployment, merged instances and modeled query latency. - -`asap-optimizer-cli --milp --output-dir` also writes `streaming_config.yaml` -and `inference_config.yaml` for the engine (ASAPQuery#803). - -## What isn't automated yet - -- **PromQL → RAQEs:** expanding queries into RAQEs is manual (Rust in - `small_problem`, YAML in ASAPQuery). -- **`skew_summary.csv` → `WorkloadFacts`/`facts.yaml`:** copying θ, K and α into - `data_shape`, and counting `cardinality` and `value_range`, is manual. No - script reads `skew_summary.csv` into facts. -- **Choosing the study's points:** the 1e9 subset's `points.csv` and the - `--merge-shards-list` values are picked by hand from the workload's N and - `m = S / x`. -- **Spatial filters:** rejected until facts carry per-filter cardinality. -- **The ASAPQuery path:** depends on ASAPQuery#804, which is not merged yet. diff --git a/docs/rqe_optimizer_TODO.md b/docs/rqe_optimizer_TODO.md deleted file mode 100644 index ee62b251..00000000 --- a/docs/rqe_optimizer_TODO.md +++ /dev/null @@ -1,66 +0,0 @@ -# RQE optimizer status - -The v1 design is in `rqe_sketch_deployment_v1.md`. This file tracks what is -implemented, what is next, and what is intentionally deferred. - -## Done in v1 - -- Sliding deployments use `(capability, configuration, metric, spatial_filter, G, x, y)`, - where `x` is window size and `y` is slide. -- Workload facts are per metric: full label set, scrape interval, and - cardinality per label set. The arrival rate is derived from them, and - `validate_facts` reports bad facts up front. -- Eligibility enforces capability, metric, spatial filter, grouping (equal, - or a subset for a family that merges across groups: a roll-up), alignment, and the RQE's - measured accuracy, by each family's own metric, against the RAQE's SLA. -- Candidate generation uses legal windows and subset gcds of query intervals, - keeping only multiples of the scrape interval. A deployment may serve - multiple compatible RQEs. -- Candidate dominance safely removes a deployment only when another at the - same grouping covers all of its RQEs with no worse ingest CPU and memory, - compaction (per window and per slide), per-RQE query-job CPU, query memory - (merge and output), or stored memory. -- The analytical model scores four phases, each with CPU and memory: ingest - (open windows), merge, query, and storage (closed windows, `(S − x)/y + 1` - for the longest lookback), plus per-RQE latency. -- The Pareto vector is `(CPU, memory, {latency_i})`. -- Eager enumeration remains available for small inputs. Streaming enumeration - retains only the incremental Pareto frontier. -- `small_problem --candidates-only` reports generated and retained candidate - counts, per-RQE eligibility, and the Cartesian mapping-space size. -- Streaming mode reports periodic throughput and frontier-size progress, and - can print initial example mappings. -- `scripts/study_saturation.py --phase optimizer-cost` measures the cost - table used by `small_problem` (#174), without changing ASAPQuery's - exporter. -- The MILP uses HiGHS to minimize `Objective::AUCCost { w_cpu, w_mem }`, - `w_cpu × CPU + w_mem × memory GiB` (default `(1, 0)`), without enumerating - mappings. It accepts optional per-RQE latency bounds and returns a normal - mapping. -- `small_problem --milp` takes `--w-cpu`/`--w-mem` and repeatable per-RQE - latency limits, and includes 1-hour, 6-hour, and 1-day quantile RQEs to - exercise those constraints. -- Regression tests confirm the MILP matches exhaustive search on tiny - workloads with shared deployments, at several weightings. - -## Next - -- Add a hard `--max-mappings` limit to streaming experiments. -- Validate accuracy after merging `S / x` sketch instances. V1 currently uses - single-instance measurements, which do not establish final query accuracy. -- Add repeated MILP solves for Pareto exploration across latency budgets or - weights. -- Set `w_cpu`/`w_mem` from real prices. - -## Explicitly deferred - -- Query-result sharing across RQEs. -- Real query concurrency in the snapshot objective, whose memory assumes every - query runs at once (cost by use holds query memory only while it runs). -- Bursty load; CPU is a mean over time. -- Admitting Hydra ([rqe_optimizer_hydra.md](rqe_optimizer_hydra.md)). -- Key labels `K` (PerGroup + PerKey sizes). -- RQE churn, replanning, and migration cost. -- Temporal pre-merging; v1 merges selected base instances at query time. -- A policy for choosing one mapping from the reported Pareto frontier. -- Additional capabilities beyond frequency, quantile, cardinality, and top-k. diff --git a/docs/rqe_optimizer_candidates.md b/docs/rqe_optimizer_candidates.md deleted file mode 100644 index 463f627e..00000000 --- a/docs/rqe_optimizer_candidates.md +++ /dev/null @@ -1,251 +0,0 @@ -# RQE optimizer: candidate generation and mapping - -This document is the canonical design for candidate construction, eligibility -(accuracy included), finer-to-coarser roll-ups and dominance pruning. What a -candidate costs, and the MILP that maps RAQEs to candidates, are in -[Cost models](rqe_optimizer_cost_model.md); Hydra is in -[Hydra](rqe_optimizer_hydra.md). - -## Candidate deployments - -A candidate deployment is: - -```text -D = (capability, configuration, metric, G, x, y) -``` - -Candidates may be generated from individual RAQEs, then deduplicated. This is -only a way to construct the candidate set: when solving a workload, one -selected deployment may serve multiple compatible RAQEs. - -Intuition: candidates are built in two steps, and each drops only candidates -that another candidate beats, so the best plan is never lost. Generation uses -no measurements. It keeps every legal window and configuration, and drops only -slides that a coarser slide beats for the same RAQEs. Pruning then attaches the -measured costs and drops a candidate when another serves all of its RAQEs and -is no worse on every cost. - -Deployments exist only at groupings some RAQE asks for. For each (capability, -metric, spatial filter, grouping `G`), let `R(G)` be the RAQEs a deployment at -`G` may serve: those grouped exactly by `G` for a family that does not merge -across groups, and those grouped by any subset of `G` (roll-ups included) for -a family that does (`mergeable_across_groups`). A coarse RAQE so contributes -its lookback and interval to the fine deployment's windows and slides. Top-k -doesn't roll up, so its heaps come from exact-grouping RAQEs only. Then: - -```text -for each RAQE set R(G), and each window x that divides some S_i in it and is a multiple of the scrape interval: - g = { g_i = gcd(x, T_i) : RAQE i with S_i divisible by x } - slides = { gcd(A) : A is a non-empty subset of g } - for each y in slides that is a multiple of the scrape interval: - for each configuration that serves the capability: - add (capability, configuration, metric, G, x, y) -``` - -`g_i = gcd(x, T_i)` is the largest slide that serves RAQE `i`. - -**Windows.** An RAQE merges `S_i / x` whole windows, so `x` must divide `S_i`. -Windows and slides finer than the scrape interval would only split one -scrape's samples. - -**Slides.** A slide `y` serves RAQE `i` when it divides both `x` and `T_i`, -that is, when it divides `g_i`. A finer slide serves no more RAQEs but holds -more open windows (`x / y`) and more closed ones, so it costs more ingest and -storage at the same latency. Hence: - -- for one RAQE, `y = g_i` is strictly better than every finer slide that serves - it; -- for a set `A` of RAQEs sharing a deployment, `y = gcd(A)`, the largest slide - dividing every `g_i` in `A`, is strictly better than every finer slide that - serves them all. - -Which RAQEs share is the solver's choice, so every subset's gcd is a candidate. -Every other divisor of `x` is strictly worse than one of these. - -Example: `x = 60`, RAQE `a` every 20 s, RAQE `b` every 30 s. - -| Served RAQEs | `y` | Open windows `x / y` | -|---|---|---| -| `a` | gcd(60, 20) = 20 | 3 | -| `b` | gcd(60, 30) = 30 | 2 | -| `a` and `b` | gcd(20, 30) = 10 | 6 | - -The slides are `{10, 20, 30}`. `y = 5` would also serve both, but holds 12 open -windows instead of 6. With a 15 s scrape interval only `y = 30` remains: no -multiple of 15 divides both 20 and 30, so `a` and `b` cannot share at -`x = 60`. - -The subset gcds take one pass, without listing subsets: keep the gcds found so -far, and for each new `g` add `g` and `gcd(found, g)` for every `found`. For -`{20, 30}`: `{20}`, then `{20, 30, 10}`. One pass suffices because -`gcd(A ∪ {g}) = gcd(gcd(A), g)`. - -An RAQE `r_i` is eligible for a candidate `D` when: - -1. `cap_i = D.capability`, `metric_i = D.metric`, the same spatial filter, - and `G_i = D.G`, or `G_i ⊂ D.G` when `D`'s family has - `mergeable_across_groups` (a roll-up; see below). A coarse deployment never - serves a finer RAQE. -2. `D.x % D.y = 0`, `S_i % D.x = 0`, and `T_i % D.y = 0`. -3. The accuracy of `D` for `r_i`, in the accuracy metric of `D`'s family - (`family_properties`, which also gives the direction), clears `tol_i`. - -Exact accumulators take that accuracy from the cost table; they merge without -loss, so the window size doesn't matter. Sketches take it -from the saturation study's error-vs-N curves (`SaturationCurves`, #156), -read at the number of items one answered group receives over the RAQE's -whole lookback: series per `G_i` group times scrapes per lookback. On a -roll-up the answered group is the RAQE's coarse one. - -- The curve is the configuration's at the metric's fitted `data_shape` for - the RAQE's grouping `G_i` (zipf θ and keys `K`, or tail index `a` for - quantiles). Between grid - points, the worst bracketing point; outside the grid, or with no fit, no - accuracy. -- Between checkpoints, the worse neighbour. Below the first checkpoint, no - accuracy. Past the last, the plateau if the point saturated, else none. -- The curve's `error_metric` must be the family's metric; `SaturationCurves::load` - refuses a curve for another metric as a stale study. -- For sketches that merge exactly, the window size doesn't matter: a merged - answer reads the curve at the lookback's item count, like a single sketch. - KLL and univmon-cardinality merge lossily (#131; UnivMon rebuilds its - heavy-hitter heaps from the union of the merged heaps): an answer merged from `n = S_i / D.x` windows, - times the average fan-out `⌈card(D.G) / card(G_i)⌉` on a roll-up, reads - the study's merge curve (`saturation_merge_curve.csv`, the sketch merged - from `n` shards of the same items) at the lookback's item count. Between - measured shard counts, the worse. Past the largest measured count, or past - a merge curve's last N, the curves say nothing, so the answer takes the - guarantee below, no better than the largest count's last measurement; the - study should measure the merge counts a workload needs - (`--merge-shards-list`) so the guarantee isn't what decides (#158); - univmon-cardinality has no guarantee, so past its curves it has no - accuracy. `SaturationCurves::load` refuses a study with no merge curves - for a lossy non-heap candidate sketch (KLL, univmon-cardinality, - univmon-topk). The fan-out is the average; the largest group's is - #189. -- A top-k RAQE asks for its own `k` (`Raqe::topk_k`, default 32). Heap - top-k (CMS-heap, CountSketch-heap) keeps a heap of `n · k` in a deployment - answering from `n = S_i / D.x` merged windows (`n = 1` gives `k`). - Candidates carry one heap per distinct `n · k` among the RAQEs on a window, - and a RAQE is eligible only where the heap holds its `n · k`. Such a merged answer reads as one sketch: the - plain curve, then the guarantee fallback. It assumes the merged heaps still - hold the true top `k`; it is not a guarantee. So heap top-k needs no merge - curves. Costs come from the cost table's rows at heaps 32, 128, 512 and - 2048 (`heap=`), linear in the heap between them and past them. Curves are - measured per `k` (`topk_k=` in the config; 10, 32 and 100 by default) at a - heap of `k`, and larger heaps read the same curve. A RAQE reads the curve - at its `k`, else at the next larger measured `k` (harder); past every - measured `k`, only the guarantee at its `k`. Its query outputs `k` - entries per group. -- Where the study measured nothing for a bracketing point at or above its - first N (past an unsaturated curve, past the measured merge counts or N, - or with no merge curve), the accuracy falls back to the algorithm's - guarantee at 95% confidence (`rqe_optimizer::theory`; two-sided for - rank and relative errors, one-sided for CMS overestimates). It is never - better than the worst last measurement of the curves it extends. The fallbacks are: - - KLL: rank error `2.296 / k^0.9723` (DataSketches' 99% value), scaled - to 95% two-sided; - - DDSketch: relative value error `α` (unbounded store); - - HLL: `1.96 · 1.04 / sqrt(2^lg_k)`; - - CMS-heap top-k, unmerged: the share of the top `k` Zipf(θ, K) keys kept - ranked for every key at once. With Markov per row, independent rows, - and a union bound over the `K − k` other keys, key `i` fails with - probability at most `(cols · (p_i − p_{k+1}))^−rows`. Keys are counted - from the most frequent while the summed failure stays within 5%. It - assumes the heap ranks keys by their final CMS estimates. - - A sketch with no published bound, a merged top-k answer, a point below - the first N, or a shape outside the grid stays unknown. - `accuracy_with_source` reports which points used a bound. - -The cost table's sketch accuracies are not read, but they are one point on -each curve: a row's `measured_at` (items, Zipf θ and population, or Pareto -`a`) is a grid point's key. `SaturationCurves::check_cost_table` reports every -row whose `accuracy_metric` is not its family's or not its curve's -`error_metric`, whose configuration is not in the grid, or whose accuracy -disagrees with the curve at `measured_at` (beyond 3 seed standard errors plus -5%). The study adds the cost table's shape to the grid as a full row and -column (θ = 1.1 at every K, K = 1e4 at every θ), so every sketch row has a -grid point and the grid stays a full cross. `SaturationCurves::load` -refuses curves whose Zipf grid isn't a full cross of its θ and K values and -names the missing points: a hole would leave every shape around it without -accuracy (for example, curves from `--points-from` or a narrowed `--resume`). A row without a grid point (a study run -with `--no-cost-shape`) is reported as unchecked. `small_problem` refuses to plan -when any row disagrees. - -For lower-is-better metrics, passing means `measured <= tol_i`. For -higher-is-better metrics, passing means `measured >= tol_i`. Direction is -explicit on the RAQE; it is never inferred from a metric name. A missing metric -does not pass. - -Before mapping, prune a candidate only if another candidate with the same -capability, metric, spatial filter and grouping can serve every RAQE it can, -and is no worse in ingest CPU and memory, in compaction (CPU-seconds per -window, and CPU and byte-seconds per second), and, for each RAQE it serves, -in query-job CPU (`ℓ`), query memory (merge accumulators and output) and -stored memory. Every cost in both objectives, and every chain (compaction, then -query), is monotone in these, so any mapping using the removed candidate can -substitute the remaining one without raising cost or latency. - -Closed instances must be stored long enough to answer the RAQEs assigned to a -deployment. Retention is not a candidate parameter: a deployment stores the -history needed by the largest assigned query window, costed as the storage -phase ([Cost models](rqe_optimizer_cost_model.md), §3). - -## Workload mapping - -Every RAQE selects exactly one eligible candidate; several RAQEs may select -the same one and so share its ingest. A query may merge several instances -of its candidate, but v1 never combines results from several deployments to -answer one RAQE. The MILP over the eligible pairs, its objective and its -latency bound are in [Cost models](rqe_optimizer_cost_model.md) (§7 for cost -by use; the snapshot objective changes only its coefficients). The solver -never enumerates full mappings. - -## Fine-to-coarse group roll-ups - -A deployment grouped by `G_d` can answer a RAQE grouped by `G_r ⊂ G_d` when -its family merges across groups (`mergeable_across_groups`): at query time, -the fine instances of each coarse group are merged into one, then answered. -This is a **roll-up**. Families that do: exact sum/min/max, HLL, -univmon-cardinality, KLL and DDSketch. These don't, and serve only -`G_r = G_d`: - -- **Rate/increase** (`exact-increase`). A coarse increase is the sum of its - series' increases, `Σ_s increase_s`, so it is not the increase of the - summed counter: a reset in one series is hidden by the others' growth. - The accumulator tracks one counter, and its merge joins two pieces of it - in time (a drop at the seam is a reset), so merging two groups' counters - is wrong: A going 100 → 110 and B going 5 → 8 over the same minute merge - to 10, not 13. A roll-up would need per-series state and a sum of values, - not this merge. (ASAPQuery keys `MultipleIncrease` per series: it plans - only bare `rate`/`increase`, which keep every label.) -- **Top-k** (CMS-heap, CountSketch-heap, univmon-topk). A coarse group's top - `k` can hold keys that are in none of its fine groups' heaps. -- **Hydra** (hydra-hll, hydra-univmon-cardinality, hydra-kll; planned only - with undeployable families allowed). It inserts every label subset of its - schema `Λ`, so any non-empty `G_r ⊆ Λ` is answered directly from the grid, - never by merging fine groups. A metric with a Hydra dataset - (`MetricFacts::hydra_dataset`) gets one grid per (capability, filter, - window, slide, config), over its full schema only (every schema label, - not the per-series ones), because that is the width the study measures - cost and accuracy at; its cost rows must name that dataset - (`measured_at.dataset`). Its accuracy is held to the worst case: the max - error over the covered groups, worst over the measured N and seeds - (`hydra_saturation.csv`), while per-group sketches read seed-mean curves. - The design is in [Hydra](rqe_optimizer_hydra.md). - -- **Cost**: ingest, compaction and storage stay at `G_d`; the query job merges - `I_d · n − I_r` times and answers `card(G_r)` groups - ([Cost models](rqe_optimizer_cost_model.md), §3). A direct roll-up - (`n = 1`) still merges `card(G_d) − card(G_r)` instances. -- **Accuracy**: read at `G_r`: its `data_shape`, and its items per group over - the lookback. KLL's and univmon-cardinality's merge curves are read at - `⌈card(G_d)/card(G_r)⌉ · n` merged instances, the average fan-out, an estimate rather than a - worst-group bound (uneven fan-out is #189). -- **Facts**: `validate_facts` requires `card(X) ≤ card(Y)` for every pair - `X ⊂ Y` among the label sets a plan can use (every RAQE grouping on the - metric, and its full label set), so a merge count is never negative. - Label sets no RAQE groups by are not checked. -- **Plan output**: a RAQE rolls up exactly when its grouping differs from its - deployment's. diff --git a/docs/rqe_optimizer_cost_model.md b/docs/rqe_optimizer_cost_model.md deleted file mode 100644 index da7916c4..00000000 --- a/docs/rqe_optimizer_cost_model.md +++ /dev/null @@ -1,439 +0,0 @@ -# RQE optimizer: cost models - -This document is the one home for what a candidate costs (§3's table), the -canonical cost-by-use model with its MILP, and the still-supported -snapshot-AUC objective. Candidates and eligibility are in -[Candidate generation and eligibility](rqe_optimizer_candidates.md); Hydra -is in [Hydra](rqe_optimizer_hydra.md). - -## Model status and relationship - -- **Cost by use and batch latency** is the canonical model for the current - ASAP-versus-AutoSketch evaluation. It accounts for ingest workers, - window-close compaction, transient memory over its holding time, elastic CPU, - and a batch-latency bound. -- **Resource primitives and the snapshot-AUC objective** remain supported for - the original generic optimizer. They define the measured per-operation and - per-instance inputs used everywhere, then use the older snapshot accounting: - query memory is summed as if concurrent, there is no worker fan-out or - compaction, and latency is serial query work. It is not the evaluation's - billing model. -- **Hydra** is a Shared + Fixed sketch (below), a candidate only with - undeployable families allowed, built over a metric's full schema - ([Candidates](rqe_optimizer_candidates.md), roll-ups section). - -The two implemented objectives share the same resource primitives and §3's -table; they differ in how they account for them over time, and in latency -(a batch's longest chain vs. each RAQE's serial query time). - -## Cost by use and batch latency - -Status: agreed design (ProjectASAP/ASAPQuery#777), implemented in -`rqe-optimizer/src/usage.rs` (resource use, cost, latency) and -`milp::minimize_usage_cost`. The MILP minimizes the cost of a plan billed by -use, in two versions: - -- **Version 1, no latency constraint:** the cheapest plan; its latency is - reported. Sweeping an optional latency bound traces each method's - cost–latency Pareto frontier. -- **Version 2, a batch latency SLA:** the cheapest plan whose batch latency, - from the job placement (§6), is at most the SLA `L`, for a grid of SLAs. -Evaluated first on the synthetic mixed template set; methods: ASAP (this -MILP, with sharing), PerQuery (the same MILP without sharing) and -AutoSketch-Adapted (fixed configs chosen by memory). - -#### 1. Definitions - -| Symbol | Meaning | -|---|---| -| `D`, `i` | a candidate deployment; a RAQE | -| `x_D`, `y_D` | `D`'s window and slide; a window closes every `y_D` | -| `a_D = x_D / y_D` | open windows; each sample is inserted into `a_D` instances | -| `S_i`, `T_i` | RAQE `i`'s lookback and interval; it fires at `t = k · T_i` | -| `G_d`, `G_r` | `D`'s grouping; RAQE `i`'s grouping, equal to `G_d` or, for a family that merges across groups, a subset of it (a **roll-up**) | -| `card(G)` | groups of grouping `G` | -| part | the sketch, plus a key tracker if the family needs one; every cost below is summed over parts | -| `I_d` | a part's instances per window: `card(G_d)`, or 1 for a sketch shared by all groups | -| `I_r` | the states a query of `i` ends with: `card(G_r)`, or 1 for a sketch shared by all groups | -| `λ_D` | samples/s arriving for `D`'s metric: `card(series) / scrape interval`, already a rate | -| `c_ins`, `c_mrg`, `c_qry` | measured CPU-seconds per insert, per pairwise merge, per query of one instance | -| `m` | measured bytes per instance; `w_D = Σ_parts I_d · m`, one window of all instances | -| `n_{i,D} = S_i / x_D` | windows a query merges | -| `k_D = ⌈ρ_D⌉` | ingest workers (at least one), from ingest CPU `ρ_D` (§3) | -| batch | all query jobs issued at one instant | - -**Measurement assumption (sketch-bench as is).** Every sketch operation is -single-threaded and compute-bound: sketch-bench's CPU time equals its wall -time (e.g. an insert phase of 62.65 ms CPU and 62.65 ms wall). So a job uses -at most one core and, on a full core, takes its CPU time. The cost table's -`*_cpu_secs` are all the model needs. - -#### 2. Cost model - -`cost = w1 · AUC(CPU) + w2 · AUC(memory)`: the mean vCPUs and GiB over time, -priced per vCPU and per GiB (CPU only: `(1, 0)`; Fargate's prices). CPU is -elastic: a job gets a core whenever it is ready. - -This is billing by use, as on fine-grained autoscaling platforms (Cloud Run -with request-based billing, Dataflow streaming, Flink on Kubernetes with an -autoscaler), idealized: capacity follows the load at once. Billing for -provisioned capacity (VMs, or containers billed by allocation such as -Fargate) is not modeled. Since a job holds its memory only while it runs, its -memory × time is the same whenever it runs, and queueing a job would save -nothing. - -#### 3. Cost of each phase: CPU and memory - -This is the one cost table: every phase, with its CPU and its memory. Each -cell is per part; a phase's cost is the sum over parts. - -| Phase | When | CPU-seconds per event | Mean vCPUs | Memory (bytes) | Held | -|---|---|---|---|---|---| -| Ingest (the precompute) | continuously, as samples arrive | — | `ρ_D = λ_D · a_D · c_ins` | `ingest_D = I_d · m · a_D · k_D` (open windows, one copy per worker) | always | -| Compaction | at each window close | `c_D = (k_D − 1) · I_d · c_mrg` | `c_D / y_D` | `k_D · I_d · m` (the closed window's partial copies; 0 when `k_D = 1`) | while it runs: `c_D` | -| Query job of RAQE `i` (merge, then estimate) | at each firing | `ℓ_{i,D} = card(G_r) · c_qry + (I_d · n_{i,D} − I_r) · c_mrg` | `ℓ_{i,D} / T_i` | `q_{i,D} = I_r · m(S_i) · [merges > 0] + card(G_r) · output bytes` | while it runs: `ℓ_{i,D}` | -| Storage | always | — | 0 | `stored_D = max_i I_d · m · ((S_i − x_D) / y_D + 1)` (closed windows, one compacted copy) | always | - -- **No roll-up** (`G_r = G_d`, so `I_r = I_d`): the query merges - `I_d · (n − 1)` times, and a direct query (`n = 1`) merges nothing and - holds no accumulator. -- **Roll-up** (`G_r ⊂ G_d`): a query reads `I_d · n` states and ends with - `I_r`, and each merge folds two into one, so it merges `I_d · n − I_r` - times, even when direct. Ingest, compaction and storage stay at `G_d`; - the query's accumulators, answers and output are at `G_r`. -- `m(S_i)` is an accumulator holding the lookback of one `G_r` group (it - differs from `m` only for DDSketch on a metric with a value range, which is - sized from the values it holds). -- Ingest runs on `k_D` parallel workers (each uses at most one core), - **split by sample**: each worker reads a share of the input (e.g. some - Kafka partitions or scrape targets) with no shuffle by group, so it keeps - its own open window instance of every group it sees. -- When a window closes (the watermark passes its end), a **compaction** job - merges the `k_D` partial instances of each group into one stored instance. - With `k_D = 1` there is none. The merged copy takes the storage slot that - the oldest window frees at the same moment, so it is not counted twice. -- Each firing of RAQE `i` issues one **query job**: merge the `n_{i,D}` - stored instances of each group for the query window, then estimate. - Merging is part of the query. It reads the newest window, which closes at - the firing time, so it starts after that window's compaction. It reads - stored instances in place. -- Closed windows wholly inside a lookback start in `[t − S, t − x]`, hence - `(S − x) / y + 1` of them; storage takes the longest lookback `D` serves. -- Query output is estimated at 8 bytes per group, or `k` × 16 bytes per group - for top-k (`k` the deployment's answered top-k, 32 by default, with 64-bit - key hashes); it is not measured. A top-k sketch's `c_qry` is measured at - `k = 32` and scaled by `k / 32`. -- `k_D = ⌈ρ_D⌉` takes the deployment's whole ingest CPU, summed over its - parts (sketch and key tracker). - -In words: - -- `ρ_D`: every second, `λ_D` samples arrive (the scrape interval is already - inside `λ_D`), and each is inserted into the `a_D` windows still open, at - `c_ins` CPU-seconds each: CPU-seconds per second. -- `c_D`: closing a window merges, for every instance, the `k_D` workers' - partial copies into one, which takes `k_D − 1` merges. This happens once - per slide. -- `ℓ_{i,D}`: a query merges the stored windows it reads down to one state per - answered group, then answers once per `G_r` group. This happens once per - interval. (A sketch shared by all groups merges one instance but still - answers every group.) -- `ingest_D`: every worker keeps one instance per group for each of the `a_D` - windows still open, and there are `k_D` workers. -- `stored_D`: closed windows are kept until the longest lookback that `D` - serves no longer needs them. -- `q_{i,D}`: a merge builds one accumulator per answered group, plus the - answer for every group. - -#### 4. AUC(CPU) and AUC(memory) - -Every "mean vCPUs" is a long-run average rate, CPU-seconds per second, over -the same period: a whole number of hyperperiods `H = lcm(T_i, y_D)`, over -which every job pattern repeats exactly. A part with `w` CPU-seconds per -event every `P` seconds runs `H / P` times per hyperperiod, so its mean is -`(H / P) · w / H = w / P`; ingest is already a rate. So the columns add. - -```text -AUC(CPU) = Σ_D ρ_D + Σ_D c_D / y_D + Σ_i ℓ_{i,D} / T_i -AUC(memory) = Σ_D (ingest_D + stored_D + k_D · w_D · c_D / y_D) - + Σ_i q_{i,D} · ℓ_{i,D} / T_i -``` - -CPU is elastic, so every job runs on its own core: a compaction runs for -`c_D` and a query for `ℓ_{i,D}`. Both AUCs are fixed by the plan, whatever -the schedule. - -In words: `AUC(CPU)` adds the three rates, work per second in vCPUs; ingest -is already a rate, and only compaction (per window close, every `y_D`) and -queries (per firing, every `T_i`) are divided by their period. -`AUC(memory)` holds ingest and storage all the time, and a compaction's and -a query's memory only while they run, a fraction `run time / y_D` or -`run time / T_i` of the time. - -#### 5. Batch latency and the SLA - -A batch's **latency** is the time from its issue until its last query job -finishes, compaction included. A plan's **query latency** is its worst batch -latency, given by the job placement (§6). Version 1 reports it; version 2 -requires it to be at most the SLA `L`. Per-RAQE latency bounds -(`Raqe::latency_sla_ms`) are not used. - -CPU is elastic, so no job waits for a core, and a batch's latency is its -longest **chain**: the newest window's compaction, then the query, each on a -full core, `c_D + ℓ_{i,D}` (`analytical_cost_model::chain_ms`). A plan's query -latency is the longest chain over its RAQEs. - -In words: the trade-off between cost and latency comes from the plan. A cheap -plan keeps short windows and merges many of them at query time (long -chains); a faster one precomputes more at ingest (more open windows, more CPU -and memory). With no bound the MILP picks the cheapest plan, whatever its -latency; a bound `L` asks for the cheapest plan at least that fast. - -#### 6. Job placement algorithm - -The job placement decides when each job runs on the available CPU, and so -gives every batch's latency. It is defined for any CPU capacity; billing by -use makes CPU elastic, so it runs with unlimited capacity. - -```text -capacity for jobs K = C − Σ_D ρ_D (C = ∞ when CPU is elastic) -event-driven, over the hyperperiod H = lcm(T_i, y_D), twice -(events: query issues, window closes, completions): - ready = compaction jobs (triggered at window closes) - + query jobs whose newest window is compacted - order: issue / trigger time (older first), then compaction before query - (it unblocks queries), then longest remaining work first - give rates in that order: job j gets a_j = min(1, K − rates already given) - a job at rate a_j finishes when its remaining work / a_j elapses - recompute the rates at every event -outputs: per-RAQE and per-batch latency, worst batch latency -``` - -In words: - -1. **Reserve ingest.** Ingest runs all the time and takes `Σ ρ_D` vCPUs; the - rest, `K`, is shared by compaction and query jobs. -2. **Issue jobs.** Each window close triggers a compaction job for its - deployment. Each firing issues a query job, which becomes ready only once - its deployment's newest window is compacted. -3. **Prioritize.** Ready jobs run oldest batch first, so no batch starves. - Within a batch, compaction runs before queries, because queries wait for - it. Then the longest remaining work runs first, so a long job doesn't end - up running alone at the end of the batch. -4. **Share capacity as rates.** In that order, each job gets up to one core - (a single-threaded job can't use more) from the capacity still free. The - last one served may get only a fraction and runs slower in proportion. -5. **Advance to the next event.** Time jumps to the next query issue, window - close or completion; remaining work drops by rate × elapsed time, and the - rates are recomputed. -6. **Read off the results.** A query's latency is its finish minus its - issue, and a batch's latency is that of its last query. - -With elastic CPU (`K = ∞`) every job starts as soon as it is ready on its own -core, so the placement has a closed form: a compaction runs for `c_D`, a -query for `ℓ_{i,D}`, and a batch's latency is its longest chain -`c_D + ℓ_{i,D}`. The code uses this closed form (`usage::usage_cost`), and -it is what makes both MILP versions exact. - -#### 7. MILP formulation - -Variables: `z_{i,D} ∈ {0,1}` (RAQE `i` uses `D`, for eligible pairs `E`), -`u_D ∈ {0,1}` (`D` is active), `stored_D ≥ 0`. Constants per candidate and -pair are §3 and §4's. - -```text -minimize w1 · [ Σ_D (ρ_D + c_D / y_D) · u_D + Σ_(i,D) (ℓ_{i,D} / T_i) · z_{i,D} ] - + w2 · [ Σ_D ((ingest_D + k_D · w_D · c_D / y_D) · u_D + stored_D) - + Σ_(i,D) (q_{i,D} · ℓ_{i,D} / T_i) · z_{i,D} ] -s.t. Σ_D z_{i,D} = 1 for every RAQE i - z_{i,D} ≤ u_D ≤ Σ_i z_{i,D} for (i, D) ∈ E - stored_D ≥ w_D · ((S_i − x_D) / y_D + 1) · z_{i,D} -version 2, a batch latency SLA L (or version 1's frontier bound L): - z_{i,D} = 0 if c_D + ℓ_{i,D} > L -``` - -In words: - -- The objective is the price of the mean vCPUs (ingest and compaction per - active deployment, plus each RAQE's query work per second), plus the price - of the mean memory (ingest and storage per active deployment, each - compaction's partial copies for the fraction of time it runs, and each - query's memory for the fraction of time it runs). -- Every RAQE is served by exactly one eligible deployment. -- A deployment is active if and only if some RAQE uses it, so its ingest is - paid once, however many RAQEs share it. -- A deployment stores enough closed windows for the longest lookback it - serves (a linearized max). -- Version 2 (and version 1's frontier bound): a pair whose chain (the newest - window's compaction, then the query, each on a full core) is longer than - `L` is ruled out. By the placement's closed form, the plan's batch latency - is then at most `L`, exactly: the SLA is met. Chains are - compared with `L` with a relative slack of `1e-9`, so float error can't rule - out a chain equal to `L`. - -#### 8. Solution method - -Every term is linear in `u` and `z`, with or without a bound, so the MILP is -solved exactly -(`milp::minimize_usage_cost`; costs are divided by the sum, over RAQEs, of -each RAQE's cheapest pair on its own, so the solver sees magnitudes near 1). The solved plan's cost and latency are -then computed from its resource use (`usage::usage_cost`). - -- **PerQuery** is the same MILP with sharing ruled out. `minimize_usage_cost` - takes `allowed`, the candidate indices each RAQE may use. PerQuery gives - each RAQE its own candidates, with a separate copy of any deployment that - two RAQEs could both use. Two RAQEs share a deployment only by choosing the - same candidate index (`u_D` is per index), so their copies stay apart and - each pays its own ingest. -- **AutoSketch** keeps its memory-chosen configs and is priced by the same - function: its cost by use and its latency, the longest chain. It ignores - latency, so it is one point, not a frontier. -- **Version 1:** ASAP and PerQuery are solved with no bound (the cheapest - plan), and for a sweep of bounds `L` from the smallest feasible one (the - largest, over RAQEs, of each RAQE's fastest chain) up to the unbounded - plan's latency. Each solve gives a (latency, cost) point; together they are - the method's frontier. `L` = AutoSketch's latency gives each method's cost - at no more than AutoSketch's latency. -- **Version 2:** ASAP and PerQuery are solved at each SLA of a fixed grid; - an SLA below the smallest feasible bound has no plan. AutoSketch ignores - the SLA; its latency either meets it or not. - -#### 9. Outputs - -Per (method, weight setting, bound or SLA): cost; mean CPU and memory, by -part; query latency and per-RAQE latency; planning time. Figures: version 1, -each method's cost–latency frontier for each workload (ASAP and PerQuery as -lines, AutoSketch as a point); version 2, each method's cost at each SLA. - -## Resource primitives and the snapshot-AUC objective - -The measured per-operation primitives (`m`, `c_ins`, `c_mrg`, `c_qry`, from -the cost table) and how they scale with a deployment (instance shape and size -law, below) are shared by both objectives; the one cost table is §3's. - -`m` is measured per instance, except for DDSketch on a metric with a -`value_range`: `m = (floor(ln(hi / lo) / ln((1 + alpha) / (1 - alpha))) + 1) × 8` -bytes, one bucket count per `gamma`-power in the range, capped by the values -one instance holds (`λ · x / card(G_d)` for a window, `λ · S / card(G_r)` for -a query's merge accumulator), as sketch-bench's `dd_footprint` counts them. -The range is the metric's, so `m` is an upper bound for a group whose own -values span less. - -**Snapshot-AUC objective** (`milp::minimize`, `analytical_cost_model::score`; -still supported). It prices §3's table with older accounting: - -- one ingest worker (`k_D = 1`), so no compaction; -- each RAQE's merge and query memory (`q_{i,D}`) held all the time, as if - every query ran at once, instead of for `ℓ_{i,D} / T_i` of the time; -- a RAQE's latency is its query job's serial CPU time `ℓ_{i,D}`, with no - compaction before it, checked against the RAQE's own - `latency_sla_ms` (no batch latency). - -Its CPU (`ρ_D`, `ℓ_{i,D} / T_i`) and its ingest and storage memory are §3's -with `k_D = 1`, roll-ups included. - -### Instance shape and size law - -Each row of the cost table (from -[`scripts/study_saturation.py --phase optimizer-cost`](../scripts/study_saturation.py)) -is measured on one instance, at whatever key count the benchmark fed it -(`measured_keys`). A key is one distinct entry an instance stores exactly, -such as one group's running sum in an exact accumulator. Turning a row into a -deployment's cost needs two properties of the family: - -- **Shape**: instances per window. `PerGroup` keeps one per group, so - `card(G_d)`; `Shared` keeps one for all groups. -- **Law**: how one instance's size grows. `Fixed` is set by the configuration - (e.g. CMS rows × columns); `PerKey` stores one entry per key, so it grows - linearly with keys. - -Memory per window is instances × instance size: - -| | Fixed | PerKey | -|---|---|---| -| **PerGroup** | `card(G_d) × m`: CMS, CountSketch, KLL, DDSketch, HLL, UnivMon, top-k (heap `m · k`) | `card(G_d) × m × keys_per_group / measured_keys`: none yet (e.g. an exact top-k map) | -| **Shared** | `m`: HydraKLL and other Hydra sketches | `m × card(G_d) / measured_keys`: exact sum, min, max, increase (one value per group, so keys = groups) | - -The same factor scales merge and query CPU. Insert CPU is per sample and does -not scale. - -Quantile sketches have no keys. KLL's `m` is Fixed, and the same at every -item count, because `asap_sketchlib::KLL` allocates once at construction. -Since #192 the cost table's KLL rows report that allocation: item slots, -level index and merge buffer, 5536 B at `k = 50`. Cost tables measured -before it, including the committed `autosketch-vs-asap-inputs`, carry a -nominal `4 · k · sizeof(T)` instead (1600 B at `k = 50`; the allocation is -1.58× that at `k = 200`, 1.14× at `k = 800`), so they understate KLL -memory (#191). -DDSketch is sized from the metric's `value_range` when given (capped by -values per instance), else from the measured size. - -The model uses `I_d × m`, where `I_d` is instances per window: - -- PerGroup + Fixed: `I_d = card(G_d)`. -- Shared + PerKey reduces to `I_d = card(G_d)` because the export divides the - exact accumulators' memory and merge cost by `measured_keys` - (`groups_per_instance`), making `m` a per-group cost. Their query cost is - already per group. -- Shared + Fixed: `I_d = 1`. In code this is the family property - `one_fixed_size_sketch_for_all_groups`, true only for the Hydra families. It applies - to memory and merge; query stays `card(G_r) × c_qry`, one probe per answered group. - A fixed-size sketch degrades as groups grow, so Hydra is built only at the - full schema its rows and accuracy were measured at (`measured_at.dataset`, - `hydra_saturation.csv`), and its accuracy is the worst case: max over the - covered groups, worst over N and seeds (per-group sketches read seed-mean - curves). - -PerGroup + PerKey is deferred until a family needs it: it needs the key labels -`K`, the metric's labels minus `G`. So for every family in use, nothing scales -with keys below the group: a per-service top-k CMS costs the same however -many endpoints it counts. - -Side by side, what each family puts into §3's table (every phase then -follows from it): - -| | KLL (per group) | exact-sum (per-group normalized) | -|---|---|---| -| `m`, `c_mrg` in the row | one group's | whole ÷ groups | -| `I_d` (instances per window) | `card(G_d)` | `card(G_d)` | -| `I_r` (states a query ends with) | `card(G_r)` | `card(G_r)` | -| probes per query (`c_qry`) | `card(G_r)` | `card(G_r)` | -| key tracker part | none | none | -| rolls up (`G_r ⊂ G_d`) | yes | yes | - -A Hydra grid is the Shared + Fixed case, `I_d = I_r = 1`; its row, probes -and key tracker are in [Hydra](rqe_optimizer_hydra.md) §3. - -The Hydra families are in their capabilities' family lists but not in -`DEPLOYABLE_FAMILIES`, so they are candidates only when undeployable families -are allowed (the §6.3 eval runner); AutoSketch skips them (#159). - -### Family properties and key tracker - -Each family has properties, hardcoded by variant in `family_properties` -(an unknown variant panics rather than getting a default): - -- `mergeable_across_windows`: a family without it only serves `S_i = x`. - Every family in use merges. -- `mergeable_across_groups`: it can serve a RAQE at a subset grouping (a - roll-up, [candidates](rqe_optimizer_candidates.md#fine-to-coarse-group-roll-ups)). - True for exact sum/min/max, HLL, univmon-cardinality, KLL and DDSketch. -- `one_fixed_size_sketch_for_all_groups`: the Shared + Fixed cell above. -- `needs_delta_set_key_tracker`: the sketch can't list its groups, so a - DeltaSet (`exact-delta-set`) records each window's keys for the query to - probe. True for HydraKLL. A candidate needing one carries the DeltaSet row - and is dropped if the cost table has none; a deployment without its tracker - is never eligible. The cost table may hold at most one DeltaSet row. - -The tracker is priced as an exact accumulator on the same `x`, `y` and `G` -(Hydra's tracker grouping is open: [Hydra](rqe_optimizer_hydra.md) §6.1), -in every phase: ingest, merge (union of the `n_i` windows' key sets), query -(enumerate keys) and storage. Its `m` is per key, so it adds `card(G_d) × m_ds` -per window. The AutoSketch baseline skips shared fixed-size families for now -(sketch-bench#159). - -A query job's serial CPU time, `ℓ_{i,D}` (§3), summed over the sketch and -its key tracker, is a RAQE's latency in the snapshot-AUC objective and the -query half of its chain in cost by use. On its own it is not a wall-clock -SLA: it assumes no parallel execution across groups and no cheaper k-way -merge. diff --git a/docs/rqe_optimizer_hydra.md b/docs/rqe_optimizer_hydra.md deleted file mode 100644 index 9a6cc227..00000000 --- a/docs/rqe_optimizer_hydra.md +++ /dev/null @@ -1,283 +0,0 @@ -# RQE optimizer: admitting Hydra - -Status: hydra-hll, hydra-univmon-cardinality and hydra-kll are candidates -when undeployable families are allowed (the §6.3 eval runner), built over a -metric's full schema with accuracy from `hydra_saturation.csv` -([Candidates](rqe_optimizer_candidates.md), roll-ups section). They are not -in `DEPLOYABLE_FAMILIES`, so ASAPQuery's planner never sees them. This -document says, for each Hydra combination sketch-bench benchmarks, what -accuracy guarantee it has, how it is costed, how it would enter the -candidates and the MILP, and what sketch-bench must measure first. Symbols -follow [Cost models](rqe_optimizer_cost_model.md) §1; `Λ` is a Hydra -schema (the hyperperiod there is `H`). - -## 1. What Hydra is in sketch-bench - -All five combinations wrap `asap_sketchlib::Hydra` (0.3.0; Manousis et al., -VLDB 2022, arXiv:2208.04927). The `polars` rows of the same names are exact -baselines. - -- **Grid.** `R` rows × `W` columns of cells, each an inner sketch, over a - schema `Λ` of label columns (`hydra_shared.rs`). -- **Insert fans out.** A record with `|Λ| = d` labels is inserted into the - cell of every non-empty label subset (`2^d − 1` subkeys, each tagged with - its column names), in every row: `R · (2^d − 1)` inner inserts per record - (`hydra.rs` `update`, masks `1..2^d`). The empty grouping (the whole - stream) is not inserted. -- **Query.** A subpopulation (values for a subset `G_r ⊆ Λ`, the rest - unconstrained) hashes to one cell per row; the answer is the **median** - over the `R` rows of each cell's inner answer (`query_key`). The library - answers any non-empty subset of `Λ`; sketch-bench's wrapper constrains - only leading columns (`hydra_shared.rs` `query`), so it can ask only - prefix groupings. -- **No keys.** Hydra stores no subpopulation keys. To answer every group of - `G_r`, something else must list them (in the optimizer, a DeltaSet key - tracker). -- **Merge** is cell by cell, needing the same dimensions, cell type and - schema; every row's column comes from one seeded hash (`HYDRA_SEED`), so - two grids built apart line up. - -| Variant | Inner cell (canonical) | Counter bytes | -|---|---|---| -| `hydra-cms` | Count-Min, cell 3 × 512 i32; grid 3 × 128 | `R·W·cell_rows·cell_cols·4` | -| `hydra-cs` | Count Sketch, same shape | same | -| `hydra-hll` | HLL, `p = 14` fixed | `R·W·2^14` | -| `hydra-kll` | KLL, `cell_k = 200` | `R·W·(slots(k)·8 + 62·8)` (no merge buffer; #191) | -| `hydra-univmon` | UnivMon: heap 1000, 5 × 2048, 8 layers; grid 2 × 8 | `R·W·layers·((sr·sc + sr)·8 + heap·entry)` | - -`hydra-univmon` is registered as four statistics: cardinality, L1, L2 and -entropy. Each footprint adds `(R·W + 1)` cell headers. - -## 2. Accuracy guarantee per combination - -### 2.1 The source of error - -For a subpopulation `q` (one group of `G_r`), in row `j` its cell also holds -every other subkey hashed there, the colliders `C_j`. Let: - -- `N_q`: items of `q`; `N_C`: items of the colliders in one row's cell; -- `M`: items inserted into one row, after fan-out: `N · (2^d − 1)` for `N` - records. A coarser label subset is inserted too, so every label added to - `Λ` roughly doubles `M`; -- `β`: a Markov factor. Modeling the column hash as random, - `E[N_C] = Σ_{s≠q} N_s / W ≤ M / W`, so `N_C ≤ β·M/W` in a row with - probability at least `1 − 1/β`; -- `δ_in`: the inner sketch's failure probability. - -The answer is the median over rows (the mean when `R = 2`). If more than -half the rows give answers inside one common interval, so does the median, -so a bound below holds when more than half the rows are **good**: their -contamination is within `β·M/W` and their inner sketch is within its own -bound. - -**Hydra's Theorem 2** (§4.5, checked against the paper): if each cell -estimates a **monotone G-sum** within `(1 ± ε_US)` with probability -`1 − δ_US`, then with `W = c(1 + ε_US)/ε` columns for a `c` with -`1 − δ_US − 1/c > 1/2`, and `R = O(log 1/δ)` rows, with probability -`1 − δ`, `G_q(1 − ε_US) ≤ Ĝ_q ≤ G_q(1 + ε_US) + ε·G_S`, where `G_S` is the -G-sum of the whole (fanned-out) stream. The proof takes the rows as -i.i.d. Three things about this library weaken it: - -- **Inner errors are correlated across rows.** Every cell is a clone of one - template, so the inner sketches hash with the same seeds (CMS and Count - Sketch matrix seed, HLL's seed, KLL's RNG state): `q`'s own items collide - the same way in every row. Only the contamination is independent across - rows. A sound confidence is - `P[Binomial(R, 1 − 1/β) > R/2] − R·δ_in`. -- **One fixed hash.** Columns come from one seeded hash (`HYDRA_SEED`), not - a random family, so the probabilities are a random-oracle heuristic, and - a group that collides badly collides in every window, merge and - deployment. For `W` not a power of two the column is not uniform (at - `W = 100` some columns get 1.56/W of the keys), so use the worst column's - share in place of `1/W`. -- **Few rows.** It is asymptotic in `R`. The canonical `hydra-cms`, - `hydra-cs`, `hydra-hll` and `hydra-kll` grids have `R = 3`: with - `β = 4` and `δ_in = 0.05` the bounds hold with probability about - `0.84 − 0.15 = 0.69`. The canonical `hydra-univmon` grid has `R = 2`, - `W = 8`: both rows must be good, and `β·M/W = 1.5·N`, so even its form is - vacuous. - -The error is **additive in the global mass** (`ε·G_S`, here `β·M/W`), not -relative to `q`. So a bound is useful only for groups large against -`β·M/W`. For the canonical `W = 128`, `d = 2` (`M = 3N`) and `β = 4`, -`β·M/W ≈ 0.094·N`: a group holding 1% of the records gets a contamination -term about 9× its own size. - -### 2.2 Bounds - -Derived from the setup above; only Theorem 2 is the paper's. `F_v` is the -fanned-out frequency of value `v` over all subkeys, `ΣD` the sum over all -subkeys of their distinct counts, and `D_q` the distinct count of `q`. - -| Combination | Statistic (sketch-bench metric) | Bound in a good row, so for the median | Closed form usable for planning? | -|---|---|---|---| -| `hydra-cms` | frequency of `v` in `q` (top-k ARE/AAE) | `f_q(v) ≤ f̂ ≤ f_q(v) + ε_c·N_q + β·(F_v + ε_c·M)/W`, `ε_c = e/cell_cols`, `δ_in = e^−cell_rows`. One-sided: never under. | Yes, for heavy (`q`, `v`) pairs; vacuous for small `f_q(v)`. | -| `hydra-cs` | same | `f̂ − f_q(v) ∈ [−ε_s·(L2_q + X), ε_s·L2_q + X']` with `ε_s ≈ 1/√cell_cols`, where the colliders' terms `X = L2_C` and `X' = f_C(v) + ε_s·L2_C` are bounded together by one Markov event (`E[F2_C] ≤ F2/W`, `E[f_C(v)] ≤ F_v/W`). Two-sided, biased upward by `f_C(v) ≥ 0`. | Yes, same caveat; not unbiased for `q`. | -| `hydra-hll` | distinct count of `q` (relative error) | `(1 − ε_h)·D_q ≤ D̂ ≤ (1 + ε_h)·(D_q + β·ΣD/W)`, `ε_h ≈ z·1.04/√2^14` (a normal approximation, not a hard bound). Cardinality is a monotone G-sum, so Theorem 2 applies. | Yes. The median removes contamination, not HLL noise (§2.1). | -| `hydra-univmon` cardinality, L1, L2 | `Σ 1[f>0]`, `Σ f`, `√Σ f²` | Theorem 2 directly (L2 via `F2 = Σ f²`: `√` of the bound). | Form only: `ε_US` for concrete heap, layers and widths is not known in closed form (UnivMon's Theorem 1 is asymptotic). Empirical. | -| `hydra-univmon` entropy | `log L1 − (Σ f log f)/L1` | None: a nonlinear combination of two G-sums, and contamination moves both. | No. Empirical only. | -| `hydra-kll` | rank error of `q`'s quantiles (`mean_rank_err`) | `|rank_q(x̂)/N_q − φ| ≤ ε_k + (1 + ε_k)·N_C/N_q`, `N_C ≤ β·M/W`: the cell answers the quantile of a mixture. | No: vacuous unless `N_q ≫ β·M/W`, and the real error depends on how the colliders' values overlap `q`'s. The paper states quantiles cannot be directly estimated by Hydra. Empirical only. | - -The KLL bound: the cell's KLL returns `x̂` with -`|rank_cell(x̂) − φ·N_cell| ≤ ε_k·N_cell`, `N_cell = N_q + N_C`, and -`rank_q(x̂) = rank_cell(x̂) − rank_C(x̂)` with `0 ≤ rank_C ≤ N_C`. - -So **no Hydra combination has a closed-form guarantee the planner can use -for every group**: CMS, CountSketch and HLL have bounds that are vacuous for -small groups; UnivMon and KLL have none. Admission must be empirical (§2.4), -and these bounds serve as sanity checks on the measurements. - -### 2.3 Merges and coarse groupings - -- **Across windows and ingest workers.** CMS and CountSketch add counters, - and HLL takes register maxima; with the shared seeds, a merged grid equals - one built from all the records, so merged accuracy is the single-grid - accuracy at the merged mass. KLL cells merge lossily (as `kll-percall`, - #131) and UnivMon rebuilds heaps from the union of candidates, so both - need measured merge curves. -- **Coarse groupings.** A grouping inside `Λ` is answered directly, because - its subkey was inserted; Hydra never rolls up, which is why - `mergeable_across_groups` is false. The cost is mass: every coarser subset - inserted adds to `M`, and so to every group's contamination. A grouping - not inside `Λ` cannot be answered. - -### 2.4 The accuracy predicate - -Because only empirical accuracy is usable, a Hydra edge is eligible only if -a measurement covers it: - -```text -hydra_error(variant, config (R, W, inner), |Λ|, G_r, shape(G_r), - N_q distribution, M, merged windows) <= the RAQE's SLA -``` - -in the family's metric and direction, with no borrowing from a -non-Hydra curve or another grouping. One `subpopulations` ceiling (today's -`hydra-kll` eligibility) is not enough: two groupings of equal cardinality -differ in skew and fan-out. Since error is driven by `N_q / (M / W)`, the -measurement should report error by group size, and the SLA must say which -groups it covers (§6). - -## 3. Cost model - -Hydra is priced by [Cost models](rqe_optimizer_cost_model.md) §3's table, -with no separate formula. One grid is one instance per window, so -`I_d = I_r = 1`, and a query probes it once per `card(G_r)` group. Let -`m_h`, `c_ins,h`, `c_mrg,h` and `c_probe,h(G_r)` be measured for one -variant, configuration and schema width `|Λ|`; `c_ins,h` is per record, fan-out -included (`R · (2^|Λ| − 1)` inner inserts). - -| Phase | CPU-seconds per event | Mean vCPUs | Memory (bytes) | -|---|---|---|---| -| Ingest | — | `λ · a_D · c_ins,h` | `m_h · a_D · k_D` | -| Compaction | `(k_D − 1) · c_mrg,h` | `/ y_D` | `k_D · m_h`, while it runs | -| Query job | `card(G_r) · c_probe,h(G_r) + (n_{i,D} − 1) · c_mrg,h` | `/ T_i` | `m_h · [n_{i,D} > 1] + card(G_r) · output bytes`, while it runs | -| Storage | — | 0 | `m_h · ((max_i S_i − x_D) / y_D + 1)` | - -plus the key tracker's part. A Hydra merge combines whole grids, not -`card(G_d)` addressable states, which is what `I_d = 1` says. This assumes -the merge cost does not depend on the number of probes; sketch-bench must -confirm it or report a probe-count term. - -**Key tracker.** If groups are listed by a DeltaSet (`exact-delta-set`), its -ingest, compaction, merge, query and storage are added as a part. A tracker -at `Λ` that derives coarse keys costs `card(Λ)` per window, not -`card(G_r)`; a tracker per requested grouping costs `card(G_r)` each but -multiplies trackers. This is a decision (§6), not a free operation. - -## 4. Candidates and the MILP - -The MILP does not change: one binary `z_{i,D}` per eligible edge, one -`u_D` per candidate, `stored_D`, the same constraints and objective -([Cost models](rqe_optimizer_cost_model.md) §7). Hydra adds candidates and -edges with their own eligibility and coefficients. - -- **Candidate identity.** `(capability, variant + config, metric, spatial - filter, Λ, x, y, key tracker)`: the schema `Λ` takes the place of the - grouping. `Λ` is an ordered list: the library encodes subkeys in its - declaration order, merges only grids of the same order, and the wrapper's - prefix queries depend on it. So `Λ` needs its own ordered field (a - `LabelSet` loses the order), plus a new family property (e.g. - `answers_any_subgrouping`) that marks Hydra. -- **Generation.** For each (capability, metric, filter), `Λ` ranges over - every distinct RAQE grouping and their union (more schemas cost more, - since `M` and `c_ins,h` grow with `|Λ|`). Windows and slides come, as for - roll-ups, from the RAQEs whose grouping is a non-empty subset of `Λ`. -- **Edge `(i, D)`.** Same capability, metric and filter; `∅ ≠ G_i ⊆ Λ` - (a prefix of `Λ` until the wrapper queries any subset); window alignment - as today; and the §2.4 predicate measured and passing. -- **Linearity.** An edge's accuracy depends only on the candidate (`Λ`, - config, window) and the metric's mass, never on which other RAQEs share - it: everything on the metric and filter is inserted whatever is selected. - So eligibility stays a per-edge filter and the MILP stays linear. -- **Coefficients.** Per candidate: ingest, compaction, storage (§3, plus the - tracker). Per edge: the query job, with `c_probe,h(G_i)`. A Hydra edge is - never priced or validated as a roll-up. -- **Pruning.** Unchanged: candidates compare only at the same capability, - metric, filter and grouping (here `Λ`), on the costs listed in - [candidates](rqe_optimizer_candidates.md). -- **Competition.** A quantile RAQE by `(service)` can then be served by KLL - at `(service)`, by KLL at `(service, endpoint)` through a roll-up, or by - Hydra with `Λ ⊇ {service}`; the MILP picks the cheapest that passes. - -Code this needs, in order: `FamilyProperties` for the four other variants; -the new property and the Hydra branch in `is_eligible` and -`build_all_candidates_unpruned`; Hydra in `Capability::families` and -`DEPLOYABLE_FAMILIES`; the saturation lookup keyed as in §2.4; cost-table -rows. AutoSketch keeps skipping Hydra (#159). - -## 5. What sketch-bench must add - -Each item names what is missing today. Every Hydra measurement sketch-bench -has today predates these requirements, so admitting Hydra means rerunning -them all; `hydra-kll` is measured in that same run, alongside `hydra-cms`, -`hydra-cs`, `hydra-hll` and `hydra-univmon`, under every item below. - -1. **Target grouping.** Comparators score only column 0 - (`aqpbm-cli/src/rows/mod.rs`); they must score any named subset of the - schema, and the wrapper must query any subset, not only prefixes - (`hydra_shared.rs` `query`). -2. **Exact baseline.** The `polars` baselines build untagged subkeys - (`polars_shared.rs`), so overlapping label values alias there but not in - the 0.3.0 sketch (#74); tag them the same way. -3. **Data.** Hierarchical labels with set cardinality per label, per-parent - fan-out and skew; the result records `|Λ|`, the target grouping's - cardinality, every group's `N_q`, and the fanned-out mass `M`. -4. **Sweep.** Grid (`R`, `W`), inner parameters, schema width, target - grouping, cardinality and fan-out, distribution. Report error per group - against `N_q / (M / W)`, and quantiles over groups, not only the mean. - The column hash has a fixed seed, so vary the label values (not only the - data seed) to see the spread over collision patterns. -5. **Bounds as checks.** For CMS, CountSketch and HLL, assert the measured - per-group error is within §2.2's bound at its confidence; a violation - means a bug. -6. **Merges.** Accuracy and cost of merged grids at the shard counts - workloads need, with records split by sample (interleaved), not only in - contiguous chunks (`sketch-bench/src/wrappers/mod.rs`); exactness checks - for CMS, CountSketch and HLL; merge curves for KLL and UnivMon cells. -7. **Cost table.** Hydra rows in `study_saturation.py` (`SKETCHES`, - `OPTIMIZER_FAMILIES`) and `approxbench atomic-costs`: `m_h` (including - each KLL cell's merge buffer, #191), `c_ins,h` per record at the schema - width, `c_mrg,h`, and `c_probe,h` per probe at the target grouping. -8. **Key enumeration.** Benchmark the chosen tracker (§6) at its grouping, - with its input and output cardinality. -9. **Saturation key.** Emit §2.4's fields machine-readably. The optimizer - rejects a Hydra edge with no matching point unless an interpolation rule - is approved (§6). -10. **Fixtures.** Equal grouping, a direct coarse subset, and a multi-window - merge, each against exact answers, asserting the exported accuracy and - cost fields. - -## 6. Decisions needed - -1. Group enumeration: one DeltaSet at `Λ` that derives coarse keys, or one - per requested grouping? -2. Groupings: prefixes of `Λ` only, or any non-empty subset once the wrapper - and comparators support it? -3. Which groups an SLA covers: every group (then Hydra almost never passes - for small groups), groups above a size, or a quantile over groups? -4. Interpolation between measured Hydra points: none, or the worse - bracketing point as for the other sketches? -5. Schemas generated per workload: each RAQE grouping and their union, or - more? -6. Whether `hydra-univmon` entropy, which has no bound, is admitted at all. diff --git a/docs/rqe_sketch_deployment_v1.md b/docs/rqe_sketch_deployment_v1.md deleted file mode 100644 index 22cf46ee..00000000 --- a/docs/rqe_sketch_deployment_v1.md +++ /dev/null @@ -1,218 +0,0 @@ -# RAQE-to-sketch deployment mapping: v1 - -## Purpose - -Given a workload of repeating atomic query expressions (RAQEs), find sketch deployments -that can serve it and report the trade-offs among query working memory, CPU, -and per-RAQE latency. - -## Glossary - -- **RAQE**: repeating atomic query expression, evaluated every `T` over the last `S`. -- **Capability**: the statistic an RAQE needs: sum/count, min, max, - rate/increase, quantile, cardinality or top-k. -- **Metric**: a named stream of samples whose series are told apart by labels. -- **Group**: one combination of values of an RAQE's group-by labels `G`. There - are `card(G)` groups. -- **Family**: one sketch or accumulator implementation, such as `kll-percall`, - `cms-heap-topk-fastpath-vector2d` or `exact-sum`. Each capability lists the - families that serve it. -- **Configuration**: a family plus its parameters, such as KLL with `k = 200`. - One row of the cost table. -- **Cost table**: per configuration, measured memory per instance, CPU per - insert, merge and query, and accuracy in its named `accuracy_metric`. - Measured by - [`scripts/study_saturation.py --phase optimizer-cost`](../scripts/study_saturation.py). -- **Window**: the interval `[k × y, k × y + x)` an instance covers; `x` is the - window size and `y` the slide. -- **Instance**: one sketch or accumulator object holding one group's samples - (or all groups', for a shared family) over one window. Open or closed; see - [Window model](#window-model). -- **Deployment**: a configuration run over one metric, grouped by `G`, with - window `x` and slide `y`. A **candidate** is a deployment the optimizer may - choose. -- **Phase**: one of the kinds of work a deployment does, each costed in CPU - and memory: **ingest** (inserting samples into open instances), - **compaction** (merging the ingest workers' copies of a closed window), - the **query job** (merging a query's windows, and on a roll-up its fine - groups, then reading the result) and **storage** (keeping closed - instances, no CPU). The snapshot objective and `PlanCost` report the query - job as two phases, **merge** and **query**, and have no compaction. See - [Cost models](rqe_optimizer_cost_model.md). -- **Roll-up**: a deployment grouped by `G_d` serving a RAQE grouped by a - subset `G_r`, by merging each coarse group's fine instances at query time. - -## Inputs - -Let `R = {r_1, ..., r_n}` be the RAQE workload. Each RAQE `r_i` provides: - -| Field | Meaning | -|---|---| -| `cap_i` | Required capability. | -| `S_i` | Query-window size. This is the current `lookback` field. | -| `T_i` | Query slide: how often the RAQE runs. This is the current `interval` field. | -| `metric_i` | Metric the RAQE reads. | -| `G_i` | Group-by label set (`grouping_labels`). | -| `tol_i` | Accuracy threshold, checked against each candidate family's own metric. | - -For each metric, the caller provides `MetricFacts`: - -- `labels`: every label the metric's series carry. -- `scrape_interval`: every series yields one sample per scrape. -- `card(X)`: distinct value combinations of each label set `X` in use, - including `labels` itself (the raw series count). -- `value_range` (optional): `(lo, hi)`, the smallest and largest positive - sample value, with `0 < lo <= hi`. It sizes DDSketch (see below). - -The arrival rate is derived as follows (not provided by the user): - -```text -lambda(metric) = card(metric.labels) / scrape_interval samples/second -``` - -`validate_facts` reports every missing or inconsistent fact up front. - -The cost table, measured by -[`scripts/study_saturation.py --phase optimizer-cost`](../scripts/study_saturation.py) -(#174), gives for each configuration: - -- memory per instance; -- CPU per insert; -- CPU per query of one instance; -- CPU per pairwise merge; -- the comparator's accuracy scores, and `accuracy_metric`, the one that is - the row's accuracy (only exact accumulators read it; see - [eligibility](rqe_optimizer_candidates.md#candidate-deployments)); and -- `measured_at`, the data it was measured on. - -The study measures every configuration once, serially, at one data shape: -Zipf θ = 1.1 over 1e4 keys (Pareto `a` = 2 for quantiles), 1e6 items, 5 runs -after 3 warm-ups. That is the dataset of the AutoSketch vs. ASAP evaluation -(ASAPQuery#777), so the evaluation's costs and the cost table are one -measurement. Cost is taken as independent of the data shape. The same study -measures the saturation curves, so the configurations in both are one grid. - -Each metric's facts also carry a fitted `data_shape` per grouping, which keys -the saturation curves. - -Families are named by sketch-bench variant, not algorithm, because variants -of the same algorithm can have different costs. - -## Window model - -A deployment materializes sliding sketch instances with: - -- `x`: sketch-window size; -- `y`: sketch slide. - -For every label-value group, it creates an instance for every interval: - -```text -[k × y, k × y + x) -``` - -At time `t`, an instance is **open** while its interval has not ended -(`t < k × y + x`): it still receives samples. After that it is **closed**: it -never changes again and is kept only while some RAQE's lookback still needs it. -A deployment has `x / y` open instances per group at any time. - -Instances overlap when `x > y`. There is no subtract operation. To answer an -RAQE, the system selects and merges only non-overlapping instances, so items -are never counted twice. - -An RAQE with query window `S` and query slide `T` can use a deployment when: - -```text -x % y == 0 -S % x == 0 -T % y == 0 -``` - -For a query ending at time `t`, use the instances: - -```text -[t-S, t-S+x), [t-S+x, t-S+2x), ..., [t-x, t) -``` - -They exactly cover the query window and do not overlap. The deployment may -have produced other, overlapping instances between these starts. That is okay. Those overlapping instances are not used for this query. - -`S % T == 0` is not required. For example, a 10-minute window running every -3 minutes can use `x = 2 minutes` and `y = 1 minute`; each query merges five -non-overlapping two-minute instances. - -We assume all RAQEs share a common time origin. Query phase offsets are not -modeled. - -## Planning design - -The optimizer has three companion design documents: - -- [Candidate generation and eligibility](rqe_optimizer_candidates.md): - candidate construction, eligibility (accuracy included), finer-to-coarser - roll-ups and dominance pruning. -- [Cost models](rqe_optimizer_cost_model.md): the one cost table, the - canonical cost-by-use and batch-latency model with its MILP, and the - supported snapshot-AUC objective. -- [Hydra](rqe_optimizer_hydra.md): what admitting Hydra needs (accuracy - guarantees per combination, cost model, candidates and MILP edges, and - sketch-bench measurements). Not implemented. - - -## Procedure - -1. Generate candidate deployments for each input RAQE, then deduplicate them. -2. Build the eligible candidate–RAQE pairs. Reject pairs that fail capability, - metric, grouping (equal, or a subset for a roll-up), window alignment, or - accuracy. -3. Enumerate feasible workload mappings. Every RAQE must select one eligible - candidate; a single selected candidate may serve multiple RAQEs. Small - instances may retain every mapping eagerly; larger ones stream mappings - through incremental Pareto filtering, retaining only the current frontier. -4. Score every complete mapping with the analytical cost model. -5. Compute and report the Pareto frontier. - -The Pareto vector is: - -```text -(CPU, memory, {latency_i}) -``` - -CPU and memory are the sums over the snapshot objective's phases (ingest, merge, query, storage). The report also includes -each phase's CPU and memory, together with the selected deployment mapping. - -## v1 scope and TODOs - -- **Accuracy after merging:** KLL and univmon-cardinality read their merge - curves up to the largest shard count and N the study measured; past them, - KLL's guarantee, no better than the last measurement (univmon-cardinality - has none, so no accuracy). Roll-ups multiply the merge count by the - fan-out, so they reach the guarantee sooner. Heap top-k reads its plain - curve (#158). -- **Query-result sharing:** v1 charges every RAQE its own query and merge CPU. - Revisit when RAQE semantics and execution timing identify safe reuse cases. -- **Latency SLAs:** `minimize` takes optional per-RAQE latency bounds; - `minimize_usage_cost` takes an optional batch-latency bound and reports the - batch latency. The enumerator reports latency but does not reject a - mapping for it. -- **Memory model:** cost by use holds query memory only while a query runs; - the snapshot objective sums every RAQE's merge and output memory as if all - queries ran at once. Query output size is an estimate. -- **Bursts:** CPU is a mean over time, so plans are sized for average load. -- **Static planning:** no RAQE churn, replanning, or migration cost. -- **Temporal pre-merging:** v1 does not precompute merged windows. Queries - merge their selected base instances when they run. -- **Selection policy:** the enumerator reports the Pareto frontier; the MILP - selects one point by the `w_cpu`/`w_mem` weights. - -## Implementation status - -`rqe-optimizer/` implements the `(x, y)` sliding-instance model, candidate -generation and pruning, eligibility checks, analytical objectives, and -streaming Pareto filtering. The separation between candidate generation, -enumeration, objectives, and Pareto filtering remains the intended boundary -for future solver work. Hydra, a shared-subpopulation deployment rather than -a fine-state roll-up, is designed in [Hydra](rqe_optimizer_hydra.md). - -Exporter bugs, benchmark-dataset choices, and historic run counts belong in -`rqe_optimizer_TODO.md` or issue tracking, not in this problem statement. diff --git a/docs/saturation_study.md b/docs/saturation_study.md index f0319949..2e5dde96 100644 --- a/docs/saturation_study.md +++ b/docs/saturation_study.md @@ -269,7 +269,7 @@ written for). A Hydra grid's error for a group depends on the group's share of the fanned-out mass `N·(2^d − 1)`, not on a per-key shape, so it is measured on data with the §6.3 eval's own schemas rather than on the Zipf grid above -(docs/rqe_optimizer_hydra.md). `HYDRA_SWEEPS` holds two sweeps: +(ASAPQuery `rqe-optimizer/docs/rqe_optimizer_hydra.md`). `HYDRA_SWEEPS` holds two sweeps: - `eval`: `hydra-hll` and `hydra-univmon-cardinality` on `configs/datagen/hydra_http.yaml` and `hydra_flows.yaml`, `hydra-kll` on @@ -637,4 +637,3 @@ Crossover at theta = 1, K = 1e5 (frequency, top-k, HLL) and Pareto alpha 1.5 now covers every top-k and KLL config. - **HLL lg_k 10.** asap_sketchlib has register types for lg_k 12, 14, 16 (and 18 for the bucket list) only, so the HLL grid starts at 12. - diff --git a/rqe-optimizer/Cargo.toml b/rqe-optimizer/Cargo.toml deleted file mode 100644 index c32dafa5..00000000 --- a/rqe-optimizer/Cargo.toml +++ /dev/null @@ -1,13 +0,0 @@ -[package] -name = "rqe-optimizer" -version.workspace = true -edition.workspace = true -license.workspace = true -repository.workspace = true -description = "v1 brute-force solver for the RQE -> sketch-deployment mapping problem (docs/rqe_sketch_deployment_v1.md). Consumes aqpbm-core's AtomicCostEntry directly so a real atomic-cost export can be dropped in later without a translation layer." - -[dependencies] -aqpbm-core = { workspace = true, default-features = false } -serde_json.workspace = true -good_lp.workspace = true -csv.workspace = true diff --git a/rqe-optimizer/README.md b/rqe-optimizer/README.md deleted file mode 100644 index e42df2c8..00000000 --- a/rqe-optimizer/README.md +++ /dev/null @@ -1,209 +0,0 @@ -# rqe-optimizer: data, results, and how to use them - -`rqe-optimizer` plans sketch deployments for a workload of RQEs (repeating -query expressions): it generates candidate deployments, prices them, and -solves a MILP for the cheapest plan that meets every RQE's accuracy target. -This page covers the data it reads and the results committed here: what each -file is, how to read it, and how to rerun or reuse it. The models themselves -are in `docs/rqe_optimizer_cost_model.md`, `docs/rqe_optimizer_candidates.md` -and `docs/rqe_optimizer_hydra.md`. The evaluation plan is -ProjectASAP/ASAPQuery#777 (`docs/evaluation/autosketch-vs-planner.md`). - -## Layout - -| Path | What it is | Status | -|---|---|---| -| `src/` | the optimizer: `candidates.rs` (candidates, roll-ups, Hydra), `usage.rs` (cost by use, batch latency), `milp.rs` (`minimize_usage_cost`), `autosketch.rs` (the AutoSketch baseline), `saturation.rs` (accuracy lookup) | | -| `examples/autosketch_vs_asap.rs` | the §6.3 runner: one workload in, one result JSON out | | -| `examples/workload_scenarios.rs` | how the optimal plan moves as one knob of a workload changes (types, groupings, windows, SLAs) | | -| `examples/small_problem.rs` | a toy instance solved end to end (brute force or MILP) | | -| `results/autosketch-vs-asap-hydra/` | **the current §6.3 results** (2026-10-11; classic and all rerun 2026-10-13 without templates 7 and 9; all rerun 2026-10-14 on the denser grid, #213): roll-ups, the roll-up ablation, Hydra candidates, the classic, multi-grouping and mixed + multi-grouping workloads, and each plan | current | -| `results/e2e-rate10/` | the classic, multi-grouping and all workloads (r = m = 1) re-planned at 10 samples/s per series, for the end-to-end run (on the grid before #213's denser one) | current (e2e input) | -| `results/case-studies-6.3.1/` | the paper's §6.3.1 case studies (performance monitoring, anomaly detection): tables, every method's plan, and what each deploys | current | -| `results/autosketch-vs-asap-inputs/` | the accuracy curves every run reads (#138's study), and #138's tables and raw records | current (shared input) | -| `results/autosketch-vs-asap-synthetic/`, `results/autosketch-vs-asap-synthetic-sla/` | the 2026-10-08 results (#138): before roll-ups and the KLL footprint fix | superseded by `-hydra` | -| `data/autosketch-eval/table.json` | the trace workloads (Alibaba 2022, Google 2011, BOOM) | not yet rerun on cost by use | - -Use `results/autosketch-vs-asap-hydra/` for anything new. Its `README.md` has -the full analysis; this page explains the files. - -## Reading the results - -Each workload is one file, `synthetic-templates-shared[-metrics]-tp95.json`: - -| Name part | Meaning | -|---|---| -| `templatesclassic` | the mixed set: 8 PromQL templates on one metric (42 RQEs); templates 7 and 9 (`sum by (job) (rate(...))`, `topk(32, sum by (label_0) (rate(...)))`) are dropped, as ASAPQuery would need per-series counter deltas to serve them | -| `templatesmultigroup` | the multi-grouping templates alone: distinct counts and p99s over several groupings of `http` and `flows`, including p99 `GROUP BY CUBE(region, service, endpoint)` (62 RQEs, exported with `--keep-duplicate-queries`; 52 without) | -| `templatesall` | classic + multi-grouping (104 RQEs, exported with `--keep-duplicate-queries`; 94 without) | -| `shared8` | 8 users query the same streams with their own windows, quantiles and repeat intervals | -| `metrics8`, `metrics16` | 8 or 16 independent copies of the workload, each on its own metric | -| `tp95` | every RQE must meet 95% accuracy in its sketch's own metric | - -Start with the summaries and figures: - -- `summary_synthetic.md`: every method's cheapest plan and every frontier - point, per workload and weight setting. -- `summary_sla.md`: each method's cost at each batch-latency SLA. -- `fig_frontier.png`, `fig_cost_vs_sla.png`, `fig_planning_time.png`, - `fig_rollup_ablation.png` (the README describes each). - -### A result file - -| Key | Meaning | -|---|---| -| `workload`, `rqes`, `streams` | the workload, its RQE count and its streams | -| `weights` | the weight settings: `cpu` (`w_cpu = 1`, cost in vCPU) and `fargate` ($/vCPU-hour and $/GB-hour, cost in $/hour) | -| `results` | version 1, no SLA: each method's cheapest plan (`bound_ms: null`) and its frontier points (`bound_ms`: the latency bound it was solved under) | -| `sla_results` | version 2: each method's cheapest plan at each SLA (`sla_ms`); `infeasible` when no plan meets it; AutoSketch's has `meets_sla` | -| `frontier` | each method's tightest feasible bound and AutoSketch's latency | -| `autosketch` | AutoSketch's search: probes, `search_secs`, `benchmark_secs_measured` (its benchmark runs, timed) | -| `dropped_unservable`, `sanity_violations` | RQEs no method could serve, and failed checks (ASAP ≤ no roll-ups ≤ PerQuery, ASAP ≤ AutoSketch); both are empty in every committed run except one solver-tolerance frontier check in `autosketch-vs-asap-targets` (see its README) | -| `notes` | what the runner assumed | - -Every entry of `results` and `sla_results` is one plan: - -| Field | Meaning | -|---|---| -| `method` | `asap`, `asap-norollup` (the roll-up ablation), `perquery` (no sharing), `autosketch` | -| `weights` | `cpu` or `fargate` | -| `objective` | the plan's cost by use, in the weights' unit | -| `cpu`, `cpu_parts` | mean vCPUs, split into ingest, compaction, query | -| `gib`, `gib_parts` | mean GiB, split into ingest, storage, compaction, query | -| `latency_ms`, `median_latency_ms` | batch latency (the longest compaction + query chain) and the median over RQEs | -| `active_deployments` | deployments the plan runs | -| `rolled_up_rqes`, `hydra_rqes` | RQEs served by merging a finer deployment's groups, and by a Hydra grid | -| `planning_secs`, `milp_solve_secs` | planning time | - -The cheapest plan of each method: - -```python -import json -d = json.load(open("synthetic-templatesmultigroup-shared1-tp95.json")) -for r in d["results"]: - if r["weights"] == "cpu" and r.get("bound_ms") is None: - print(r["method"], r["objective"], r["latency_ms"], r["active_deployments"]) -``` - -Every method at the same latency (the paper table's comparison): take each -method's cheapest plan with `latency_ms <= d["frontier"]["autosketch_latency_ms"]`. - -### Plans - -The result files leave out which deployment serves each RQE (the runner's -`--no-chosen`). The plans are in `plans/.plans.json.gz`. Each file -lists, for every method and weight setting, the cheapest plan -(`kind: "cheapest"`) and the plan at each feasible SLA (`kind: "sla"`); each -plan's `chosen` has one entry per RQE: - -| Field | Meaning | -|---|---| -| `rqe` | the RQE's id: `///` | -| `sketch`, `params` | the sketch and its config | -| `window_secs`, `slide_secs` | the deployment's window and slide | -| `merged_shards` | the windows a query merges (lookback / window) | -| `deployment` | the deployment's index: RQEs with one index share one deployment | -| `deployment_grouping` | only when it differs from the RQE's: the finer grouping it rolls up from, or a Hydra grid's schema | -| `ingest_workers`, `latency_ms` | ingest workers, and the RQE's compaction + query chain | -| `asap_accuracy`, `accuracy_source` | estimated error, and whether it was measured or from theory | -| `covers_share` | the RQE's coverage rule, if any | - -```sh -zcat plans/synthetic-templatesmultigroup-shared1.plans.json.gz | jq '.plans[] - | select(.kind == "cheapest" and .method == "asap" and .weights == "cpu") | .chosen' -``` - -To turn a plan into deployments, group `chosen` by `deployment`: each group is -one sketch (`sketch`, `params`) over the stream, kept per group of its -grouping, in windows of `window_secs` that start every `slide_secs`, serving -the listed RQEs. A deployment is tumbling when the two are equal and sliding -otherwise (the classic set's cheapest ASAP plan at CPU weights has a 300 s -window with a 60 s slide). - -## The inputs - -### Workload tables - -`results/autosketch-vs-asap-hydra/inputs/tables/*.json`, from -`scripts/export_autosketch_eval_table.py --synthetic`. They are built as RQEs -directly; no PromQL is parsed. `plan.tsv` lists one run per table. A table -has `workloads[0].rqes` (one entry per RQE) and `workloads[0].schemas` (each -multi-grouping metric's labels, cardinalities and skew). An RQE: - -| Field | Meaning | -|---|---| -| `id`, `query_id`, `range` | its name, and its lookback as written (`5m`) | -| `capability` | `quantile`, `cardinality`, `topk`, `sum`, `rate` | -| `stream`, `metric` | the data it reads (`http/latency`); RQEs on one stream can share deployments | -| `grouping` | its group-by labels (multi-grouping RQEs; others group per series or by job) | -| `lookback_secs`, `interval_secs` | lookback `S` and repeat interval `T` | -| `families` | the sketches that may serve it, each with its accuracy `target` and the data shape it is read at (`grid_param`: Zipf θ or Pareto a; `grid_K`: distinct keys) | -| `label_set` | groups, samples/s of the stream, keys per window | -| `min_N`, `max_N` | items per group per window (mean group) | -| `covers_share`, `covered_min_N`, `covered_max_N` | the coverage rule (only groups holding at least this share of records, plus the largest; `null`: every group) and the items of its smallest and largest covered group, where accuracy is read | - -Every table also has `workloads[0].e2e`, for running a plan end to end -(sketch-bench#204). The runner does not read it, so the plans are unchanged. - -| Field | Meaning | -|---|---| -| `e2e.queries` | classic: one entry per PromQL query and repeat interval: `promql`, `interval_secs`, and the `rqes` it reads (template 10's ratio reads two). Multi-grouping (no PromQL): one entry per distinct query and interval: canonical ASAPQuery `sql` over `NOW()` (the planner floors and replaces it), plus `table`, `value`, `capability`, `grouping`, `quantile` (p99: 0.99), `window_secs`, `interval_secs`, and the `rqes` it reads: only the strictest of templates 14, 15 and 17 where they share a grouping, or every one, sorted, with `--keep-duplicate-queries` | -| `e2e.metrics` | the metrics to generate. Classic: gauge `data` (sums, quantiles, top-k by sum) and counter `data_total` (rates), with `labels` in the `schemas` format (`label_0` is `child_of: job`, 1,000 per job, so 1e4 series), samples/s per series, and each stream's value shape. Multi-grouping: `type: "records"` tables `http` and `flows` (`data_1_http` for metric `data_1/http` in `metrics=m` tables) with the schema's `labels`, `records_per_sec` and `values` (shape, `scaled_by`, `anomaly`, `burst`) | - -### Accuracy and cost - -| File | Rows | Used for | -|---|---|---| -| `autosketch-vs-asap-inputs/saturation/out_grid_1e7_cost/saturation_curve.csv` | error vs. items `n`, per sketch config and data shape (`dist`, `param`, `cardinality`), seed mean | a deployment's accuracy at its items per instance | -| `.../out_grid_1e7_cost/saturation_merge_curve.csv` | the same after merging `m` shards | KLL and top-k when a query merges windows | -| `.../out_1e9/` | points past 1e7 items | large windows | -| `autosketch-vs-asap-hydra/inputs/saturation/optimizer_cost/rqe_atomic_costs.json` | one row per config: `insert_cpu_secs`, `merge_cpu_secs`, `query_cpu_secs` (per operation), `mem_bytes_per_instance`, and `measured_at` (items, data shape; for Hydra, its `dataset` and `schema_width`) | every plan's cost | -| `autosketch-vs-asap-hydra/inputs/saturation/hydra_saturation.csv` | Hydra error per (variant, config `R`×`W`, dataset, grouping, records, seed): mean, p50, p90, max, and max over groups holding ≥ 1% / 5% (`err_max_cov_*`) | Hydra grids' accuracy | - -The raw study records are in `autosketch-vs-asap-inputs/raw/`. - -## How to use them - -**Reproduce the committed results.** Build, assemble one saturation directory -(the curves plus this run's cost table and Hydra study), and run every row of -`plan.tsv`; the exact commands are in -`results/autosketch-vs-asap-hydra/README.md`, "Reproduce". Costs are -deterministic; planning times depend on the machine. - -**Run a new workload.** Write a table in the format above (add templates to -`SCHEMA_TEMPLATES` in `scripts/export_autosketch_eval_table.py`, or write the -JSON yourself) and run: - -```sh -target/release/examples/autosketch_vs_asap synthetic --table MY_TABLE.json \ - --target p95 --runs 1 --saturation-dir SAT --out MY_RESULT.json -``` - -Leave out `--no-chosen` to keep each plan's `chosen`; add `--weights cpu` or -`--slas-ms 1000` to run less. A new data shape (θ, K, a) or sketch config not -on the measured grid needs a new saturation study -(`scripts/study_saturation.py`); new windows, intervals, groupings or -queries on the same shapes do not. - -**See how the plan reacts to one change.** `examples/workload_scenarios.rs` -steps one knob of a base workload and prints the deployments chosen: - -```sh -cargo run --release -p rqe-optimizer --example workload_scenarios -- \ - --saturation-dir SAT [--sweep NAME] -``` - -**Run plans end to end.** The plans give each deployment's sketch, config, -grouping and window; ASAPQuery's planner (`asap-planner-rs`, the MILP port) -produces the same kind of plan from PromQL or SQL. Hydra grids are not in -ASAPQuery's deployable families, and no committed plan uses one. - -## Caveats - -- Costs and latencies are estimates from per-operation measurements, not - measured end-to-end runs. -- CPU is elastic and billed by use (an idealized autoscaler), so cost is a - lower bound for provisioned machines. -- A merged top-k heap of `m · k` is assumed to keep the true top `k`. -- The trace workloads (`data/autosketch-eval/`) have not been rerun on cost - by use. diff --git a/rqe-optimizer/data/autosketch-eval/README.md b/rqe-optimizer/data/autosketch-eval/README.md deleted file mode 100644 index ddf7bde0..00000000 --- a/rqe-optimizer/data/autosketch-eval/README.md +++ /dev/null @@ -1,103 +0,0 @@ -# AutoSketch vs ASAPQuery evaluation table - -`table.json` holds the accuracy and cost evidence for the AutoSketch vs -ASAPQuery planner evaluation (plan: ProjectASAP/ASAPQuery#777, section 6). It -is generated by `scripts/export_autosketch_eval_table.py`. - -## Contents - -Workloads (W2): one per dataset in ASAPQuery's -`asap-tools/dataset-analysis/results/skew_summary.csv`. - -| Dataset | RQEs | Config rows | Saturated rows | -|---|---|---|---| -| alibaba_v2022 | 51 | 1020 | 867 | -| boom | 20 | 140 | 47 | -| google_2011 | 27 | 546 | 403 | - -Each RQE is one (dataset, query_id, kind, range). Its data parameters are the -worst case over the **whole dataset** (`worst_theta_cms`, `worst_K`, `max_N`, -`worst_alpha_rank`/`worst_alpha_memory`), rounded to the measured grid toward -the harder side by `recommend_config.grid_point`. There is no burst variant: -these whole-dataset worst cases replace AutoSketch's synthetic burst injection. - -Each RQE lists every measured config of every candidate family (keys: CMS, -CountSketch, CMS-heap top-k; values: KLL, DDSketch), with: - -| Field | Meaning | -|---|---| -| `autosketch_error` | Single-sketch curve at `N = max_N`. AutoSketch keeps one sketch per query window and never merges, so it has no saturation requirement. | -| `asap_error_by_m` | Error after merging `m ∈ {1, 4, 16, 64}` shards, at `N = max_N`. CMS, CountSketch, HLL and DDSketch merge exactly: every `m` reads the single-sketch curve. KLL and top-k read the merged curve at `m` (sketch-bench #131); a missing one falls back to the single-sketch curve, flagged "single-sketch curve optimistic". | -| `saturated` | `max_N ≥ n_sat`. ASAP merges smaller-window sketches, so it may only use a config's value when this is true. | -| `n_sat`, `not_saturated_in_grid` | From `saturation.csv`; the 1e9 subset wins when it has the point. | -| `memory_bytes`, `insert_ns_per_item`, `merge_us_per_fold`, `query_phase_us` | `recommend_config.cost_of`. `query_phase_us` is the benchmark's whole query phase, not one query. | -| `*_flag*` | Grid and interpolation flags: "N below grid", "extrapolated beyond N=…", "cost scaled from rows=3". | - -Per family: `error_metric`, `higher_is_better`, `target` (from -skew_summary), and the grid point used. - -## Label sets and windows (assumptions) - -`rqe-optimizer` needs `card(ℓ)` (sketch instances) and `λ(ℓ)` (items/s). -skew_summary.csv describes one population per query, so: - -- **`groups = 1`.** A key query (`sum by (k)`) is one sketch whose keys are - the group-by values (`keys_per_window = worst_K`). It is not one sketch per - group. Value queries have no `by`. -- **`arrival_rate_per_sec = max_N / lookback_secs`**: the rate in the worst - window. -- **Instant queries:** `lookback_secs = step_s`, because `max_N` counts one - step's samples. -- **BOOM** has no timestamps, window or N in skew_summary, so - `boom_query()` derives them from its 20 equal chunks per series: - - `N = rows_total / 20`; - - `lookback = interval` = one chunk's duration, from the sampling step in - the series name (`ds-2187-H` is hourly); - - a series without an alpha fit uses the hardest grid alpha for error and - the easiest for memory. - - Each RQE's `assumptions` field lists what was assumed. - -## Regenerate - -The raw curves (2.8 MB) are not committed. Produce them with -`scripts/study_saturation.py`: the grid from #130 (`--phase all`), the 1e9 -subset, and `--merge-shards-list 1,4,16,64` from #131 for top-k and KLL/DDSketch. -Then run: - -```sh -python3 scripts/export_autosketch_eval_table.py \ - --summary /asap-tools/dataset-analysis/results/skew_summary.csv \ - --curves out_grid_1e7_cost/saturation_curve.csv \ - --curves-big out_1e9/saturation_curve.csv \ - --saturation out_grid_1e7_cost/saturation.csv \ - --saturation-big out_1e9/saturation.csv \ - --merge-curves out_merge_topk/saturation_merge_curve.csv \ - out_merge_quantile/saturation_merge_curve.csv \ - --revision "curves: sketch-bench #130 study_saturation.py; merge curves: sketch-bench #131 (branch merged-accuracy)" \ - --out rqe-optimizer/data/autosketch-eval/table.json -``` - -The merge curves were produced on #131's branch at a0b8b42. #131 has since -merged into main as bd644fe with identical content, so the recorded revision -string above still describes the inputs. - -`table.json` records the SHA-256 of every input under `inputs`: - -| Input | SHA-256 | -|---|---| -| skew_summary.csv (ASAPQuery 46f3942) | `f5984b655e7720449fdf081dd4c0a8d883b5bf2a9fd968f379fa8d44df364382` | -| out_grid_1e7_cost/saturation_curve.csv | `2d34a925c5bf8e4e72cab5932f63f8ec6e9cdf3e57b07f21aae2e5192144ca1a` | -| out_grid_1e7_cost/saturation.csv | `569941ea162698963671d2a0ad40faeff53614d3cdb374de2a3b8aa3917ae674` | -| out_1e9/saturation_curve.csv | `fb8fc62a16e137b831c20c16fe8d9b846e3fe80eb468451f08a727514d1e2bfd` | -| out_1e9/saturation.csv | `0dd8448349f63317d7f4d385ce3063ab78d8daf3504015f580b7147cd22a3f22` | -| out_merge_topk/saturation_merge_curve.csv | `ec43995c8b9ac0862fe5c19f038693fbce6e73cf48ebf46fb3bacbfb474d4ef6` | -| out_merge_quantile/saturation_merge_curve.csv | `d4fb544bf14b06274f2558632faa766b5601ba4f942b48119f45c04aa39898f8` | - -## Limits - -- Merged curves cover `N ≤ 1e7` and `m ≤ 64`. A deployment with `m > 64` - has no measured KLL/top-k value. -- 530 config rows extrapolate beyond the largest measured N, and 56 rows - sit below the grid's smallest N; both are flagged. -- 720 rows=5 configs take 5/3 of the rows=3 cost (flagged). diff --git a/rqe-optimizer/data/autosketch-eval/table.json b/rqe-optimizer/data/autosketch-eval/table.json deleted file mode 100644 index 98f852bf..00000000 --- a/rqe-optimizer/data/autosketch-eval/table.json +++ /dev/null @@ -1,54796 +0,0 @@ -{ - "inputs": { - "out_1e9/saturation.csv": "0dd8448349f63317d7f4d385ce3063ab78d8daf3504015f580b7147cd22a3f22", - "out_1e9/saturation_curve.csv": "fb8fc62a16e137b831c20c16fe8d9b846e3fe80eb468451f08a727514d1e2bfd", - "out_grid_1e7_cost/saturation.csv": "569941ea162698963671d2a0ad40faeff53614d3cdb374de2a3b8aa3917ae674", - "out_grid_1e7_cost/saturation_curve.csv": "2d34a925c5bf8e4e72cab5932f63f8ec6e9cdf3e57b07f21aae2e5192144ca1a", - "out_merge_quantile/saturation_merge_curve.csv": "d4fb544bf14b06274f2558632faa766b5601ba4f942b48119f45c04aa39898f8", - 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"1": 0.03607387318040962, - "16": 0.03607387318040962, - "4": 0.03607387318040962, - "64": 0.03607387318040962 - }, - "asap_flag_by_m": { - "1": "", - "16": "", - "4": "", - "64": "" - }, - "autosketch_error": 0.03607387318040962, - "autosketch_flag": "", - "config": "alpha=0.05", - "cost_flag": "", - "insert_ns_per_item": 15.603900000000001, - "memory_bytes": 928.0, - "merge_us_per_fold": 0.6222222222222222, - "n_sat": 1000.0, - "not_saturated_in_grid": false, - "params": { - "alpha": 0.05 - }, - "query_phase_us": 2011.0000000000002, - "saturated": true - }, - { - "asap_error_by_m": { - "1": 0.014286585714478767, - "16": 0.014286585714478767, - "4": 0.014286585714478767, - "64": 0.014286585714478767 - }, - "asap_flag_by_m": { - "1": "", - "16": "", - "4": "", - "64": "" - }, - "autosketch_error": 0.014286585714478767, - "autosketch_flag": "", - "config": "alpha=0.02", - "cost_flag": "", - "insert_ns_per_item": 15.745933333333333, - "memory_bytes": 2312.0, - "merge_us_per_fold": 0.8222222222222222, - "n_sat": 1000.0, - "not_saturated_in_grid": false, - "params": { - "alpha": 0.02 - }, - "query_phase_us": 1969.3333333333337, - "saturated": true - }, - { - "asap_error_by_m": { - "1": 0.007347478509427665, - "16": 0.007347478509427665, - "4": 0.007347478509427665, - "64": 0.007347478509427665 - }, - "asap_flag_by_m": { - "1": "", - "16": "", - "4": "", - "64": "" - }, - "autosketch_error": 0.007347478509427665, - "autosketch_flag": "", - "config": "alpha=0.01", - "cost_flag": "", - "insert_ns_per_item": 15.611766666666668, - "memory_bytes": 4616.0, - "merge_us_per_fold": 1.266666666666667, - "n_sat": 1000.0, - "not_saturated_in_grid": false, - "params": { - "alpha": 0.01 - }, - "query_phase_us": 1987.333333333333, - "saturated": true - }, - { - "asap_error_by_m": { - "1": 0.0037676761127085397, - "16": 0.0037676761127085397, - "4": 0.0037676761127085397, - "64": 0.0037676761127085397 - }, - "asap_flag_by_m": { - "1": "", - "16": "", - "4": "", - "64": "" - }, - "autosketch_error": 0.0037676761127085397, - "autosketch_flag": "", - "config": "alpha=0.005", - "cost_flag": "", - "insert_ns_per_item": 15.6329, - "memory_bytes": 9224.0, - "merge_us_per_fold": 1.8444444444444446, - "n_sat": 1778.0, - "not_saturated_in_grid": false, - "params": { - "alpha": 0.005 - }, - "query_phase_us": 2017.3333333333333, - "saturated": true - } - ], - "error_metric": "mean_rank_err", - "family": "dd", - "flags": [ - "alpha 16.0911 beyond grid, used 3", - "light tail" - ], - "grid_K": "", - "grid_param": 3.0, - "higher_is_better": false, - "sketch": "dd", - "target": 0.01 - } - ], - "id": "google_2011/memory_p99/values/1h", - "instant": false, - "interval_secs": 300.0, - "kind": "values", - "label_set": { - "arrival_rate_per_sec": 679.6536111111111, - "groups": 1, - "keys_per_window": null - }, - "lookback_secs": 3600.0, - "max_N": 2446753.0, - "min_N": 1188516.0, - "query_id": "memory_p99", - "range": "1h" - } - ] - } - ] -} diff --git a/rqe-optimizer/examples/autosketch_vs_asap.rs b/rqe-optimizer/examples/autosketch_vs_asap.rs deleted file mode 100644 index 446bd39e..00000000 --- a/rqe-optimizer/examples/autosketch_vs_asap.rs +++ /dev/null @@ -1,1731 +0,0 @@ -//! AutoSketch vs. the ASAP planner (ProjectASAP/ASAPQuery#777, paper §6.3). -//! -//! One invocation evaluates one workload and writes one JSON file: -//! -//! ```text -//! autosketch_vs_asap traces --dataset alibaba_v2022 --saturation-dir DIR --out FILE [--runs 5] -//! autosketch_vs_asap synthetic --table FILE --target p95 --saturation-dir DIR --out FILE [--runs 1] -//! -//! Options for both: --weights cpu,fargate (a subset of the weight -//! settings), --slas-ms 100,1000 (version 2's SLA grid, in ms), --no-chosen -//! (drop per-RQE choices). The synthetic --target is the accuracy level; -//! `accNN` is NN% accuracy in each family's own metric (acc90, acc95, acc99; -//! `p95`, the name of the committed runs, is acc95): acc95 is the paper's -//! level and the one traces use; acc90 and acc99 are the target sweep. -//! ``` -//! -//! Every plan is priced by use (`usage::usage_cost`, sketch-bench -//! `docs/rqe_optimizer_cost_model.md`, "Cost by use and batch latency"): -//! `w_cpu · AUC(CPU) + w_mem · AUC(memory)`, CPU elastic, at each weight -//! setting of #777 §4 (CPU only; Fargate's per-vCPU and per-GB prices). A -//! batch's latency is its longest chain (the newest window's compaction, -//! then the query): the job placement's closed form with elastic CPU. Two -//! versions, in one output file: -//! -//! - **Version 1, no latency constraint** (`results`): each method's cheapest -//! plan, its latency reported, and ASAP's (with and without roll-ups) and -//! PerQuery's cost–latency frontiers (`bound_ms`). -//! - **Version 2, a batch latency SLA** (`sla_results`): at each SLA of the -//! grid, ASAP's (both) and PerQuery's cheapest plan whose batch latency is at most -//! the SLA (`milp::minimize_usage_cost` with `latency_bound_ms`, exact). -//! An SLA below a method's tightest feasible bound has no plan -//! (`infeasible`). AutoSketch ignores the SLA; its plan is recorded at -//! each SLA with `meets_sla`. -//! -//! Methods: -//! - **ASAP:** `milp::minimize_usage_cost` over every RQE jointly, with -//! sharing. -//! - **AutoSketch-Adapted:** `autosketch::plan`, one search and one dedicated -//! deployment per RQE, accuracy only; its plan does not depend on the -//! weights and is priced under each. -//! - **PerQuery-CostAware:** `milp::minimize_usage_cost` over each RQE's own -//! candidates (separate copies, so no sharing). -//! - **ASAP (no roll-ups)** (`asap-norollup`), an ablation: ASAP's MILP and -//! candidates, with each RQE allowed only deployments at its own grouping -//! or Hydra grids (not a roll-up). Every result counts `rolled_up_rqes`, -//! the RQEs served from a strictly finer non-Hydra deployment. -//! -//! `traces` reads `data/autosketch-eval/table.json` (alibaba_v2022 and -//! google_2011 are evaluated; boom is left out for now); `synthetic` reads one -//! table written by `export_autosketch_eval_table.py --synthetic`. A table -//! gives the workload: RQEs, and each stream's groups, rate and data shape -//! (the family's `grid_param`/`grid_K`, the worst case rounded to the grid). -//! Each stream (`traces`: dataset, query and kind; `synthetic`: the table's -//! `stream`) is one metric, which is where sharing comes from. A stream of a -//! metric with a label schema (the table's `schemas`) has the schema's labels, -//! and its RQEs name their `grouping`, so one stream carries several -//! groupings and a fine deployment can serve a coarse RQE (a roll-up). -//! Accuracy is read at the mean group's items per window, or, for an RQE with -//! a `covers_share` (`null`: every group), at its smallest and its largest -//! covered group's (`min_covered_share` and `max_covered_share` of the -//! stream's), the worse of the two: a per-group sketch's error need not fall -//! as N grows (sketch-bench#189), so either may be the hardest group. -//! -//! Costs and accuracy come from `--saturation-dir`, the same inputs the -//! planner reads (#174): the cost table `optimizer_cost/rqe_atomic_costs.json` -//! (one row per config, at the evaluation dataset's shape) and the saturation -//! curves. ASAP reads `SaturationCurves::accuracy` (the curve at the -//! lookback's items; KLL and top-k merged from `L/x` shards read the merge -//! curve, #158); AutoSketch reads `autosketch_accuracy` (one unmerged sketch -//! per query window, no saturation requirement). -//! -//! Hydra: ASAP and PerQuery plan with undeployable families allowed, so a -//! Hydra grid over a schema metric's full schema competes with per-group -//! sketches (sketch-bench `docs/rqe_optimizer_hydra.md`). Its cost rows and -//! accuracy (`hydra_saturation.csv` under the saturation dir) are measured -//! on the dataset shaped like the stream's metric ([`hydra_dataset`]), at -//! that full schema; without them no Hydra deployment is eligible. Hydra is -//! held to the worst case (max over covered groups, worst over N and seeds), -//! per-group sketches to seed-mean curves. AutoSketch allows undeployable -//! families too but skips Hydra itself (#159). - -use std::collections::{BTreeMap, BTreeSet}; -use std::time::Instant; - -use rqe_optimizer::analytical_cost_model::chain_ms; -use rqe_optimizer::autosketch; -use rqe_optimizer::candidates::{build_all_candidates, eligible_deployments_for, is_eligible}; -use rqe_optimizer::milp::minimize_usage_cost; -use rqe_optimizer::saturation::{AccuracySource, DataShape, SaturationCurves, COST_TABLE}; -use rqe_optimizer::usage::{usage_cost, PlanLoad}; -use rqe_optimizer::{ - accuracy_key, validate_facts, AccuracyDirection, AtomicCostEntry, AtomicCostTable, Capability, - Deployment, LabelSet, Mapping, MetricFacts, Millis, Raqe, WorkloadFacts, -}; -use serde_json::{json, Value}; - -const TRACES_TABLE: &str = "rqe-optimizer/data/autosketch-eval/table.json"; -/// Objective weights of #777 §4: CPU only, then AWS Fargate (us-east-1, -/// Linux/x86) $/vCPU-hour and $/GB-hour, so the objective reads in $/hour. -const WEIGHTS: [(&str, f64, f64); 2] = [("cpu", 1.0, 0.0), ("fargate", 0.0405, 0.00445)]; -/// A stream's scrape interval when its table gives no per-series rate: windows -/// and slides are whole seconds, and the stream's samples/s become its series -/// count. -const SCRAPE_MS: Millis = 1_000; -/// `ms` in seconds: an integer when whole, so second-grained plans read as -/// before, else fractional (an instant RQE's one-scrape window). -fn json_secs(ms: Millis) -> Value { - if ms.is_multiple_of(1000) { - json!(ms / 1000) - } else { - json!(ms as f64 / 1000.0) - } -} -const BYTES_PER_GIB: f64 = (1u64 << 30) as f64; -const SEED: u64 = 7; -/// AutoSketch's benchmark time per evaluated configuration, at the paper's -/// rate: 1–2 minutes each (NSDI '24, §5.2 and Exp#9); we take 1 minute. -const PAPER_SECS_PER_PROBE: f64 = 60.0; -/// Version 2's batch latency SLAs, ms (`--slas-ms` overrides). -const SLAS_MS: [f64; 5] = [100.0, 300.0, 1_000.0, 3_000.0, 10_000.0]; -/// Relative slack for comparing a latency with an SLA. Must equal -/// `milp::minimize_usage_cost`'s (private) slack for chains vs. its bound, so -/// "the tightest bound meets the SLA" and "the MILP has a plan" agree. -const LATENCY_SLACK: f64 = 1e-9; - -/// One workload: RQEs, one metric per stream, the cost entries each stream -/// may use, and the curves every accuracy is read from. -struct Workload { - name: String, - raqes: Vec, - facts: WorkloadFacts, - costs: BTreeMap>, - /// The metric (data) each RQE reads, by RQE id: AutoSketch benchmarks a - /// config once per metric. Tables without one read one metric per dataset. - metric_of: BTreeMap, - /// RQEs of streams with more keys than samples/s, left out. - excluded_high_cardinality: Vec, - /// The table's `covers_share` by RQE id, for RQEs that name one (`None`: - /// every group). - covers_share: BTreeMap>, - /// Facts for reading an RQE's accuracy at its smallest covered group, by - /// RQE id, for RQEs with a `covers_share` (`null` too): its stream's, - /// with the series scaled so the mean group gets that group's items. - covered_facts: BTreeMap, - /// The same, at its largest covered group. - largest_facts: BTreeMap, - curves: SaturationCurves, - notes: Vec, -} - -impl Workload { - /// The facts `r`'s accuracy is read with. - fn accuracy_facts(&self, r: &Raqe) -> &WorkloadFacts { - self.covered_facts.get(&r.id).unwrap_or(&self.facts) - } - - /// `read` at `r`'s accuracy facts, and for an RQE with a largest covered - /// group also at its, the worse in `sketch`'s direction; unknown if - /// either is. - fn worse_read( - &self, - r: &Raqe, - sketch: &str, - read: impl Fn(&WorkloadFacts) -> Option<(f64, T)>, - ) -> Option<(f64, T)> { - let smallest = read(self.accuracy_facts(r))?; - let Some(facts) = self.largest_facts.get(&r.id) else { - return Some(smallest); - }; - let largest = read(facts)?; - let largest_worse = match accuracy_key(sketch).1 { - AccuracyDirection::LowerIsBetter => largest.0 > smallest.0, - AccuracyDirection::HigherIsBetter => largest.0 < smallest.0, - }; - Some(if largest_worse { largest } else { smallest }) - } - - /// ASAP's accuracy: the planner's own lookup, and where it came from. - fn accuracy_with_source(&self, r: &Raqe, d: &Deployment) -> Option<(f64, AccuracySource)> { - self.worse_read(r, &d.config.sketch, |facts| { - self.curves.accuracy_with_source(r, d, facts) - }) - } - - /// ASAP's accuracy. - fn asap_accuracy(&self, r: &Raqe, d: &Deployment) -> Option { - self.accuracy_with_source(r, d).map(|(value, _)| value) - } - - /// AutoSketch's: one unmerged sketch per query window. - fn autosketch_accuracy(&self, r: &Raqe, c: &AtomicCostEntry) -> Option { - self.worse_read(r, &c.sketch, |facts| { - Some((self.curves.autosketch_accuracy(r, c, facts)?, ())) - }) - .map(|(value, _)| value) - } -} - -fn arg(args: &[String], name: &str) -> Option { - args.windows(2) - .find(|pair| pair[0] == name) - .map(|pair| pair[1].clone()) -} - -fn main() { - let args: Vec = std::env::args().collect(); - let out = arg(&args, "--out").expect("--out FILE is required"); - let runs: usize = arg(&args, "--runs").map_or(5, |r| r.parse().unwrap()); - let saturation_dir = arg(&args, "--saturation-dir").expect("--saturation-dir DIR is required"); - let workload = match args.get(1).map(String::as_str) { - Some("traces") => from_table( - TRACES_TABLE, - Some(&arg(&args, "--dataset").expect("--dataset")), - Some("p95".to_string()), - &saturation_dir, - ), - Some("synthetic") => from_table( - &arg(&args, "--table").expect("--table"), - None, - Some(arg(&args, "--target").expect("--target")), - &saturation_dir, - ), - _ => panic!("usage: autosketch_vs_asap (traces|synthetic) ... --out FILE"), - }; - let weights: Vec<(&str, f64, f64)> = WEIGHTS - .iter() - .copied() - .filter(|(name, ..)| { - arg(&args, "--weights").is_none_or(|list| list.split(',').any(|w| w == *name)) - }) - .collect(); - assert!( - !weights.is_empty(), - "--weights names none of {:?}", - WEIGHTS.map(|(name, ..)| name) - ); - let slas = arg(&args, "--slas-ms").map_or(SLAS_MS.to_vec(), |list| parse_slas(&list)); - let mut result = evaluate(&workload, runs, &weights, &slas); - if args.iter().any(|a| a == "--no-chosen") { - for key in ["results", "sla_results"] { - for r in result[key].as_array_mut().unwrap() { - r.as_object_mut().unwrap().remove("chosen"); - } - } - } - std::fs::write(&out, serde_json::to_string_pretty(&result).unwrap()).unwrap(); - eprintln!("wrote {out}"); -} - -// ---------------------------------------------------------------- workloads - -/// Per-capability accuracy target at accuracy level `accNN`: NN% accuracy -/// in each family's own metric (acc95 is the paper's level; `p95` names it in -/// the committed runs). Exact accumulators must have zero error. -fn strictness_target(level: &str, capability: Capability) -> f64 { - // (error bound, precision), written out so 95% is exactly 0.05. - let (error, precision) = match level { - "acc90" => (0.10, 0.90), - "acc95" | "p95" => (0.05, 0.95), - "acc99" => (0.01, 0.99), - _ => panic!("accuracy level {level}: one of acc90, acc95 (p95), acc99"), - }; - match capability { - // Error at most 1 − accuracy for rank, relative value and relative - // errors; precision at least the accuracy. - Capability::Quantile | Capability::Cardinality => error, - Capability::TopKByValue | Capability::TopKByCount => precision, - Capability::Sum - | Capability::Count - | Capability::Min - | Capability::Max - | Capability::RateOrIncrease => 0.0, - } -} - -/// The table's capability for an RQE. `traces` key queries are per-key sums -/// (`sum by (key)`), served by the cost table's exact accumulators, one per -/// key; the table lists only their sketch families, so their target falls -/// back to 0 (exact). -fn capability_of(r: &Value) -> Capability { - match (r["capability"].as_str(), r["kind"].as_str().unwrap()) { - (Some("sum"), _) | (Some("freq"), _) | (None, "keys") => Capability::Sum, - (Some("rate"), _) => Capability::RateOrIncrease, - // `topk(k, sum by (...) (...))`: ranked by summed value. - (Some("topk"), _) => Capability::TopKByValue, - (Some("quantile"), _) | (None, "values") => Capability::Quantile, - (Some("cardinality"), _) => Capability::Cardinality, - other => panic!("unknown capability {other:?}"), - } -} - -/// A top-k RQE's k, from its query id (`topk32_...`: the table names the -/// query's literal k there); `None` for other capabilities. -fn topk_k_of(r: &Value, capability: Capability) -> Option { - if !matches!( - capability, - Capability::TopKByValue | Capability::TopKByCount - ) { - return None; - } - let query = r["query_id"].as_str().unwrap(); - let digits: String = query - .strip_prefix("topk") - .unwrap_or_else(|| panic!("top-k query {query:?} names no k")) - .chars() - .take_while(char::is_ascii_digit) - .collect(); - Some( - digits - .parse() - .unwrap_or_else(|_| panic!("top-k query {query:?} names no k")), - ) -} - -/// The groups an RQE's deployment keeps one instance per. A `traces` key -/// query sums per key: one exact accumulator per key, so its groups are the -/// window's keys (the table's `groups` counts one sketch holding them all). -fn groups_of(r: &Value, capability: Capability) -> u64 { - let groups = r["label_set"]["groups"].as_u64().unwrap(); - if r["capability"].is_null() && capability == Capability::Sum { - let keys = r["label_set"]["keys_per_window"].as_f64().unwrap_or(1.0); - groups.max(keys.ceil() as u64) - } else { - groups - } -} - -/// The `hydra_saturation.csv` dataset measured with a schema metric's -/// labels, shares and values (`configs/datagen/hydra_*.yaml`): `http`'s -/// latency for quantiles, its user ids otherwise, and `flows`' sources. -/// `None` for any other metric, which no Hydra deployment serves. -fn hydra_dataset(metric: &str, capability: Capability) -> Option { - let dataset = match (metric.rsplit('/').next()?, capability) { - ("http", Capability::Quantile) => "hydra_http_latency", - ("http", _) => "hydra_http", - ("flows", _) => "hydra_flows", - _ => return None, - }; - Some(dataset.to_string()) -} - -fn label_set(names: &[&str]) -> LabelSet { - names.iter().map(|s| s.to_string()).collect() -} - -/// One workload of an evaluation table: `traces`'s `dataset`, or a -/// `synthetic` table's only workload. `target` sets every RQE's accuracy -/// target to that level (p95). -fn from_table( - path: &str, - dataset: Option<&str>, - target: Option, - saturation_dir: &str, -) -> Workload { - let table: Value = serde_json::from_str(&std::fs::read_to_string(path).unwrap()).unwrap(); - let dir = std::path::Path::new(saturation_dir); - let curves = SaturationCurves::load(dir) - .unwrap_or_else(|e| panic!("couldn't load saturation curves from {saturation_dir}: {e}")); - let cost_path = dir.join(COST_TABLE); - let cost_table: AtomicCostTable = - serde_json::from_str(&std::fs::read_to_string(&cost_path).unwrap_or_else(|e| { - panic!( - "couldn't read {} ({e}); run study_saturation.py --phase optimizer-cost", - cost_path.display() - ) - })) - .unwrap(); - let workload = table["workloads"] - .as_array() - .unwrap() - .iter() - .find(|w| dataset.is_none_or(|d| w["dataset"] == d)) - .unwrap_or_else(|| panic!("no workload {dataset:?} in {path}")); - let dataset = workload["dataset"].as_str().unwrap(); - let mut raqes = Vec::new(); - let mut facts = WorkloadFacts::new(); - // Queries one evaluation issues per instance, per stream (max over its RQEs). - let mut queries_per_stream: BTreeMap = BTreeMap::new(); - let mut metric_of = BTreeMap::new(); - let mut skipped_families = 0; - let mut stream_series: BTreeMap = BTreeMap::new(); - let mut covers_share = BTreeMap::new(); - let mut min_covered_share = BTreeMap::new(); - let mut max_covered_share = BTreeMap::new(); - for r in workload["rqes"].as_array().unwrap() { - let id = r["id"].as_str().unwrap().to_string(); - let metric = r["metric"].as_str().unwrap_or(dataset); - metric_of.insert(id.clone(), metric.to_string()); - let stream = match r["stream"].as_str() { - Some(stream) => format!("{dataset}/{stream}"), - None => format!( - "{}/{}/{}", - dataset, - r["query_id"].as_str().unwrap(), - r["kind"].as_str().unwrap() - ), - }; - let capability = capability_of(r); - let rate = r["label_set"]["arrival_rate_per_sec"].as_f64().unwrap(); - // A metric with a per-series rate (the synthetic gauge and counter) is - // scraped at it, so an instant RQE's one-scrape lookback holds one - // sample per series. Record metrics (http, flows) keep SCRAPE_MS. - let scrape_ms = workload["e2e"]["metrics"][metric]["samples_per_sec_per_series"] - .as_u64() - .map_or(SCRAPE_MS, |per_sec| { - assert!( - per_sec > 0 && 1_000 % per_sec == 0, - "{metric}: samples_per_sec_per_series {per_sec} must divide 1000" - ); - 1_000 / per_sec - }); - let groups = groups_of(r, capability); - // A schema metric's RQE names its grouping (its `groups` are that - // grouping's); otherwise the stream has one label `g`, the grouping. - let schema = &workload["schemas"][metric]; - let grouping: LabelSet = match r["grouping"].as_array() { - Some(labels) => labels - .iter() - .map(|l| l.as_str().unwrap().to_string()) - .collect(), - None if groups > 1 => label_set(&["g"]), - None => LabelSet::new(), - }; - // `null` covers every group, so its smallest is read too. - if let Some(share) = r.get("covers_share") { - covers_share.insert(id.clone(), share.as_f64()); - min_covered_share.insert(id.clone(), r["min_covered_share"].as_f64().unwrap()); - max_covered_share.insert(id.clone(), r["max_covered_share"].as_f64().unwrap()); - } - // One metric per stream, labels {g, x} (or the schema's and x), and - // the full label set has one series per sample per scrape interval. A stream's RQEs differ only in range and grouping, so - // take the largest per-RQE rate (conservative). Rates below `groups` - // samples/s are rounded up so every group has a series. - let series = (rate * scrape_ms as f64 / 1000.0).round() as u64; - let most = stream_series.entry(stream.clone()).or_default(); - *most = (*most).max(series); - let most = *most; - // A schema metric's full schema (its labels without `x`) and its - // groups: the product of every label's fan-out, as the generator's - // grouping_cardinality (the full set holds every child's parent). - let full_schema: Option<(LabelSet, u64)> = schema["labels"].as_array().map(|labels| { - let names = labels - .iter() - .map(|l| l["name"].as_str().unwrap().to_string()) - .collect(); - let groups = labels - .iter() - .map(|l| l["cardinality"].as_u64().unwrap()) - .product(); - (names, groups) - }); - let metric = facts.entry(stream.clone()).or_insert_with(|| { - let mut labels = full_schema - .as_ref() - .map_or_else(|| label_set(&["g"]), |(names, _)| names.clone()); - labels.insert("x".to_string()); - MetricFacts { - cardinality: [(labels.clone(), 0), (LabelSet::new(), 1)].into(), - labels, - scrape_interval_ms: scrape_ms, - value_range: None, - data_shape: BTreeMap::new(), - // A stream is one value of its metric, so one capability. - hydra_dataset: full_schema.as_ref().and_then(|(names, _)| { - Some((hydra_dataset(metric, capability)?, names.clone())) - }), - } - }); - // A stream's RQEs may see different group counts (keys per window - // grow with the range): take the largest (conservative). - let card = metric.cardinality.entry(grouping.clone()).or_default(); - *card = (*card).max(groups); - let total = metric.cardinality.get_mut(&metric.labels).unwrap(); - *total = (*total).max(series).max(groups); - // The full schema (a Hydra grid's, and its tracker's keys), capped at - // the stream's series so it doesn't make the stream high-cardinality. - if let Some((names, groups)) = full_schema { - let card = metric.cardinality.entry(names).or_default(); - *card = (*card).max(groups.min(most)); - } - // Queries one evaluation issues per instance (the table's q_r); tables - // written before it existed charge one query phase per evaluation. - let queries = r["queries_per_instance"].as_f64().unwrap_or(1.0); - let q = queries_per_stream.entry(stream.clone()).or_default(); - *q = q.max(queries); - - // The first family of the RQE's capability gives its target and the - // stream's data shape; the table rounds both to the grid. - let mut rqe_target = None; - for family in r["families"].as_array().unwrap() { - let sketch = family["sketch"].as_str().unwrap(); - if !capability.families().contains(&sketch) { - // e.g. `traces`' frequency sketches, or top-k for a sum query. - skipped_families += 1; - continue; - } - if rqe_target.is_none() { - rqe_target = Some(match target.as_deref() { - Some(level) => strictness_target(level, capability), - None => family["target"].as_f64().unwrap(), - }); - } - if let Some(shape) = data_shape(family) { - // Several RQEs of a stream: the harder side of each parameter. - metric - .data_shape - .entry(grouping.clone()) - .and_modify(|old| { - old.zipf_s = old.zipf_s.min(shape.zipf_s); - old.distinct_keys = old.distinct_keys.max(shape.distinct_keys); - old.tail_index = old.tail_index.min(shape.tail_index); - }) - .or_insert(shape); - } - } - let lookback_ms = (r["lookback_secs"].as_f64().unwrap() * 1000.0).round() as Millis; - let interval_ms = (r["interval_secs"].as_f64().unwrap() * 1000.0).round() as Millis; - // Otherwise no candidate window fits, and the RQE would be dropped as - // unservable rather than reported as a table error. - assert!( - lookback_ms.is_multiple_of(scrape_ms) && interval_ms.is_multiple_of(scrape_ms), - "{id}: lookback {lookback_ms} ms and interval {interval_ms} ms must be \ - multiples of {stream}'s {scrape_ms} ms scrape" - ); - raqes.push(Raqe { - id: id.clone(), - capability, - lookback_ms, - interval_ms, - metric: stream, - spatial_filter: String::new(), - grouping_labels: grouping, - // An RQE with no family of its capability is unservable; its - // target is never read. - accuracy_sla: rqe_target.unwrap_or(0.0), - latency_sla_ms: None, - topk_k: topk_k_of(r, capability), - accuracy_covers_share: r.get("covers_share").and_then(Value::as_f64), - }); - } - // Every stream may use every row; one evaluation runs q_r queries per - // instance. - let costs = queries_per_stream - .iter() - .map(|(stream, &queries)| { - let rows = cost_table - .iter() - .map(|row| AtomicCostEntry { - query_cpu_secs: queries * row.query_cpu_secs, - ..row.clone() - }) - .collect(); - (stream.clone(), rows) - }) - .collect(); - // A stream with more keys than samples per second can't give every key a - // series at one scrape a second, and rounding its rate up to its key count - // inflates every method's cost (Alibaba's CallGraph keys: up to 436x). - // Such streams are left out, and their RQEs listed. - let high_cardinality: BTreeSet = facts - .iter() - .filter(|(stream, m)| { - m.cardinality - .iter() - .any(|(labels, &card)| labels != &m.labels && card > stream_series[*stream]) - }) - .map(|(stream, _)| stream.clone()) - .collect(); - let excluded_high_cardinality: Vec = raqes - .iter() - .filter(|r| high_cardinality.contains(&r.metric)) - .map(|r| r.id.clone()) - .collect(); - raqes.retain(|r| !high_cardinality.contains(&r.metric)); - facts.retain(|stream, _| !high_cardinality.contains(stream)); - // Only the items per group read the series count: scaled by share·groups, - // the mean group gets the smallest (largest) covered group's items. - let scaled = |shares: &BTreeMap| { - raqes - .iter() - .filter_map(|r| { - let share = shares.get(&r.id)?; - let mut m = facts[&r.metric].clone(); - let groups = m.cardinality[&r.grouping_labels] as f64; - let series = m.cardinality.get_mut(&m.labels).unwrap(); - *series = (*series as f64 * share * groups).round() as u64; - Some((r.id.clone(), [(r.metric.clone(), m)].into())) - }) - .collect() - }; - let covered_facts = scaled(&min_covered_share); - let largest_facts = scaled(&max_covered_share); - let name = if dataset.starts_with("synthetic") { - format!("{dataset}/t{}", target.as_deref().unwrap_or("p95")) - } else { - format!("traces/{dataset}") - }; - Workload { - name, - raqes, - facts, - costs, - metric_of, - excluded_high_cardinality, - covers_share, - covered_facts, - largest_facts, - curves, - notes: vec![ - format!("{skipped_families} families skipped: they serve another capability"), - "one metric per stream: groups from the table, samples/s = max over the stream's RQEs, scraped once a second".into(), - format!("streams with more keys than samples/s, left out: {high_cardinality:?}"), - format!("costs: {COST_TABLE} under the saturation dir, one row per config"), - "data shape per stream: the families' grid_param/grid_K, the harder side over its RQEs".into(), - "query_cpu_secs = q_r queries per instance per evaluation".into(), - ], - } -} - -/// A table family's grid point as a curve key: Zipf θ and K, or the Pareto -/// tail index for quantiles. The unused fields are never read. -fn data_shape(family: &Value) -> Option { - let param = family["grid_param"].as_f64()?; - let keys = &family["grid_K"]; - match keys - .as_f64() - .or_else(|| keys.as_str().and_then(|k| k.parse().ok())) - { - Some(keys) => Some(DataShape { - zipf_s: param, - distinct_keys: keys, - tail_index: f64::INFINITY, - }), - None => Some(DataShape { - zipf_s: f64::INFINITY, - distinct_keys: 0.0, - tail_index: param, - }), - } -} - -// --------------------------------------------------------------- evaluation - -fn median(mut v: Vec) -> f64 { - v.sort_by(f64::total_cmp); - v[v.len() / 2] -} - -fn group_by_stream(raqes: &[Raqe]) -> BTreeMap<&str, Vec> { - let mut out: BTreeMap<&str, Vec> = BTreeMap::new(); - for r in raqes { - out.entry(r.metric.as_str()).or_default().push(r.clone()); - } - out -} - -/// ASAP candidates, built per stream from that stream's entries, with -/// undeployable families (Hydra) allowed. -fn candidates(w: &Workload, raqes: &[Raqe]) -> Vec { - group_by_stream(raqes) - .into_iter() - .flat_map(|(stream, group)| { - let costs = w.costs.get(stream).map_or(&[][..], Vec::as_slice); - build_all_candidates(&group, costs, &w.facts, true, &|r, d| w.asap_accuracy(r, d)) - }) - .collect() -} - -/// AutoSketch, searched per stream over that stream's entries. -fn autosketch_plan( - w: &Workload, - raqes: &[Raqe], -) -> Result> { - let mut deployments = Vec::new(); - let mut searches = Vec::new(); - let mut unservable = Vec::new(); - let mut order = Vec::new(); - for (stream, group) in group_by_stream(raqes) { - let costs = w.costs.get(stream).map_or(&[][..], Vec::as_slice); - let oracle = |r: &Raqe, c: &AtomicCostEntry| w.autosketch_accuracy(r, c); - // Undeployable families allowed, as for ASAP: AutoSketch skips - // Hydra itself (#159), so every method sees the same other families. - match autosketch::plan(&group, costs, SEED, true, oracle) { - Ok(plan) => { - deployments.extend(plan.deployments); - searches.extend(plan.searches); - order.extend(group.iter().map(|r| r.id.clone())); - } - Err(ids) => unservable.extend(ids), - } - } - if !unservable.is_empty() { - return Err(unservable); - } - // Back to the caller's RQE order. - let position: BTreeMap<&str, usize> = order - .iter() - .enumerate() - .map(|(i, id)| (id.as_str(), i)) - .collect(); - let mapping: Mapping = raqes.iter().map(|r| position[r.id.as_str()]).collect(); - Ok(autosketch::AutoSketchPlan { - deployments, - mapping, - searches, - }) -} - -/// Whether `d` serves `r` as a roll-up: a non-Hydra deployment at another -/// grouping (one that serves `r` is strictly finer). A Hydra grid answers any -/// subgrouping directly. -fn is_rollup(r: &Raqe, d: &Deployment) -> bool { - !d.properties().answers_any_subgrouping && d.grouping_labels != r.grouping_labels -} - -/// A plan priced by use (`usage::usage_cost`): its cost, CPU and memory by -/// part, its latency (the longest chain) and each RQE's choice. -fn summarize( - w: &Workload, - raqes: &[Raqe], - deployments: &[Deployment], - mapping: &Mapping, - w_cpu: f64, - w_mem: f64, -) -> Value { - let load = PlanLoad::new(raqes, deployments, mapping, &w.facts); - let cost = usage_cost(&load, w_cpu, w_mem); - let secs = |ms: Millis| ms as f64 / 1000.0; - let gib = |bytes: f64| bytes / BYTES_PER_GIB; - let ingest_cpu: f64 = load.deployments.iter().map(|d| d.ingest_cpu).sum(); - let compaction_cpu: f64 = load - .deployments - .iter() - .map(|d| d.compaction_secs / secs(d.slide_ms)) - .sum(); - let query_cpu: f64 = load - .queries - .iter() - .map(|q| q.work_secs / secs(q.interval_ms)) - .sum(); - let ingest_bytes: f64 = load.deployments.iter().map(|d| d.ingest_bytes).sum(); - let storage_bytes: f64 = load.deployments.iter().map(|d| d.storage_bytes).sum(); - let compaction_bytes: f64 = load - .deployments - .iter() - .map(|d| d.compaction_bytes * d.compaction_secs / secs(d.slide_ms)) - .sum(); - let query_bytes: f64 = load - .queries - .iter() - .map(|q| q.memory_bytes * q.work_secs / secs(q.interval_ms)) - .sum(); - let latencies: Vec = load.queries.iter().map(|q| q.chain_ms).collect(); - let active: BTreeSet = mapping.iter().copied().collect(); - let hydra_rqes = mapping - .iter() - .filter(|&&d| deployments[d].properties().answers_any_subgrouping) - .count(); - let rolled_up_rqes = raqes - .iter() - .zip(mapping) - .filter(|(r, &d)| is_rollup(r, &deployments[d])) - .count(); - let chosen: Vec = raqes - .iter() - .zip(mapping) - .zip(&latencies) - .map(|((r, &di), &latency)| { - let d = &deployments[di]; - let mut choice = json!({ - "rqe": r.id, - "sketch": d.config.sketch, - "params": d.config.sketch_config["params"], - "window_secs": json_secs(d.window_ms), - "slide_secs": json_secs(d.slide_ms), - "merged_shards": r.lookback_ms / d.window_ms, - "ingest_workers": rqe_optimizer::analytical_cost_model::ingest_workers(d, &w.facts), - "latency_ms": latency, - "deployment": di, - "asap_accuracy": w.asap_accuracy(r, d), - "accuracy_source": w - .accuracy_with_source(r, d) - .map(|(_, source)| format!("{source:?}")), - }); - // A roll-up or a Hydra grid: the deployment is grouped finer than - // the RQE. - if d.grouping_labels != r.grouping_labels { - choice["deployment_grouping"] = json!(d.grouping_labels); - } - if let Some(share) = w.covers_share.get(&r.id) { - choice["covers_share"] = json!(share); - } - choice - }) - .collect(); - let mut v = json!({ - "objective": cost.value, - "cpu": cost.cpu, - "gib": gib(cost.bytes), - "cpu_parts": {"ingest": ingest_cpu, "compaction": compaction_cpu, "query": query_cpu}, - "gib_parts": { - "ingest": gib(ingest_bytes), - "storage": gib(storage_bytes), - "compaction": gib(compaction_bytes), - "query": gib(query_bytes), - }, - "latency_ms": cost.latency_ms, - "median_latency_ms": (!latencies.is_empty()).then(|| median(latencies.clone())), - "active_deployments": active.len(), - "rolled_up_rqes": rolled_up_rqes, - "chosen": chosen, - }); - // Only workloads Hydra may serve report it, so others read as before. - if w.facts.values().any(|m| m.hydra_dataset.is_some()) { - v["hydra_rqes"] = json!(hydra_rqes); - } - v -} - -/// `--slas-ms`: comma-separated SLAs in ms, each positive and finite. -fn parse_slas(list: &str) -> Vec { - let slas: Vec = list - .split(',') - .map(|x| { - let sla: f64 = x - .trim() - .parse() - .unwrap_or_else(|_| panic!("--slas-ms: {x:?} is not a number")); - assert!( - sla.is_finite() && sla > 0.0, - "--slas-ms: {x:?} must be positive" - ); - sla - }) - .collect(); - assert!(!slas.is_empty(), "--slas-ms is empty"); - slas -} - -/// Whether a latency meets an SLA, with [`LATENCY_SLACK`]. A method has a plan -/// at an SLA iff its tightest feasible bound meets it. -fn meets_sla(latency_ms: f64, sla_ms: f64) -> bool { - latency_ms <= sla_ms * (1.0 + LATENCY_SLACK) -} - -/// Points on each method's cost–latency frontier: bounds log-spaced from the -/// tightest feasible one to the unbounded plan's latency. -const FRONTIER_POINTS: usize = 12; - -/// The tightest latency bound any plan over `candidates` can meet: the -/// largest, over RQEs, of each RQE's fastest chain among its (allowed) -/// eligible candidates. -fn tightest_bound_ms( - w: &Workload, - raqes: &[Raqe], - candidates: &[Deployment], - allowed: Option<&[Vec]>, -) -> f64 { - let accuracy = |r: &Raqe, d: &Deployment| w.asap_accuracy(r, d); - raqes - .iter() - .enumerate() - .map(|(i, r)| { - eligible_deployments_for(r, candidates, &w.facts, &accuracy) - .into_iter() - .filter(|d| allowed.is_none_or(|allowed| allowed[i].contains(d))) - .map(|d| chain_ms(r, &candidates[d], &w.facts)) - .fold(f64::INFINITY, f64::min) - }) - .fold(0.0, f64::max) -} - -/// `n` bounds log-spaced over `[lo, hi]`, plus `extra` when it falls inside. -fn frontier_bounds(lo: f64, hi: f64, n: usize, extra: f64) -> Vec { - let mut bounds: Vec = if hi > lo && lo > 0.0 { - (0..n) - .map(|k| lo * (hi / lo).powf(k as f64 / (n - 1) as f64)) - .collect() - } else { - vec![lo] - }; - if extra >= lo && extra < hi { - bounds.push(extra); - } - bounds.sort_by(f64::total_cmp); - bounds -} - -/// Every method's plan for `w`, priced by use at each weight setting, with -/// its latency (`docs/rqe_optimizer_cost_model.md`, "Cost by use and batch -/// latency"): version 1 (no latency constraint; frontier) in `results`, -/// version 2 (each SLA of `slas`) in `sla_results`. -fn evaluate(w: &Workload, runs: usize, weights: &[(&str, f64, f64)], slas: &[f64]) -> Value { - let asap_accuracy = |r: &Raqe, d: &Deployment| w.asap_accuracy(r, d); - // Drop RQEs that either method cannot serve, so both plan the same batch. - let all_candidates = candidates(w, &w.raqes); - let asap_unservable: Vec = w - .raqes - .iter() - .filter(|r| { - eligible_deployments_for(r, &all_candidates, &w.facts, &asap_accuracy).is_empty() - }) - .map(|r| r.id.clone()) - .collect(); - let autosketch_unservable = autosketch_plan(w, &w.raqes).err().unwrap_or_default(); - let dropped: BTreeSet = asap_unservable - .iter() - .chain(&autosketch_unservable) - .cloned() - .collect(); - let raqes: Vec = w - .raqes - .iter() - .filter(|r| !dropped.contains(&r.id)) - .cloned() - .collect(); - eprintln!( - "{}: {} RQEs, {} dropped", - w.name, - raqes.len(), - dropped.len() - ); - if let Err(problems) = validate_facts(&raqes, &w.facts) { - panic!("invalid facts: {problems:?}"); - } - for (r, facts) in raqes.iter().flat_map(|r| { - [&w.covered_facts, &w.largest_facts] - .into_iter() - .filter_map(move |f| Some((r, f.get(&r.id)?))) - }) { - if let Err(problems) = validate_facts(std::slice::from_ref(r), facts) { - panic!("invalid covered facts: {problems:?}"); - } - } - - // AutoSketch: one plan, independent of the weights. - let mut search_secs = Vec::new(); - let mut plan = None; - for _ in 0..runs { - let started = Instant::now(); - plan = Some(autosketch_plan(w, &raqes).expect("servable RQEs only")); - search_secs.push(started.elapsed().as_secs_f64()); - } - let plan = plan.unwrap(); - let probes: usize = plan.searches.iter().map(|s| s.probes.len()).sum(); - // The distinct probed (metric, config) pairs and the data shape each is - // benchmarked on: AutoSketch benchmarks a config once per metric, on that - // metric's data. scripts/autosketch_benchmark_time.py times them. - let mut probed_configs = BTreeMap::new(); - for s in &plan.searches { - let r = raqes.iter().find(|r| r.id == s.raqe_id).unwrap(); - let shape = w.facts[&r.metric].data_shape.get(&r.grouping_labels); - for &p in &s.probes { - let c = &w.costs[&r.metric][p]; - let metric = &w.metric_of[&r.id]; - probed_configs - .entry(format!( - "{metric}/{}/{}", - c.sketch, c.sketch_config["params"] - )) - .or_insert_with(|| { - json!({ - "metric": metric, - "sketch": c.sketch, - "params": c.sketch_config["params"], - "zipf_s": shape.map(|d| d.zipf_s), - "distinct_keys": shape.map(|d| d.distinct_keys), - "tail_index": shape.map(|d| d.tail_index), - }) - }); - } - } - let distinct_probes = probed_configs.len(); - let paper_secs = distinct_probes as f64 * PAPER_SECS_PER_PROBE; - - // ASAP's candidates, for the whole batch. - let mut build_secs = Vec::new(); - let mut asap_candidates = Vec::new(); - for _ in 0..runs { - let started = Instant::now(); - asap_candidates = candidates(w, &raqes); - build_secs.push(started.elapsed().as_secs_f64()); - } - // PerQuery's: each RQE's own candidates, as separate copies, so no two - // RQEs share a deployment. - let started = Instant::now(); - let mut perquery_candidates: Vec = Vec::new(); - let mut allowed: Vec> = Vec::new(); - for r in &raqes { - let own = candidates(w, std::slice::from_ref(r)); - allowed.push((perquery_candidates.len()..perquery_candidates.len() + own.len()).collect()); - perquery_candidates.extend(own); - } - let perquery_build_secs = started.elapsed().as_secs_f64(); - // ASAP without roll-ups: ASAP's candidates, each RQE kept off any finer - // non-Hydra deployment. - let norollup_allowed: Vec> = raqes - .iter() - .map(|r| { - (0..asap_candidates.len()) - .filter(|&d| !is_rollup(r, &asap_candidates[d])) - .collect() - }) - .collect(); - - // AutoSketch's plan ignores the weights and latency: one point. - let auto_latency_ms = usage_cost( - &PlanLoad::new(&raqes, &plan.deployments, &plan.mapping, &w.facts), - 1.0, - 0.0, - ) - .latency_ms; - let asap_tightest = tightest_bound_ms(w, &raqes, &asap_candidates, None); - let perquery_tightest = tightest_bound_ms(w, &raqes, &perquery_candidates, Some(&allowed)); - let norollup_tightest = tightest_bound_ms(w, &raqes, &asap_candidates, Some(&norollup_allowed)); - - let mut results = Vec::new(); - let mut sla_results = Vec::new(); - let mut sanity = Vec::new(); - for &(weight_name, w_cpu, w_mem) in weights { - let solve = - |candidates: &[Deployment], allowed: Option<&[Vec]>, bound: Option| { - let started = Instant::now(); - let solved = minimize_usage_cost( - &raqes, - candidates, - &w.facts, - w_cpu, - w_mem, - &asap_accuracy, - allowed, - bound, - ); - (solved, started.elapsed().as_secs_f64()) - }; - // ASAP unbounded: the cheapest plan, timed over `runs`. - let mut solve_secs = Vec::new(); - let mut solution = None; - for _ in 0..runs { - let (solved, secs) = solve(&asap_candidates, None, None); - solve_secs.push(secs); - solution = Some(solved); - } - let asap = match solution.unwrap() { - Ok(s) => { - let mut v = summarize(w, &raqes, &asap_candidates, &s.mapping, w_cpu, w_mem); - v["planning_secs"] = json!(median(build_secs.clone()) + median(solve_secs.clone())); - v["candidate_build_secs"] = json!(median(build_secs.clone())); - v["milp_solve_secs"] = json!(median(solve_secs.clone())); - v["candidates"] = json!(asap_candidates.len()); - v - } - Err(e) => json!({"error": e.to_string()}), - }; - let (norollup, norollup_solve_secs) = - solve(&asap_candidates, Some(&norollup_allowed), None); - let norollup = match norollup { - Ok(s) => { - let mut v = summarize(w, &raqes, &asap_candidates, &s.mapping, w_cpu, w_mem); - v["planning_secs"] = json!(median(build_secs.clone()) + norollup_solve_secs); - v["candidates"] = json!(asap_candidates.len()); - v - } - Err(e) => json!({"error": e.to_string()}), - }; - let (perquery, perquery_solve_secs) = solve(&perquery_candidates, Some(&allowed), None); - let mut perq = match perquery { - Ok(s) => summarize(w, &raqes, &perquery_candidates, &s.mapping, w_cpu, w_mem), - Err(e) => json!({"error": e.to_string()}), - }; - perq["planning_secs"] = json!(perquery_solve_secs + perquery_build_secs); - let mut auto = summarize(w, &raqes, &plan.deployments, &plan.mapping, w_cpu, w_mem); - auto["planning_secs"] = json!(median(search_secs.clone())); - auto["probes"] = json!(probes); - auto["benchmark_secs_paper_rate"] = json!(paper_secs); - - // ASAP costs no more than without roll-ups, which costs no more than - // PerQuery (whose own candidates are at each RQE's grouping or - // Hydra), nor than AutoSketch when every AutoSketch choice is one ASAP - // could make. - let cost = |v: &Value| v["objective"].as_f64(); - // `sanity` gets a violation of `cheaper <= dearer`, both solved. - let ordered = |sanity: &mut Vec, - at: Value, - cheaper: (&str, &Value), - dearer: (&str, &Value)| { - if let (Some(a), Some(b)) = (cost(cheaper.1), cost(dearer.1)) { - if a > b * (1.0 + 1e-6) { - let mut v = json!({"check": format!("{} <= {}", cheaper.0, dearer.0), "weights": weight_name, (cheaper.0): a, (dearer.0): b}); - v.as_object_mut() - .unwrap() - .extend(at.as_object().unwrap().clone()); - sanity.push(v); - } - } - }; - for (cheaper, dearer) in [ - (("asap", &asap), ("asap-norollup", &norollup)), - (("asap-norollup", &norollup), ("perquery", &perq)), - (("asap", &asap), ("perquery", &perq)), - ] { - ordered(&mut sanity, json!({}), cheaper, dearer); - } - if let (Some(a), Some(b)) = (cost(&asap), cost(&auto)) { - let not_eligible: Vec<&str> = raqes - .iter() - .zip(&plan.mapping) - .filter(|(r, &d)| !is_eligible(r, &plan.deployments[d], &w.facts, &asap_accuracy)) - .map(|(r, _)| r.id.as_str()) - .collect(); - if not_eligible.is_empty() && a > b * (1.0 + 1e-6) { - sanity.push(json!({"check": "asap <= autosketch", "weights": weight_name, "asap": a, "autosketch": b})); - } - } - if [&asap, &norollup, &perq] - .iter() - .any(|v| v.get("error").is_some()) - { - sanity.push(json!({"check": "every method solves", "weights": weight_name, "asap": asap.get("error"), "asap-norollup": norollup.get("error"), "perquery": perq.get("error")})); - } - - // The frontiers: the cheapest plan at most `L` slow, for a sweep of - // `L` from the tightest feasible bound to the unbounded latency, and - // at AutoSketch's latency. - for (method, candidates, allowed, tightest, unbounded) in [ - ("asap", &asap_candidates, None, asap_tightest, &asap), - ( - "asap-norollup", - &asap_candidates, - Some(&norollup_allowed[..]), - norollup_tightest, - &norollup, - ), - ( - "perquery", - &perquery_candidates, - Some(&allowed[..]), - perquery_tightest, - &perq, - ), - ] { - let Some(hi) = unbounded["latency_ms"].as_f64() else { - continue; - }; - let mut previous: Option = None; - for bound in frontier_bounds(tightest, hi, FRONTIER_POINTS, auto_latency_ms) { - let (solved, secs) = solve(candidates, allowed, Some(bound)); - let mut v = match solved { - Ok(s) => summarize(w, &raqes, candidates, &s.mapping, w_cpu, w_mem), - Err(e) => json!({"error": e.to_string()}), - }; - // A looser bound never costs more. - if let (Some(prev), Some(now)) = (previous, cost(&v)) { - if now > prev * (1.0 + 1e-6) { - sanity.push(json!({"check": "frontier is monotone", "method": method, "weights": weight_name, "bound_ms": bound})); - } - } - previous = cost(&v).or(previous); - v["method"] = json!(method); - v["weights"] = json!(weight_name); - v["bound_ms"] = json!(bound); - v["milp_solve_secs"] = json!(secs); - v["rqes"] = json!(raqes.len()); - results.push(v); - } - } - // Version 2: at each SLA, the cheapest plan whose batch latency meets - // it; AutoSketch's one plan, with whether it meets it. - for &sla in slas { - let mut at_sla = Vec::new(); - for (method, candidates, allowed, tightest) in [ - ("asap", &asap_candidates, None, asap_tightest), - ( - "asap-norollup", - &asap_candidates, - Some(&norollup_allowed[..]), - norollup_tightest, - ), - ( - "perquery", - &perquery_candidates, - Some(&allowed[..]), - perquery_tightest, - ), - ] { - let mut v = if meets_sla(tightest, sla) { - let (solved, secs) = solve(candidates, allowed, Some(sla)); - let mut v = match solved { - Ok(s) => summarize(w, &raqes, candidates, &s.mapping, w_cpu, w_mem), - Err(e) => json!({"error": e.to_string()}), - }; - v["milp_solve_secs"] = json!(secs); - v["infeasible"] = json!(false); - if v.get("error").is_some() - || v["latency_ms"].as_f64().is_some_and(|l| !meets_sla(l, sla)) - { - sanity.push(json!({"check": "plan meets the SLA", "method": method, "weights": weight_name, "sla_ms": sla, "latency_ms": v.get("latency_ms"), "error": v.get("error")})); - } - v - } else { - json!({"infeasible": true, "tightest_bound_ms": tightest}) - }; - v["method"] = json!(method); - at_sla.push(v); - } - for (i, j) in [(0, 1), (1, 2), (0, 2)] { - ordered( - &mut sanity, - json!({"sla_ms": sla}), - (at_sla[i]["method"].as_str().unwrap(), &at_sla[i]), - (at_sla[j]["method"].as_str().unwrap(), &at_sla[j]), - ); - } - let mut v = auto.clone(); - v["meets_sla"] = json!(meets_sla(auto_latency_ms, sla)); - v["method"] = json!("autosketch"); - at_sla.push(v); - for mut v in at_sla { - v["weights"] = json!(weight_name); - v["sla_ms"] = json!(sla); - v["rqes"] = json!(raqes.len()); - sla_results.push(v); - } - } - - for (method, value) in [ - ("asap", asap), - ("asap-norollup", norollup), - ("autosketch", auto), - ("perquery", perq), - ] { - let mut v = value; - v["method"] = json!(method); - v["weights"] = json!(weight_name); - v["bound_ms"] = Value::Null; - v["rqes"] = json!(raqes.len()); - results.push(v); - } - } - json!({ - "workload": w.name, - "rqes": raqes.len(), - "dropped_unservable": {"asap": asap_unservable, "autosketch": autosketch_unservable}, - "excluded_high_cardinality": w.excluded_high_cardinality, - "streams": raqes.iter().map(|r| &r.metric).collect::>().len(), - "runs": runs, - "notes": w.notes, - "cost_model": "w_cpu * AUC(CPU) + w_mem * AUC(memory), billed by use; latency = longest chain, reported", - "weights": weights.iter().map(|&(name, w_cpu, w_mem)| { - json!({"name": name, "w_cpu": w_cpu, "w_mem": w_mem}) - }).collect::>(), - "frontier": { - "points": FRONTIER_POINTS, - "asap_tightest_bound_ms": asap_tightest, - "perquery_tightest_bound_ms": perquery_tightest, - "asap_norollup_tightest_bound_ms": norollup_tightest, - "autosketch_latency_ms": auto_latency_ms, - }, - "autosketch": { - "probes": probes, - "search_secs": median(search_secs), - "distinct_probes": distinct_probes, - "probed_configs": probed_configs.values().collect::>(), - "benchmark_secs_paper_rate": paper_secs, - }, - "sla_grid_ms": slas, - "sanity_violations": sanity, - "results": results, - "sla_results": sla_results, - }) -} - -#[cfg(test)] -mod tests { - use super::*; - - #[test] - fn a_trace_key_query_keeps_one_accumulator_per_key() { - let key_query = - json!({"kind": "keys", "label_set": {"groups": 1, "keys_per_window": 26019.0}}); - assert_eq!(groups_of(&key_query, capability_of(&key_query)), 26019); - let quantile = - json!({"kind": "values", "label_set": {"groups": 1, "keys_per_window": 5.0}}); - assert_eq!(groups_of(&quantile, capability_of(&quantile)), 1); - let synthetic = json!({"capability": "sum", "kind": "keys", "label_set": {"groups": 10, "keys_per_window": 1e4}}); - assert_eq!(groups_of(&synthetic, Capability::Sum), 10); - } - - #[test] - fn topk_k_comes_from_the_query_id() { - let r = json!({"query_id": "topk32_sum_by_label0_sum_over_time"}); - assert_eq!(topk_k_of(&r, Capability::TopKByValue), Some(32)); - let r = json!({"query_id": "topk100_x"}); - assert_eq!(topk_k_of(&r, Capability::TopKByValue), Some(100)); - assert_eq!(topk_k_of(&r, Capability::Quantile), None); - } - - #[test] - fn slas_parse_and_reject_nonsense() { - assert_eq!(parse_slas("100, 1000,2.5"), vec![100.0, 1000.0, 2.5]); - for bad in ["", "0", "-5", "inf", "fast"] { - assert!( - std::panic::catch_unwind(|| parse_slas(bad)).is_err(), - "{bad:?}" - ); - } - } - - #[test] - fn a_method_has_a_plan_at_an_sla_iff_its_tightest_bound_meets_it() { - assert!(meets_sla(100.0, 100.0)); - // Float residue in summing µs-scale work is not a miss. - assert!(meets_sla(100.0 * (1.0 + 1e-12), 100.0)); - assert!(!meets_sla(100.1, 100.0)); - } - - /// A saturation dir with the committed cost table and inline HLL curves - /// (each lg_k, uniform over 1e6 keys), so the test runs without the - /// study's (gitignored) curves. `tag` keeps parallel tests' dirs apart. - fn hll_saturation_dir(tag: &str) -> std::path::PathBuf { - let dir = std::env::temp_dir().join(format!("hll-saturation-{tag}-{}", std::process::id())); - let costs = concat!( - env!("CARGO_MANIFEST_DIR"), - "/results/autosketch-vs-asap-inputs/saturation/optimizer_cost/rqe_atomic_costs.json" - ); - std::fs::create_dir_all(dir.join("optimizer_cost")).unwrap(); - std::fs::copy(costs, dir.join(COST_TABLE)).unwrap(); - for run in ["out_grid_1e7_cost", "out_1e9"] { - let mut summary = "family,sketch,config,dist,param,cardinality,n_sat,final_error,\ - error_metric,insert_cpu_secs,merge_cpu_secs,query_cpu_secs,memory_bytes\n" - .to_string(); - let mut curve = - "family,sketch,config,dist,param,cardinality,n,seed_mean_error,seed_se\n" - .to_string(); - for lg_k in [12, 14, 16].iter().filter(|_| run == "out_grid_1e7_cost") { - let point = format!("cardinality,hll,lg_k={lg_k},zipf,0.0,1000000"); - summary += &format!("{point},1000,0.01,relative_error,,,,\n"); - for n in ["1000", "100000", "10000000"] { - curve += &format!("{point},{n},0.01,0.0\n"); - } - } - std::fs::create_dir_all(dir.join(run)).unwrap(); - std::fs::write(dir.join(run).join("saturation.csv"), summary).unwrap(); - std::fs::write(dir.join(run).join("saturation_curve.csv"), curve).unwrap(); - } - dir - } - - /// An HLL RQE on `http`'s user ids, grouped by `grouping`. - fn http_rqe(grouping: &[&str], groups: u64, covers: Option, smallest: f64) -> Value { - json!({ - "id": format!("t/{}", grouping.join(",")), "query_id": "q", "kind": "keys", - "capability": "cardinality", "metric": "http", "stream": "http/user_id", - "grouping": grouping, "covers_share": covers, "min_covered_share": smallest, - "max_covered_share": 0.5, - "lookback_secs": 300, "interval_secs": 60, "queries_per_instance": 1, - "label_set": {"groups": groups, "arrival_rate_per_sec": 2e6}, - "families": [{"family": "hll", "sketch": "hll", "target": 0.02, - "grid_param": 0.0, "grid_K": 1e6}], - }) - } - - /// A workload of `rqes` on the `http` schema, on [`hll_saturation_dir`]. - fn http_workload(tag: &str, rqes: Vec) -> Workload { - let table = json!({"workloads": [{ - "dataset": "synthetic/test", - "schemas": {"http": http_schema()}, - "rqes": rqes, - }]}); - let inputs = hll_saturation_dir(tag); - let path = inputs.join("table.json"); - std::fs::write(&path, table.to_string()).unwrap(); - let w = from_table( - path.to_str().unwrap(), - None, - Some("p95".into()), - inputs.to_str().unwrap(), - ); - std::fs::remove_dir_all(&inputs).unwrap(); - w - } - - /// The unbounded plan of `method` at the first weight setting. - fn unbounded<'a>(result: &'a Value, method: &str) -> &'a Value { - result["results"] - .as_array() - .unwrap() - .iter() - .find(|r| r["method"] == method && r["bound_ms"].is_null()) - .unwrap() - } - - #[test] - fn a_schema_stream_carries_every_grouping_and_rolls_up() { - let w = http_workload( - "schema", - vec![ - http_rqe(&["region"], 4, Some(0.05), 0.05), - http_rqe(&["region", "service"], 100, None, 0.001), - ], - ); - let facts = &w.facts["synthetic/test/http/user_id"]; - assert_eq!( - facts.labels, - label_set(&["region", "service", "endpoint", "status", "x"]) - ); - assert_eq!(facts.cardinality[&label_set(&["region"])], 4); - assert_eq!(facts.cardinality[&label_set(&["region", "service"])], 100); - assert_eq!(facts.cardinality[&facts.labels], 2_000_000); - // Hydra reads `http`'s user ids over its full schema, at each RQE's - // coverage. - let (dataset, schema) = facts.hydra_dataset.as_ref().unwrap(); - assert_eq!(dataset, "hydra_http"); - assert_eq!( - schema, - &label_set(&["region", "service", "endpoint", "status"]) - ); - assert_eq!(facts.cardinality[schema], 4 * 25 * 25 * 4); - let covers: Vec<_> = w.raqes.iter().map(|r| r.accuracy_covers_share).collect(); - assert_eq!(covers, [Some(0.05), None]); - // Accuracy is read at the smallest covered group's items: 5% of the - // stream's, so 4 · 5% of the series for {region}'s mean group. - let covered = &w.covered_facts["t/region"]["synthetic/test/http/user_id"]; - assert_eq!(covered.cardinality[&covered.labels], 400_000); - // `null` covers every group: 100 · 0.1% of the series. - let every = &w.covered_facts["t/region,service"]["synthetic/test/http/user_id"]; - assert_eq!(every.cardinality[&every.labels], 200_000); - // One {region, service} deployment serves both RQEs. - let result = evaluate(&w, 1, &WEIGHTS[..1], &[1e4]); - let asap = unbounded(&result, "asap"); - assert_eq!(asap["active_deployments"], 1); - let coarse = &asap["chosen"][0]; - assert_eq!(coarse["rqe"], "t/region"); - assert_eq!(coarse["deployment_grouping"], json!(["region", "service"])); - assert_eq!(coarse["covers_share"], 0.05); - // No hydra_saturation.csv: Hydra serves nothing, and says so. - assert_eq!(asap["hydra_rqes"], 0); - } - - /// Without roll-ups, the coarse RQE needs its own deployment, which costs - /// more than reading the fine one; every method counts its roll-ups. - #[test] - fn forbidding_roll_ups_costs_more_only_where_one_applies() { - let cost = |v: &Value| v["objective"].as_f64().unwrap(); - let w = http_workload( - "rollup", - vec![ - http_rqe(&["region"], 4, Some(0.05), 0.05), - http_rqe(&["region", "service"], 100, None, 0.001), - ], - ); - let result = evaluate(&w, 1, &WEIGHTS[..1], &[1e4]); - assert_eq!(result["sanity_violations"], json!([])); - let [asap, norollup, perquery] = - ["asap", "asap-norollup", "perquery"].map(|m| unbounded(&result, m)); - assert_eq!(asap["rolled_up_rqes"], 1); - assert_eq!(norollup["rolled_up_rqes"], 0); - assert_eq!(perquery["rolled_up_rqes"], 0); - assert_eq!(norollup["active_deployments"], 2); - assert!(cost(asap) < cost(norollup), "{asap} vs {norollup}"); - assert!(cost(norollup) <= cost(perquery) * (1.0 + 1e-6)); - // The ablation has a frontier and a plan at each SLA, like ASAP. - let norollup_rows = |key: &str| { - result[key] - .as_array() - .unwrap() - .iter() - .filter(|r| r["method"] == "asap-norollup") - .count() - }; - assert!(norollup_rows("results") > 1); - assert_eq!(norollup_rows("sla_results"), 1); - - // {region} and {service}: neither serves the other, so the two agree. - let w = http_workload( - "no-rollup", - vec![ - http_rqe(&["region"], 4, Some(0.05), 0.05), - http_rqe(&["service"], 25, Some(0.05), 0.01), - ], - ); - let result = evaluate(&w, 1, &WEIGHTS[..1], &[1e4]); - assert_eq!(result["sanity_violations"], json!([])); - let [asap, norollup] = ["asap", "asap-norollup"].map(|m| unbounded(&result, m)); - assert_eq!(asap["rolled_up_rqes"], 0); - assert_eq!(cost(asap), cost(norollup)); - } - - /// The generator's `http` schema: endpoint fans out under service. - fn http_schema() -> Value { - json!({"labels": [ - {"name": "region", "cardinality": 4}, - {"name": "service", "cardinality": 25}, - {"name": "endpoint", "cardinality": 25, "child_of": "service"}, - {"name": "status", "cardinality": 4}, - ]}) - } - - /// `flows`' distinct sources and `http`'s p99 latency each get a Hydra - /// grid over their full schema, from the cost rows measured on their own - /// dataset. `flows`' 3e6 schema groups exceed its 1e6 series, so its - /// cardinality is capped there and the stream kept. - #[test] - fn flows_and_http_latency_get_a_full_schema_hydra_grid() { - use rqe_optimizer::candidates::build_all_candidates_unpruned; - let rqe = |id: &str, - metric: &str, - value: &str, - capability: &str, - grouping: &[&str], - groups: u64, - rate: f64| { - let (sketch, target) = if capability == "quantile" { - ("kll-percall", 0.05) - } else { - ("hll", 0.02) - }; - json!({ - "id": id, "query_id": "q", "kind": "keys", "capability": capability, - "metric": metric, "stream": format!("{metric}/{value}"), "grouping": grouping, - "covers_share": null, "min_covered_share": 0.01, "max_covered_share": 0.5, - "lookback_secs": 300, "interval_secs": 60, "queries_per_instance": 1, - "label_set": {"groups": groups, "arrival_rate_per_sec": rate}, - "families": [{"sketch": sketch, "target": target}], - }) - }; - let flows = json!({"labels": [ - {"name": "dst_subnet", "cardinality": 1000}, - {"name": "dst_port", "cardinality": 1000}, - {"name": "proto", "cardinality": 3}, - ]}); - let table = json!({"workloads": [{ - "dataset": "synthetic/test", - "schemas": {"flows": flows, "http": http_schema()}, - "rqes": [ - rqe("t11", "flows", "src_ip", "cardinality", &["dst_subnet"], 1000, 1e6), - rqe("t16", "http", "latency", "quantile", &["service"], 25, 2e6), - ], - }]}); - let path = std::env::temp_dir().join(format!("hydra-table-{}.json", std::process::id())); - std::fs::write(&path, table.to_string()).unwrap(); - // The committed cost table plus a Hydra row per (variant, dataset), - // one measured on `http`'s user ids, which `flows` must not use. - let inputs = hll_saturation_dir("hydra"); - let mut costs: AtomicCostTable = - serde_json::from_str(&std::fs::read_to_string(inputs.join(COST_TABLE)).unwrap()) - .unwrap(); - let hydra = |from: &str, sketch: &str, dataset: &str| { - let mut row = costs.iter().find(|c| c.sketch == from).unwrap().clone(); - row.sketch = sketch.into(); - row.measured_at.dataset = Some(dataset.into()); - row - }; - let rows = [ - hydra("hll", "hydra-hll", "hydra_flows"), - hydra("hll", "hydra-univmon-cardinality", "hydra_http"), - hydra("kll-percall", "hydra-kll", "hydra_http_latency"), - ]; - costs.extend(rows); - std::fs::write( - inputs.join(COST_TABLE), - serde_json::to_string(&costs).unwrap(), - ) - .unwrap(); - let w = from_table(path.to_str().unwrap(), None, None, inputs.to_str().unwrap()); - std::fs::remove_file(&path).unwrap(); - std::fs::remove_dir_all(&inputs).unwrap(); - assert!(w.excluded_high_cardinality.is_empty()); - let flows_facts = &w.facts["synthetic/test/flows/src_ip"]; - let full = label_set(&["dst_subnet", "dst_port", "proto"]); - assert_eq!(flows_facts.cardinality[&full], 1_000_000); - let hydra: Vec = group_by_stream(&w.raqes) - .into_iter() - .flat_map(|(stream, group)| { - build_all_candidates_unpruned(&group, &w.costs[stream], &w.facts, true) - }) - .filter(|d| d.properties().answers_any_subgrouping) - .collect(); - // A grid finer than its RQE is not a roll-up; a per-group sketch there - // would be. - let t11 = &w.raqes[0]; - let grid = hydra.iter().find(|d| d.metric == t11.metric).unwrap(); - assert!(!is_rollup(t11, grid)); - let mut per_group = grid.clone(); - per_group.config.sketch = "hll".into(); - assert!(is_rollup(t11, &per_group)); - let grids: BTreeSet<(String, LabelSet)> = hydra - .into_iter() - .map(|d| (d.config.sketch, d.grouping_labels)) - .collect(); - assert_eq!( - grids, - BTreeSet::from([ - ("hydra-hll".to_string(), full), - ( - "hydra-kll".to_string(), - label_set(&["region", "service", "endpoint", "status"]) - ), - ]) - ); - } - - #[test] - fn hydra_reads_the_dataset_shaped_like_the_metric() { - let at = |metric| hydra_dataset(metric, Capability::Cardinality); - assert_eq!(at("http").as_deref(), Some("hydra_http")); - assert_eq!(at("data_1/http").as_deref(), Some("hydra_http")); - assert_eq!(at("flows").as_deref(), Some("hydra_flows")); - assert_eq!( - hydra_dataset("http", Capability::Quantile).as_deref(), - Some("hydra_http_latency") - ); - assert_eq!(at("data"), None); - } - - /// `r`'s HLL accuracy (ASAP's, AutoSketch's) and the reads at its - /// smallest and largest covered groups, on curves with `errors` at N = - /// 1e3 .. 1e7; `r` covers groups of 1% and 50% of a 1000 samples/s - /// stream (3e3 and 1.5e5 items per 5m window, read at the worse of the - /// bracketing N). A classic RQE on another - /// stream reads the mean group alone. - fn covered_reads(errors: [f64; 5]) -> [Option; 4] { - let inputs = hll_saturation_dir(&format!("{errors:?}")); - let mut curve = - "family,sketch,config,dist,param,cardinality,n,seed_mean_error,seed_se\n".to_string(); - for lg_k in [12, 14, 16] { - for (n, error) in ["1e3", "1e4", "1e5", "1e6", "1e7"].iter().zip(errors) { - curve += &format!("cardinality,hll,lg_k={lg_k},zipf,0.0,1000000,{n},{error},0.0\n"); - } - } - let curves = inputs - .join("out_grid_1e7_cost") - .join("saturation_curve.csv"); - std::fs::write(curves, curve).unwrap(); - let rqe = |id: &str, stream: &str, extra: Value| { - let mut r = json!({ - "id": id, "query_id": "q", "kind": "keys", "capability": "cardinality", - "stream": stream, "lookback_secs": 300, "interval_secs": 60, - "queries_per_instance": 1, - "label_set": {"groups": 4, "arrival_rate_per_sec": 1000.0}, - "families": [{"family": "hll", "sketch": "hll", "target": 0.02, - "grid_param": 0.0, "grid_K": 1e6}], - }); - r.as_object_mut() - .unwrap() - .extend(extra.as_object().unwrap().clone()); - r - }; - let table = json!({"workloads": [{ - "dataset": "synthetic/test", - "schemas": {"http": {"labels": [{"name": "region", "cardinality": 4}]}}, - "rqes": [ - rqe("covered", "http/user_id", json!({ - "metric": "http", "grouping": ["region"], "covers_share": 0.01, - "min_covered_share": 0.01, "max_covered_share": 0.5})), - rqe("classic", "classic/user_id", json!({})), - ], - }]}); - let path = inputs.join("table.json"); - std::fs::write(&path, table.to_string()).unwrap(); - let w = from_table( - path.to_str().unwrap(), - None, - Some("p95".into()), - inputs.to_str().unwrap(), - ); - std::fs::remove_dir_all(&inputs).unwrap(); - let [covered, classic] = [&w.raqes[0], &w.raqes[1]]; - let hll = |r: &Raqe| { - let costs = &w.costs[&r.metric]; - rqe_optimizer::candidates::build_all_candidates_unpruned( - std::slice::from_ref(r), - costs, - &w.facts, - false, - ) - .into_iter() - .find(|d| d.config.sketch == "hll") - .unwrap() - }; - // A classic RQE: the mean group's read, as before. - let d = hll(classic); - assert!(!w.largest_facts.contains_key(&classic.id)); - assert_eq!( - w.asap_accuracy(classic, &d), - w.curves.accuracy(classic, &d, &w.facts) - ); - assert_eq!( - w.autosketch_accuracy(classic, &d.config), - w.curves.autosketch_accuracy(classic, &d.config, &w.facts) - ); - let d = hll(covered); - let autosketch = w.autosketch_accuracy(covered, &d.config); - assert_eq!(autosketch, w.asap_accuracy(covered, &d)); - [ - w.asap_accuracy(covered, &d), - autosketch, - w.curves.accuracy(covered, &d, &w.covered_facts["covered"]), - w.curves.accuracy(covered, &d, &w.largest_facts["covered"]), - ] - } - - #[test] - fn a_covered_rqe_is_held_to_the_worse_of_its_smallest_and_largest_group() { - // Relative error: a 3% target accepts at most 0.03. - let at = |target: f64, error: Option| error.unwrap() <= target; - // Error grows with N (#189): the largest group rejects what the - // smallest accepts. - let [asap, autosketch, smallest, largest] = covered_reads([0.01, 0.01, 0.05, 0.05, 0.05]); - assert!(at(0.03, smallest) && !at(0.03, largest)); - assert_eq!((asap, autosketch), (largest, largest)); - assert!(!at(0.03, asap)); - // Error falls with N: the smallest group rejects. - let [asap, autosketch, smallest, largest] = covered_reads([0.05, 0.05, 0.01, 0.01, 0.01]); - assert!(!at(0.03, smallest) && at(0.03, largest)); - assert_eq!((asap, autosketch), (smallest, smallest)); - } - - #[test] - fn each_level_in_each_familys_metric() { - assert_eq!(strictness_target("p95", Capability::Sum), 0.0); - assert_eq!(strictness_target("p95", Capability::RateOrIncrease), 0.0); - assert_eq!(strictness_target("p95", Capability::Quantile), 0.05); - assert_eq!(strictness_target("p95", Capability::TopKByValue), 0.95); - assert_eq!(strictness_target("acc95", Capability::Quantile), 0.05); - assert_eq!(strictness_target("acc90", Capability::Cardinality), 0.10); - assert_eq!(strictness_target("acc99", Capability::Quantile), 0.01); - assert_eq!(strictness_target("acc99", Capability::TopKByCount), 0.99); - assert_eq!(strictness_target("acc99", Capability::Max), 0.0); - } -} diff --git a/rqe-optimizer/examples/small_problem.rs b/rqe-optimizer/examples/small_problem.rs deleted file mode 100644 index dc2032e1..00000000 --- a/rqe-optimizer/examples/small_problem.rs +++ /dev/null @@ -1,603 +0,0 @@ -//! Toy instance, solved end to end: builds candidates, brute-forces -//! every feasible mapping, scores each one, then reports the -//! Pareto-optimal subset. Picking one mapping *off* the Pareto front is still -//! the parked scalarization question (problem statement, "Open for later"). -//! -//! Cost data is real, not fabricated: `scripts/study_saturation.py --phase -//! optimizer-cost` measures it via `sketch-bench`/`aqpbm-cli` and writes -//! [`COST_TABLE`] under the saturation directory, an `AtomicCostTable` -//! loaded here verbatim -- no translation layer, since -//! `rqe_optimizer::Deployment` embeds `AtomicCostEntry` directly. -//! -//! Every other number this example uses (RAQE definitions, metric labels, -//! cardinalities and scrape intervals) lives in the two tables below -//! (`facts`, `raqes`) and nowhere else in this file. -//! -//! Sketch accuracy comes from the saturation study's curves, not the cost -//! table (#156). Pass `--saturation-dir DIR` (required), the directory -//! holding `out_grid_1e7_cost/`, `out_1e9/` and the cost table from -//! `scripts/study_saturation.py` (#174). -//! -//! Do not run this example -//! with no mode flag on a large workload: eager mode retains every feasible -//! mapping and can exhaust memory. Use `--milp`, `--candidates-only`, or -//! `--sample-mappings N` instead. Add -//! `--candidates-only` to inspect candidate pruning safely, without starting -//! mapping enumeration. Streaming mode logs progress every one million -//! mappings by default; pass `--progress-every N` to change that interval or -//! `--print-first N` to display example mappings. `--milp` minimizes -//! `w_cpu · CPU + w_mem · memory GiB` without enumerating mappings; set the -//! weights with `--w-cpu X --w-mem Y` (default 1 and 0). Repeat -//! `--latency-sla RAQE_ID=MS` to impose MILP latency SLAs. -//! `--sample-mappings N` prints N feasible mappings and exits. -//! `--allow-undeployable-families` also plans with measured families -//! ASAPQuery can't deploy (dd, univmon-*, countsketch-heap). - -use std::collections::BTreeSet; -use std::time::Instant; - -use rqe_optimizer::analytical_cost_model::{score, PlanCost}; -use rqe_optimizer::candidates::{ - build_all_candidates, build_all_candidates_unpruned, eligible_deployments_for, -}; -use rqe_optimizer::enumerate::{brute_force, for_each_mapping, for_each_mapping_while, unservable}; -use rqe_optimizer::milp::{minimize, Objective}; -use rqe_optimizer::pareto::{pareto_front, ParetoFront}; -use rqe_optimizer::saturation::{DataShape, SaturationCurves, COST_TABLE}; -use rqe_optimizer::{ - validate_facts, AtomicCostTable, Capability, Deployment, LabelSet, MetricFacts, Raqe, - WorkloadFacts, -}; - -const REQUESTS: &str = "http_requests_total"; -const DURATION: &str = "http_request_duration_seconds"; - -fn label_set(names: &[&str]) -> LabelSet { - names.iter().map(|s| s.to_string()).collect() -} - -/// The directory after `flag`; panics with `what` when it's absent. -fn required_dir(flag: &str, what: &str) -> String { - let args: Vec<_> = std::env::args().collect(); - args.iter() - .position(|arg| arg == flag) - .map(|index| { - args.get(index + 1) - .unwrap_or_else(|| panic!("{flag} requires a directory")) - .clone() - }) - .unwrap_or_else(|| panic!("{flag} DIR is required ({what})")) -} - -fn saturation_dir() -> String { - required_dir( - "--saturation-dir", - "holding out_grid_1e7_cost/, out_1e9/ and the cost table from scripts/study_saturation.py", - ) -} - -fn load_saturation_curves() -> SaturationCurves { - SaturationCurves::load(std::path::Path::new(&saturation_dir())) - .unwrap_or_else(|e| panic!("couldn't load saturation curves: {e}")) -} - -fn load_cost_table() -> AtomicCostTable { - let path = std::path::Path::new(&saturation_dir()).join(COST_TABLE); - let raw = std::fs::read_to_string(&path).unwrap_or_else(|e| { - panic!( - "couldn't read {} ({e}) -- run \ - `scripts/study_saturation.py --phase optimizer-cost --out DIR/optimizer_cost` \ - first to generate it", - path.display() - ) - }); - serde_json::from_str(&raw) - .unwrap_or_else(|e| panic!("{} isn't a valid AtomicCostTable: {e}", path.display())) -} - -/// Both metrics: 5 services × 10 endpoints × 600 pods = 30,000 series -/// scraped every 15 s, so `λ` = 2,000 samples/sec each. Durations range from -/// 1 ms to 60 s, which sizes DDSketch. The data -/// shapes stand in for fits over a real dataset; the worst group's keys -/// exceed the average group's pods. -fn facts() -> WorkloadFacts { - let http_metric_facts = |value_range| MetricFacts { - labels: label_set(&["service", "endpoint", "pod"]), - scrape_interval_ms: 15_000, - cardinality: [ - (label_set(&["service"]), 5), - (label_set(&["service", "endpoint"]), 50), - (label_set(&["service", "endpoint", "pod"]), 30_000), - ] - .into(), - value_range, - data_shape: [ - (label_set(&["service"]), data_shape(10_000.0)), - (label_set(&["service", "endpoint"]), data_shape(1_000.0)), - ] - .into(), - hydra_dataset: None, - }; - [ - (REQUESTS.to_string(), http_metric_facts(None)), - (DURATION.to_string(), http_metric_facts(Some((0.001, 60.0)))), - ] - .into() -} - -fn data_shape(distinct_keys: f64) -> DataShape { - DataShape { - zipf_s: 1.1, - distinct_keys, - tail_index: 1.5, - } -} - -/// Each RAQE's `accuracy_sla` is checked against the metric its candidate -/// family reports (`rqe_optimizer::family_properties`): rank error for KLL, -/// relative value error for DDSketch, relative error for the exact counter -/// and HLL, precision@k for top-k. -/// -/// Request rate is served by an exact counter accumulator, so its error -/// should be zero; the bound is there to reject anything that isn't. -fn raqes() -> Vec { - let se = label_set(&["service", "endpoint"]); - let s = label_set(&["service"]); - let requests = || REQUESTS.to_string(); - let duration = || DURATION.to_string(); - vec![ - Raqe { - id: "req_rate_1h".to_string(), - capability: Capability::RateOrIncrease, - lookback_ms: 3_600_000, - interval_ms: 60_000, - metric: requests(), - spatial_filter: String::new(), - grouping_labels: se.clone(), - accuracy_sla: 0.1, - latency_sla_ms: None, - topk_k: None, - accuracy_covers_share: None, - }, - Raqe { - id: "req_rate_1d".to_string(), - capability: Capability::RateOrIncrease, - lookback_ms: 86_400_000, - interval_ms: 60_000, - metric: requests(), - spatial_filter: String::new(), - grouping_labels: se.clone(), - accuracy_sla: 0.1, - latency_sla_ms: None, - topk_k: None, - accuracy_covers_share: None, - }, - Raqe { - id: "req_rate_5m_tick".to_string(), - capability: Capability::RateOrIncrease, - lookback_ms: 3_600_000, - interval_ms: 300_000, - metric: requests(), - spatial_filter: String::new(), - grouping_labels: se.clone(), - accuracy_sla: 0.1, - latency_sla_ms: None, - topk_k: None, - accuracy_covers_share: None, - }, - Raqe { - id: "latency_p99_1h".to_string(), - capability: Capability::Quantile, - lookback_ms: 3_600_000, - interval_ms: 60_000, - metric: duration(), - spatial_filter: String::new(), - grouping_labels: se.clone(), - accuracy_sla: 0.05, - latency_sla_ms: None, - topk_k: None, - accuracy_covers_share: None, - }, - Raqe { - id: "latency_p99_6h_tick".to_string(), - capability: Capability::Quantile, - lookback_ms: 21_600_000, - interval_ms: 300_000, - metric: duration(), - spatial_filter: String::new(), - grouping_labels: se.clone(), - accuracy_sla: 0.05, - latency_sla_ms: None, - topk_k: None, - accuracy_covers_share: None, - }, - // Shares KLL with the per-endpoint p99s by rolling up. - Raqe { - id: "latency_p99_1h_by_service".to_string(), - capability: Capability::Quantile, - lookback_ms: 3_600_000, - interval_ms: 60_000, - metric: duration(), - spatial_filter: String::new(), - grouping_labels: s.clone(), - accuracy_sla: 0.05, - latency_sla_ms: None, - topk_k: None, - accuracy_covers_share: None, - }, - Raqe { - id: "latency_p99_1d".to_string(), - capability: Capability::Quantile, - lookback_ms: 86_400_000, - interval_ms: 60_000, - metric: duration(), - spatial_filter: String::new(), - grouping_labels: se.clone(), - accuracy_sla: 0.05, - latency_sla_ms: None, - topk_k: None, - accuracy_covers_share: None, - }, - Raqe { - id: "distinct_services_1h".to_string(), - capability: Capability::Cardinality, - lookback_ms: 3_600_000, - interval_ms: 60_000, - metric: requests(), - spatial_filter: String::new(), - grouping_labels: s.clone(), - accuracy_sla: 0.1, - latency_sla_ms: None, - topk_k: None, - accuracy_covers_share: None, - }, - // The higher-is-better case: top-k's metric is precision@k, so 0.9 is - // a *floor*, not a ceiling on error. Served by cms-heap, whose measured precision is - // 1.0 at both configs under the zipf workload (it was 0.0 at both - // under uniform -- top-k is meaningless without real skew). - Raqe { - id: "top_endpoints_1h".to_string(), - capability: Capability::TopKByValue, - lookback_ms: 3_600_000, - interval_ms: 60_000, - metric: requests(), - spatial_filter: String::new(), - grouping_labels: se.clone(), - accuracy_sla: 0.9, - latency_sla_ms: None, - topk_k: None, - accuracy_covers_share: None, - }, - ] -} - -fn positive_integer_flag(name: &str, default: u64) -> u64 { - let args: Vec<_> = std::env::args().collect(); - match args.iter().position(|arg| arg == name) { - Some(index) => args - .get(index + 1) - .unwrap_or_else(|| panic!("{name} requires a positive integer")) - .parse::() - .ok() - .filter(|&value| value > 0) - .unwrap_or_else(|| panic!("{name} requires a positive integer")), - None => default, - } -} - -fn print_mapping( - mapping_number: u64, - mapping: &[usize], - raqes: &[Raqe], - deployments: &[Deployment], -) { - println!("mapping {mapping_number}:"); - for (raqe, &deployment_index) in raqes.iter().zip(mapping) { - let deployment = &deployments[deployment_index]; - println!( - " {} -> {} {} (x={}ms, y={}ms)", - raqe.id, - deployment.config.sketch, - deployment.config.sketch_config, - deployment.window_ms, - deployment.slide_ms, - ); - } -} - -fn print_deployment(label: &str, deployment: &rqe_optimizer::Deployment) { - println!( - "{label}: {} {} {} by {:?} (x={}ms, y={}ms)", - deployment.config.sketch, - deployment.config.sketch_config, - deployment.metric, - deployment.grouping_labels, - deployment.window_ms, - deployment.slide_ms, - ); -} - -/// Only `--milp` enforces latency SLAs, so every other mode rejects the flag. -/// `--candidates-only` and `--sample-mappings` run before the MILP branch. -fn apply_latency_slas(raqes: &mut [Raqe]) { - let args: Vec<_> = std::env::args().collect(); - let has = |flag: &str| args.iter().any(|arg| arg == flag); - assert!( - !has("--latency-sla") - || (has("--milp") && !has("--candidates-only") && !has("--sample-mappings")), - "--latency-sla is only enforced with --milp, without --candidates-only or --sample-mappings" - ); - for pair in args.windows(2).filter(|pair| pair[0] == "--latency-sla") { - let (id, ms) = pair[1] - .split_once('=') - .unwrap_or_else(|| panic!("--latency-sla expects RAQE_ID=MS")); - let ms = ms - .parse::() - .ok() - .filter(|value| value.is_finite() && *value > 0.0) - .unwrap_or_else(|| { - panic!("latency SLA must be a finite positive number of milliseconds") - }); - let raqe = raqes - .iter_mut() - .find(|raqe| raqe.id == id) - .unwrap_or_else(|| panic!("unknown RAQE in --latency-sla: {id}")); - assert!( - raqe.latency_sla_ms.replace(ms).is_none(), - "duplicate latency SLA for {id}" - ); - } -} - -fn weight_flag(name: &str, default: f64) -> f64 { - let args: Vec<_> = std::env::args().collect(); - match args.iter().position(|arg| arg == name) { - Some(index) => args - .get(index + 1) - .and_then(|value| value.parse::().ok()) - .filter(|value| *value >= 0.0) - .unwrap_or_else(|| panic!("{name} requires a non-negative number")), - None => default, - } -} - -fn print_plan_cost(label: &str, plan_cost: &PlanCost) { - println!( - "{label}: cpu={:.3e} cpu-sec/sec, memory={:.1}MB", - plan_cost.cpu_secs_per_sec(), - plan_cost.memory_bytes() / 1e6, - ); - for (phase, cost) in [ - ("ingest", &plan_cost.ingest), - ("merge", &plan_cost.merge), - ("query", &plan_cost.query), - ("storage", &plan_cost.storage), - ] { - println!( - " {phase:<7} cpu={:.3e} memory={:.1}MB", - cost.cpu_secs_per_sec, - cost.memory_bytes / 1e6, - ); - } -} - -/// Flag parsing above only looks up known names, so a typo would be ignored -/// silently; reject anything unrecognized instead. -fn reject_unknown_args() { - const SWITCHES: &[&str] = &[ - "--allow-undeployable-families", - "--candidates-only", - "--milp", - "--streaming", - ]; - const WITH_VALUE: &[&str] = &[ - "--latency-sla", - "--print-candidates", - "--print-first", - "--progress-every", - "--sample-mappings", - "--saturation-dir", - "--w-cpu", - "--w-mem", - ]; - let mut args = std::env::args().skip(1); - while let Some(arg) = args.next() { - if WITH_VALUE.contains(&arg.as_str()) { - args.next(); - } else if !SWITCHES.contains(&arg.as_str()) { - panic!("unknown argument: {arg}"); - } - } -} - -fn main() { - reject_unknown_args(); - let candidates_only = std::env::args().any(|arg| arg == "--candidates-only"); - let mut raqes = raqes(); - apply_latency_slas(&mut raqes); - let cost_table = load_cost_table(); - let curves = load_saturation_curves(); - let check = curves.check_cost_table(&cost_table); - for row in &check.unchecked { - eprintln!("cost table vs. saturation curves, unchecked: {row}"); - } - if !check.mismatched.is_empty() { - panic!( - "the cost table disagrees with the saturation curves: {:#?}", - check.mismatched - ); - } - let facts = facts(); - if let Err(problems) = validate_facts(&raqes, &facts) { - panic!("invalid workload facts: {problems:#?}"); - } - let accuracy = |raqe: &Raqe, deployment: &Deployment| curves.accuracy(raqe, deployment, &facts); - - let allow_undeployable = std::env::args().any(|arg| arg == "--allow-undeployable-families"); - let unpruned_count = candidates_only.then(|| { - build_all_candidates_unpruned(&raqes, &cost_table, &facts, allow_undeployable).len() - }); - let deployments = - build_all_candidates(&raqes, &cost_table, &facts, allow_undeployable, &accuracy); - println!( - "{} RAQEs, {} candidate deployments (from {} real cost-table rows)", - raqes.len(), - deployments.len(), - cost_table.len(), - ); - - if let Some(unpruned_count) = unpruned_count { - println!( - "candidate dominance: {unpruned_count} generated -> {} retained ({} removed)", - deployments.len(), - unpruned_count - deployments.len(), - ); - let mut possible_mappings = Some(1_u64); - for raqe in &raqes { - let eligible = eligible_deployments_for(raqe, &deployments, &facts, &accuracy); - println!(" {}: {} eligible deployments", raqe.id, eligible.len()); - possible_mappings = possible_mappings.and_then(|count| { - count.checked_mul(eligible.len().try_into().expect("usize fits in u64")) - }); - } - match possible_mappings { - Some(count) => println!("Cartesian mapping space: {count}"), - None => println!("Cartesian mapping space: exceeds u64"), - } - let print_candidates = positive_integer_flag("--print-candidates", 0); - for (index, deployment) in deployments - .iter() - .take(usize::try_from(print_candidates).expect("u64 fits in usize")) - .enumerate() - { - print_deployment(&format!("candidate {index}"), deployment); - } - return; - } - - let missing = unservable(&raqes, &deployments, &facts, &accuracy); - if !missing.is_empty() { - println!("unservable (no eligible deployment): {missing:?}"); - return; - } - - let sample_mappings = positive_integer_flag("--sample-mappings", 0); - if sample_mappings > 0 { - let mut printed = 0; - let result = for_each_mapping_while(&raqes, &deployments, &facts, &accuracy, |mapping| { - printed += 1; - print_mapping(printed, mapping, &raqes, &deployments); - printed < sample_mappings - }); - println!( - "printed {} feasible mapping sample(s); enumeration {}", - result.visited, - if result.completed { - "completed" - } else { - "stopped early" - }, - ); - return; - } - - if std::env::args().any(|arg| arg == "--milp") { - let objective = Objective::AUCCost { - w_cpu: weight_flag("--w-cpu", 1.0), - w_mem: weight_flag("--w-mem", 0.0), - }; - let solution = minimize(&raqes, &deployments, &facts, objective, &accuracy) - .expect("small_problem MILP should be feasible"); - println!( - "MILP solution for {objective:?}: {:.3e}", - objective.value(&solution.plan_cost) - ); - print_plan_cost(" totals", &solution.plan_cost); - for (index, planned) in solution.deployments.iter().enumerate() { - print_deployment(&format!("deployment {index}"), &planned.deployment); - println!( - " retained_instances={} key_tracker={}", - planned.retained_instance_count, - planned.deployment.key_tracker.is_some(), - ); - } - for ((raqe, planned), latency) in raqes - .iter() - .zip(&solution.raqes) - .zip(&solution.plan_cost.query_latency_ms) - { - let served_by = &solution.deployments[planned.deployment].deployment; - let roll_up = if served_by.grouping_labels == raqe.grouping_labels { - String::new() - } else { - format!( - " rolls up {:?} -> {:?}", - served_by.grouping_labels, raqe.grouping_labels - ) - }; - println!( - " {} -> deployment {} merged_instances={} query_latency={latency:.3e} ms{roll_up}", - raqe.id, planned.deployment, planned.merged_instance_count, - ); - } - return; - } - - if std::env::args().any(|arg| arg == "--streaming") { - let mut front = ParetoFront::new(); - let progress_every = positive_integer_flag("--progress-every", 1_000_000); - let print_first = positive_integer_flag("--print-first", 0); - let started = Instant::now(); - let mut processed = 0_u64; - let mapping_count = for_each_mapping(&raqes, &deployments, &facts, &accuracy, |mapping| { - front.consider(mapping, score(&raqes, &deployments, mapping, &facts)); - processed += 1; - if processed <= print_first { - print_mapping(processed, mapping, &raqes, &deployments); - } - if processed.is_multiple_of(progress_every) { - let elapsed_secs = started.elapsed().as_secs_f64(); - eprintln!( - "progress: {processed} mappings in {elapsed_secs:.1}s ({:.0}/sec), {} on frontier", - processed as f64 / elapsed_secs.max(f64::MIN_POSITIVE), - front.entries().len(), - ); - } - }); - println!( - "streamed {mapping_count} feasible mappings in {:.1}s; {} remain on the Pareto front", - started.elapsed().as_secs_f64(), - front.entries().len(), - ); - return; - } - - eprintln!( - "WARNING: eager mode will retain every feasible mapping. For larger workloads, use --milp, \ - --candidates-only, or --sample-mappings N instead." - ); - let mappings = brute_force(&raqes, &deployments, &facts, &accuracy); - println!("{} feasible full mappings", mappings.len()); - - let plan_costs: Vec<_> = mappings - .iter() - .map(|m| score(&raqes, &deployments, m, &facts)) - .collect(); - let front = pareto_front(&plan_costs); - println!("{} on the Pareto front\n", front.len()); - - let mut front = front; - front.sort_unstable(); - for &i in &front { - let plan_cost = &plan_costs[i]; - let distinct_deployments: BTreeSet = mappings[i].iter().copied().collect(); - print_plan_cost( - &format!("mapping {i}: {} deployments", distinct_deployments.len()), - plan_cost, - ); - for (raqe, latency) in raqes.iter().zip(&plan_cost.query_latency_ms) { - let raqe_id = &raqe.id; - println!(" {raqe_id}: query_latency={latency:.3e} ms"); - } - } -} diff --git a/rqe-optimizer/examples/workload_scenarios.rs b/rqe-optimizer/examples/workload_scenarios.rs deleted file mode 100644 index 45261445..00000000 --- a/rqe-optimizer/examples/workload_scenarios.rs +++ /dev/null @@ -1,510 +0,0 @@ -//! How the optimal deployment moves as the query workload changes: query -//! types, groupings, lookbacks, repetition intervals, and accuracy and -//! latency SLAs. Each sweep steps one knob of a base workload; every step is -//! one workload solved with the MILP and printed as the deployments it runs -//! and the queries each one serves. -//! -//! A deployment serves only queries with its own capability and metric, and -//! its own grouping or, for a family that merges across groups, a subset of -//! it (a roll-up; `candidates::is_eligible`). So the mixed-type sweeps cost -//! the same as solving each part alone, while the mixed-grouping sweeps can -//! share one fine deployment across groupings. -//! -//! Run: -//! `cargo run --release -p rqe-optimizer --example workload_scenarios -- -//! --saturation-dir DIR [--w-cpu X] [--w-mem Y] -//! [--allow-undeployable-families] [--sweep NAME]` - -use rqe_optimizer::candidates::build_all_candidates; -use rqe_optimizer::enumerate::unservable; -use rqe_optimizer::milp::{minimize, Objective}; -use rqe_optimizer::saturation::{DataShape, SaturationCurves, COST_TABLE}; -use rqe_optimizer::{ - accuracy_key, validate_facts, AtomicCostTable, Capability, Deployment, LabelSet, MetricFacts, - Raqe, WorkloadFacts, -}; - -const MINUTE_MS: u64 = 60_000; -const HOUR_MS: u64 = 60 * MINUTE_MS; -const DAY_MS: u64 = 24 * HOUR_MS; - -const REQUESTS: &str = "http_requests_total"; -const DURATION: &str = "http_request_duration_seconds"; -/// The lookback × interval sweep's queries each read their own metric, so -/// none of them share a deployment. -const PER_SERVICE_METRIC_COUNT: usize = 10; - -fn per_service_metric(service_index: usize) -> String { - format!("service_{service_index}_request_duration_seconds") -} - -fn label_set(names: &[&str]) -> LabelSet { - names.iter().map(|name| name.to_string()).collect() -} - -fn by_service() -> LabelSet { - label_set(&["service"]) -} - -fn by_service_endpoint() -> LabelSet { - label_set(&["service", "endpoint"]) -} - -/// One query, with a default accuracy SLA for its capability. Each candidate -/// checks the SLA against its own family's accuracy metric -/// (`rqe_optimizer::accuracy_key`). Sweeps adjust the returned value. -fn query( - id: &str, - capability: Capability, - metric: &str, - grouping_labels: LabelSet, - lookback_ms: u64, - interval_ms: u64, -) -> Raqe { - let accuracy_sla = match capability { - Capability::Quantile => 0.05, - Capability::TopKByValue => 0.9, - _ => 0.1, - }; - Raqe { - id: id.to_string(), - capability, - lookback_ms, - interval_ms, - metric: metric.to_string(), - spatial_filter: String::new(), - grouping_labels, - accuracy_sla, - latency_sla_ms: None, - topk_k: None, - accuracy_covers_share: None, - } -} - -fn p99(id: &str, lookback_ms: u64) -> Raqe { - query( - id, - Capability::Quantile, - DURATION, - by_service_endpoint(), - lookback_ms, - MINUTE_MS, - ) -} - -fn distinct_endpoints(id: &str, lookback_ms: u64) -> Raqe { - query( - id, - Capability::Cardinality, - REQUESTS, - by_service(), - lookback_ms, - MINUTE_MS, - ) -} - -/// p99 latency over three lookbacks of one metric: candidates for sharing -/// one deployment. -fn shared_p99_workload() -> Vec { - vec![ - p99("p99_1h", HOUR_MS), - p99("p99_6h", 6 * HOUR_MS), - p99("p99_1d", DAY_MS), - ] -} - -/// Distinct endpoints per service over three lookbacks of one metric. -fn shared_distinct_workload() -> Vec { - vec![ - distinct_endpoints("distinct_1h", HOUR_MS), - distinct_endpoints("distinct_6h", 6 * HOUR_MS), - distinct_endpoints("distinct_1d", DAY_MS), - ] -} - -struct Scenario { - label: String, - raqes: Vec, -} - -struct Sweep { - name: &'static str, - description: &'static str, - scenarios: Vec, -} - -fn lookback_interval_sweep() -> Sweep { - let mut scenarios = Vec::new(); - for lookback_ms in [5 * MINUTE_MS, HOUR_MS, 6 * HOUR_MS, DAY_MS, 7 * DAY_MS] { - for interval_ms in [15_000, MINUTE_MS, 5 * MINUTE_MS] { - let raqes = (0..PER_SERVICE_METRIC_COUNT) - .map(|service_index| { - query( - &format!("p99_service_{service_index}"), - Capability::Quantile, - &per_service_metric(service_index), - by_service_endpoint(), - lookback_ms, - interval_ms, - ) - }) - .collect(); - scenarios.push(Scenario { - label: format!( - "lookback {} interval {}", - duration_label(lookback_ms), - duration_label(interval_ms) - ), - raqes, - }); - } - } - Sweep { - name: "lookback-interval", - description: "10 p99 queries on distinct metrics, all with one lookback and interval", - scenarios, - } -} - -fn mixed_grouping_sweep() -> Sweep { - let p99_by = |grouping_labels: LabelSet, id: &str| { - query( - id, - Capability::Quantile, - DURATION, - grouping_labels, - HOUR_MS, - MINUTE_MS, - ) - }; - let by_service_query = || p99_by(by_service(), "p99_1h_by_service"); - let by_service_endpoint_query = || p99_by(by_service_endpoint(), "p99_1h_by_endpoint"); - Sweep { - name: "mixed-grouping", - description: "1h p99 by (service), by (service, endpoint), and both together", - scenarios: vec![ - Scenario { - label: "by service".to_string(), - raqes: vec![by_service_query()], - }, - Scenario { - label: "by service, endpoint".to_string(), - raqes: vec![by_service_endpoint_query()], - }, - Scenario { - label: "both".to_string(), - raqes: vec![by_service_query(), by_service_endpoint_query()], - }, - ], - } -} - -fn mixed_type_sweep() -> Sweep { - let rate = || { - query( - "rate_1h", - Capability::RateOrIncrease, - REQUESTS, - by_service_endpoint(), - HOUR_MS, - MINUTE_MS, - ) - }; - let quantile = || p99("p99_1h", HOUR_MS); - let distinct = || distinct_endpoints("distinct_1h", HOUR_MS); - let top_endpoints = || { - query( - "topk_1h", - Capability::TopKByValue, - REQUESTS, - by_service_endpoint(), - HOUR_MS, - MINUTE_MS, - ) - }; - let alone = |raqe: Raqe| Scenario { - label: format!("{} alone", raqe.id), - raqes: vec![raqe], - }; - Sweep { - name: "mixed-type", - description: "1h rate, p99, distinct count and top-k, alone and together", - scenarios: vec![ - alone(rate()), - alone(quantile()), - alone(distinct()), - alone(top_endpoints()), - Scenario { - label: "rate + p99 + distinct".to_string(), - raqes: vec![rate(), quantile(), distinct()], - }, - Scenario { - label: "all four".to_string(), - raqes: vec![rate(), quantile(), distinct(), top_endpoints()], - }, - ], - } -} - -fn latency_sla_sweep() -> Sweep { - let scenarios = [None, Some(5.0), Some(2.0), Some(1.0), Some(0.5), Some(0.2)] - .into_iter() - .map(|latency_sla_ms| { - let mut raqes = shared_p99_workload(); - raqes[0].latency_sla_ms = latency_sla_ms; - Scenario { - label: match latency_sla_ms { - Some(limit) => format!("p99_1h latency <= {limit} ms"), - None => "no latency SLA".to_string(), - }, - raqes, - } - }) - .collect(); - Sweep { - name: "latency-sla", - description: "p99 over 1h/6h/1d of one metric; tighten the latency SLA of p99_1h", - scenarios, - } -} - -fn accuracy_sla_sweep() -> Sweep { - let tightened = |mut raqes: Vec, accuracy_sla: f64| { - raqes[0].accuracy_sla = accuracy_sla; - Scenario { - label: format!("{} accuracy SLA {accuracy_sla}", raqes[0].id), - raqes, - } - }; - let quantile_scenarios = [0.05, 0.01, 0.005, 0.002, 0.001] - .into_iter() - .map(|accuracy_sla| tightened(shared_p99_workload(), accuracy_sla)); - let distinct_scenarios = [0.1, 0.05, 0.02, 0.01] - .into_iter() - .map(|accuracy_sla| tightened(shared_distinct_workload(), accuracy_sla)); - Sweep { - name: "accuracy-sla", - description: "p99 and distinct count over 1h/6h/1d of one metric; tighten the 1h query's accuracy SLA", - scenarios: quantile_scenarios.chain(distinct_scenarios).collect(), - } -} - -/// Every metric: 5 services × 10 endpoints × 600 pods = 30,000 series scraped -/// every 15 s, as in `small_problem`. Durations range from 1 ms to 60 s. -fn facts() -> WorkloadFacts { - let metric_facts = |value_range| MetricFacts { - labels: label_set(&["service", "endpoint", "pod"]), - scrape_interval_ms: 15_000, - cardinality: [ - (by_service(), 5), - (by_service_endpoint(), 50), - (label_set(&["service", "endpoint", "pod"]), 30_000), - ] - .into(), - value_range, - data_shape: [ - (by_service(), data_shape(10_000.0)), - (by_service_endpoint(), data_shape(1_000.0)), - ] - .into(), - hydra_dataset: None, - }; - let duration_range = Some((0.001, 60.0)); - let per_service_metrics = (0..PER_SERVICE_METRIC_COUNT).map(|service_index| { - ( - per_service_metric(service_index), - metric_facts(duration_range), - ) - }); - [ - (REQUESTS.to_string(), metric_facts(None)), - (DURATION.to_string(), metric_facts(duration_range)), - ] - .into_iter() - .chain(per_service_metrics) - .collect() -} - -fn data_shape(distinct_keys: f64) -> DataShape { - DataShape { - zipf_s: 1.1, - distinct_keys, - tail_index: 1.5, - } -} - -/// The flag's value, or `None` when the flag is absent. -fn flag_value(flag: &str) -> Option { - let args: Vec<_> = std::env::args().collect(); - let index = args.iter().position(|arg| arg == flag)?; - Some( - args.get(index + 1) - .unwrap_or_else(|| panic!("{flag} requires a value")) - .clone(), - ) -} - -fn weight_flag(flag: &str, default: f64) -> f64 { - flag_value(flag).map_or(default, |value| { - value - .parse::() - .ok() - .filter(|weight| *weight >= 0.0) - .unwrap_or_else(|| panic!("{flag} requires a non-negative number")) - }) -} - -fn reject_unknown_args() { - const SWITCHES: &[&str] = &["--allow-undeployable-families"]; - const WITH_VALUE: &[&str] = &["--saturation-dir", "--sweep", "--w-cpu", "--w-mem"]; - let mut args = std::env::args().skip(1); - while let Some(arg) = args.next() { - if WITH_VALUE.contains(&arg.as_str()) { - args.next(); - } else if !SWITCHES.contains(&arg.as_str()) { - panic!("unknown argument: {arg}"); - } - } -} - -fn saturation_dir() -> String { - flag_value("--saturation-dir").expect("--saturation-dir DIR is required") -} - -fn load_cost_table() -> AtomicCostTable { - let path = std::path::Path::new(&saturation_dir()).join(COST_TABLE); - let raw = std::fs::read_to_string(&path) - .unwrap_or_else(|e| panic!("couldn't read {} ({e})", path.display())); - serde_json::from_str(&raw) - .unwrap_or_else(|e| panic!("{} isn't a valid AtomicCostTable: {e}", path.display())) -} - -fn load_saturation_curves(cost_table: &AtomicCostTable) -> SaturationCurves { - let curves = SaturationCurves::load(std::path::Path::new(&saturation_dir())) - .unwrap_or_else(|e| panic!("couldn't load saturation curves: {e}")); - // ponytail: warn rather than panic, since a config with no curve is never - // eligible; panic like small_problem once the cost export matches the grid. - let check = curves.check_cost_table(cost_table); - for row in check.unchecked.iter().chain(&check.mismatched) { - eprintln!("cost table vs. saturation curves: {row}"); - } - curves -} - -fn duration_label(ms: u64) -> String { - match ms { - _ if ms.is_multiple_of(DAY_MS) => format!("{}d", ms / DAY_MS), - _ if ms.is_multiple_of(HOUR_MS) => format!("{}h", ms / HOUR_MS), - _ if ms.is_multiple_of(MINUTE_MS) => format!("{}m", ms / MINUTE_MS), - _ => format!("{}s", ms / 1_000), - } -} - -fn describe(deployment: &Deployment) -> String { - format!( - "{} {} by {:?} x={} y={}", - deployment.config.sketch, - deployment.config.sketch_config, - deployment.grouping_labels, - duration_label(deployment.window_ms), - duration_label(deployment.slide_ms), - ) -} - -fn solve_and_print( - scenario: &Scenario, - cost_table: &AtomicCostTable, - curves: &SaturationCurves, - facts: &WorkloadFacts, - objective: Objective, - allow_undeployable: bool, -) { - let raqes = &scenario.raqes; - if let Err(problems) = validate_facts(raqes, facts) { - panic!("invalid workload facts: {problems:#?}"); - } - let accuracy = |raqe: &Raqe, deployment: &Deployment| curves.accuracy(raqe, deployment, facts); - let deployments = build_all_candidates(raqes, cost_table, facts, allow_undeployable, &accuracy); - let missing = unservable(raqes, &deployments, facts, &accuracy); - if !missing.is_empty() { - println!("[{}] unservable: {missing:?}", scenario.label); - return; - } - let solution = match minimize(raqes, &deployments, facts, objective, &accuracy) { - Ok(solution) => solution, - Err(error) => { - println!("[{}] infeasible: {error}", scenario.label); - return; - } - }; - let plan_cost = &solution.plan_cost; - println!( - "[{}] objective={:.3e} cpu={:.3e} cpu-sec/sec memory={:.1}MB deployments={}", - scenario.label, - objective.value(plan_cost), - plan_cost.cpu_secs_per_sec(), - plan_cost.memory_bytes() / 1e6, - solution.deployments.len(), - ); - for (deployment_index, planned) in solution.deployments.iter().enumerate() { - println!(" D{deployment_index}: {}", describe(&planned.deployment)); - for ((raqe, planned_raqe), latency_ms) in raqes - .iter() - .zip(&solution.raqes) - .zip(&plan_cost.query_latency_ms) - .filter(|((_, planned_raqe), _)| planned_raqe.deployment == deployment_index) - { - let (accuracy_metric, _) = accuracy_key(&planned.deployment.config.sketch); - let achieved = accuracy(raqe, &planned.deployment) - .map_or("unknown".to_string(), |value| format!("{value:.4}")); - println!( - " {:<22} merged={:<4} latency={latency_ms:.3e} ms {}={achieved} (sla {})", - raqe.id, planned_raqe.merged_instance_count, accuracy_metric, raqe.accuracy_sla, - ); - } - } -} - -fn main() { - reject_unknown_args(); - let cost_table = load_cost_table(); - let curves = load_saturation_curves(&cost_table); - let facts = facts(); - let objective = Objective::AUCCost { - w_cpu: weight_flag("--w-cpu", 1.0), - w_mem: weight_flag("--w-mem", 0.0), - }; - let allow_undeployable = std::env::args().any(|arg| arg == "--allow-undeployable-families"); - let sweeps = [ - lookback_interval_sweep(), - mixed_grouping_sweep(), - mixed_type_sweep(), - latency_sla_sweep(), - accuracy_sla_sweep(), - ]; - let selected = flag_value("--sweep"); - if let Some(name) = &selected { - assert!( - sweeps.iter().any(|sweep| sweep.name == name), - "unknown --sweep {name}; expected one of {:?}", - sweeps.iter().map(|sweep| sweep.name).collect::>() - ); - } - println!("{objective:?}"); - for sweep in sweeps - .iter() - .filter(|sweep| selected.as_ref().is_none_or(|name| sweep.name == name)) - { - println!("\n=== {}: {} ===", sweep.name, sweep.description); - for scenario in &sweep.scenarios { - solve_and_print( - scenario, - &cost_table, - &curves, - &facts, - objective, - allow_undeployable, - ); - } - } -} diff --git a/rqe-optimizer/results/autosketch-vs-asap-hydra/README.md b/rqe-optimizer/results/autosketch-vs-asap-hydra/README.md deleted file mode 100644 index 1bf4d923..00000000 --- a/rqe-optimizer/results/autosketch-vs-asap-hydra/README.md +++ /dev/null @@ -1,299 +0,0 @@ -# AutoSketch vs. ASAP with Hydra candidates and roll-ups - -Results of ProjectASAP/ASAPQuery#777's synthetic evaluation (§6.3), rerun with -everything added since #138: - -- the planner may serve a coarse RQE from a finer deployment by merging its - groups (**roll-ups**, #190), and an **ablation** without them; -- **Hydra** grids (hydra-hll, hydra-univmon-cardinality, hydra-kll) are - candidates for ASAP and PerQuery, with accuracy measured on the eval's own - data (#193 design; #195–#202 measurement and optimizer); -- **multi-grouping** templates (`http` and `flows`, templates 11–17, #196), - among them p99 latency `GROUP BY CUBE(region, service, endpoint)` - (template 16, all 7 groupings), evaluated alone (**multi-grouping**) and - added to the classic mixed set (**mixed + multi-grouping**); -- the KLL footprint is asap_sketchlib's real allocation (#192), and the cost - table is re-measured; -- classic templates 7 (`sum by (job) (rate(data_total[w]))`) and 9 - (`topk(32, sum by (label_0) (rate(data_total[w])))`) are dropped, as - ASAPQuery's engine would need per-series counter deltas to serve them - (2026-10-13 rerun of the classic and all workloads; the multi-grouping - results are unchanged); -- a **denser config grid** (2026-10-14, #213): KLL `k` ∈ {25, 50, 100, 200, - 400, 800} (was 50/200/800), DDSketch α adds 0.1, and Hydra grids add - W = 65536 (hydra-hll, hydra-kll). Only the new configs were measured and - appended to the curves, the cost table and the Hydra study. HLL stays at - `lg_k` 12/14/16, the precisions asap_sketchlib builds; -- **instant RQEs read one scrape** (2026-10-14, #215): `sum by (job)` and - `quantile by (job)` look back 5 ms, one sample per series (N = 1e3 per - job), instead of 1 s (2e5), as a PromQL instant query does. Rerun of the - classic and all workloads; the multi-grouping tables are unchanged, so - their results stand. - -Cost is billed by use (sketch-bench `docs/rqe_optimizer_cost_model.md`): -`w_cpu · AUC(CPU) + w_mem · AUC(memory)`, at two weight settings (CPU only, in -vCPU; AWS Fargate's per-vCPU and per-GiB prices, in $/hour). Every RQE has a -p95 accuracy target (0.05 in its family's own metric). An RQE with a coverage -rule is held to the worse of its smallest and largest covered group -(sketch-bench#189). Hydra is held to its measured worst error over the covered -groups, the worst over N and seeds. - -## Methods - -| Method | What it may use | -|---|---| -| ASAP | the MILP over every RQE jointly: shared deployments, roll-ups, Hydra grids | -| ASAP (no roll-ups) | the same MILP, each RQE only at its own grouping (or a Hydra grid): the roll-up ablation | -| PerQuery-CostAware | the same MILP without sharing (one deployment per RQE, no roll-ups) | -| AutoSketch-Adapted | fixed configs chosen by memory (NSDI '24); no Hydra (#159) | - -## Results - -Cost of each method's cheapest plan (no latency bound), absolute: - -| Workload | RQEs | Weights | ASAP | ASAP (no roll-ups) | PerQuery | AutoSketch | RQEs rolled up | Deployments (with / without roll-ups) | -|---|---|---|---|---|---|---|---|---| -| mixed (classic) | 42 | CPU (vCPU) | 2.71 | 2.71 | 7.67 | 2,701 | 0 | 7 / 7 | -| mixed (classic) | 42 | Fargate ($/h) | 0.163 | 0.163 | 0.487 | 127 | 0 | 7 / 7 | -| mixed, r = 8 | 76 | CPU | 2.8 | 2.8 | 13.5 | 3,239 | 0 | 7 / 7 | -| mixed, m = 8 | 336 | CPU | 21.7 | 21.7 | 61.3 | 21,610 | 0 | 56 / 56 | -| mixed, m = 16 | 672 | CPU | 43.4 | 43.4 | 123 | 43,221 | 0 | 112 / 112 | -| mixed, m = 16 | 672 | Fargate | 2.61 | 2.61 | 7.78 | 2,035 | 0 | 112 / 112 | -| multi-grouping | 62 | CPU | 0.22 | 0.55 | 1.07 | 9.65 | 52 | 5 / 26 | -| multi-grouping | 62 | Fargate | 0.0134 | 0.0333 | 0.0589 | 0.406 | 52 | 5 / 26 | -| multi-grouping, r = 8 | 124 | CPU | 0.241 | 0.572 | 2.04 | 11.6 | 104 | 5 / 26 | -| multi-grouping, m = 8 | 496 | CPU | 1.76 | 4.4 | 8.54 | 77.2 | 416 | 40 / 208 | -| multi-grouping, m = 16 | 992 | CPU | 3.52 | 8.8 | 17.1 | 154 | 832 | 80 / 416 | -| multi-grouping, m = 16 | 992 | Fargate | 0.215 | 0.532 | 0.942 | 6.5 | 832 | 80 / 416 | -| mixed + multi-grouping | 104 | CPU | 2.93 | 3.26 | 8.74 | 2,711 | 52 | 12 / 33 | -| mixed + multi-grouping | 104 | Fargate | 0.177 | 0.197 | 0.545 | 128 | 52 | 12 / 33 | -| mixed + multi-grouping, r = 8 | 200 | CPU | 3.04 | 3.37 | 15.5 | 3,250 | 104 | 12 / 33 | -| mixed + multi-grouping, m = 8 | 832 | CPU | 23.5 | 26.1 | 69.9 | 21,688 | 416 | 96 / 264 | -| mixed + multi-grouping, m = 16 | 1664 | CPU | 46.9 | 52.2 | 140 | 43,375 | 832 | 192 / 528 | -| mixed + multi-grouping, m = 16 | 1664 | Fargate | 2.83 | 3.15 | 8.73 | 2,042 | 832 | 192 / 528 | - -Every workload: 0 RQEs dropped, 0 sanity violations (ASAP ≤ ASAP without -roll-ups ≤ PerQuery, and ASAP ≤ AutoSketch). `summary_synthetic.md` has -every frontier point; `summary_sla.md` every SLA. - -Planning time (CPU weights): ASAP (candidates + MILP) vs. AutoSketch (search -plus its measured benchmark, approxbench accuracy runs at 1e8 items): - -| Workload | RQEs | ASAP | PerQuery | AutoSketch | -|---|---|---|---|---| -| mixed | 42 | 2.2 s | 4.6 s | 0.002 s + 169 s | -| mixed, m = 16 | 672 | 37.2 s | 79.4 s | 0.028 s + 2,710 s | -| multi-grouping | 62 | 0.52 s | 0.28 s | 0.002 s + 227 s | -| multi-grouping, m = 16 | 992 | 8.5 s | 5.0 s | 0.037 s + 3,625 s | -| mixed + multi-grouping | 104 | 2.8 s | 5.0 s | 0.004 s + 396 s | -| mixed + multi-grouping, m = 16 | 1664 | 40.0 s | 72.7 s | 0.067 s + 6,335 s | - -Every planning time above comes from the same node type (CloudLab Xeon -E5-2683 v3), each node running its share of the tables serially. The mixed -templates dominate it: their per-series quantile and sum RQEs give one -candidate per (series grouping, window, config). - -The denser grid gives ASAP and PerQuery more candidates (6 KLL and 5 DDSketch -configs instead of 3 and 4), so their MILPs are larger than before (mixed + -multi-grouping: 0.94 → 2.1 s). The reruns also spread over 4 nodes and ran -beside each other's shares, so planning times are not comparable across reruns -to better than about 2×; within one rerun they are. AutoSketch reaches a -feasible config sooner (15 probes instead of 19 on mixed + multi-grouping), so -its measured benchmark is shorter (481 → 396 s). - -**What the denser grid changed** (95%, cheapest plans): CPU cost falls 0–1.4% -(mixed 2.38 → 2.35 vCPU, mixed + multi-grouping 2.60 → 2.57), since insert -CPU per item is flat across configs (KLL ≈ 30 ns, DDSketch ≈ 19 ns at every -size). Memory-weighted cost falls more, as plans pick a smaller KLL or -DDSketch closer to the target: Fargate 0.170 → 0.149 $/h on mixed (−13%), -0.184 → 0.162 on mixed + multi-grouping (−12%), 0.0137 → 0.0134 on -multi-grouping. No conclusion changes. - -**What one-scrape instant RQEs changed** (95%, cheapest plans): ASAP's CPU -rises ~15% on every mixed and mixed + multi-grouping workload (mixed -2.35 → 2.71 vCPU, mixed + multi-grouping 2.57 → 2.93; Fargate +9%), with one -more deployment per metric. The instant `sum by (job)` now reads a 5 ms -window, so `sum by (job) (sum_over_time)` no longer shares its 1 s exact-sum -deployment and gets its own, ingesting the stream again (~0.36 vCPU per -metric). PerQuery and AutoSketch, which never shared it, are unchanged, as -are AutoSketch's probes and benchmark times, and the tightest feasible -latency bounds. No conclusion changes: ASAP stays 2.8–3.0× below PerQuery on -mixed and ~1,000× below AutoSketch. - -### Roll-ups - -On the **multi-grouping** set, ASAP serves 52 of 62 RQEs from a finer -deployment, cutting its deployments from 26 to 5 and its cost by **60%** -(0.550 → 0.220 vCPU; 0.0333 → 0.0134 $/h), and by the same share at r = 8, -m = 8 and m = 16. The cube (template 16) is the clearest case: one KLL -deployment at (region, service, endpoint) answers all 14 p99 RQEs. Every -deployment ingests the whole stream, so cost grows with the number of -groupings kept. A KLL or DDSketch merge summarizes the pooled data of the -merged groups, so a roll-up answers a coarse group's p99, not a quantile of -quantiles (KLL reads its measured merge curve). - -On **mixed + multi-grouping**, roll-ups save 10% (3.26 → 2.93 vCPU, 33 → 12 -deployments): the classic templates, whose streams have one grouping each, -are most of the cost. On classic alone, roll-ups never apply and the two ASAP -lines coincide. ASAP's ~2.8× advantage over PerQuery on classic (7.67 vs. 2.71 -vCPU; 1.7–3.0× at every SLA) comes from sharing deployments across RQEs at -the same grouping. - -### Hydra - -Hydra grids are candidates for every eligible RQE, and many are eligible -(e.g. hydra-hll at W ≥ 4096 for flows at ≥ 5% coverage; hydra-kll by service -at W = 16384 for every group). **Neither ASAP nor PerQuery chooses a Hydra -grid in any workload, weight setting, bound or SLA** (`hydra_rqes = 0` -throughout). A Hydra insert fans out to every label subset in every row and -costs 1.9 µs per record (flows, 3 labels) to 5.1 µs (http, 4 labels), against -3.85 ns for a per-group HLL insert; at the eval's rates that is 8–11 vCPU of -ingest for one grid, against ASAP's 0.05–1.9 vCPU for the whole stream. The -only near case is flows under Fargate weights, where a Hydra-only plan costs -1.8× ASAP's (it uses 4.4× less memory). A check on real traces (Alibaba 2022, -Google 2011; not committed) agreed: Hydra meets 0.05 only for groups holding -≥ 1–5% of records, and per-group sketches that grow with n use less memory -than the grid at the W that needs. - -W = 65536 (#213) widens what Hydra is accurate for: on groups holding ≥ 1% of -the records, the worst error at 1e7 records drops from 0.283 to 0.039 -(hydra-hll, `http`), 0.074 to 0.021 (hydra-hll, `flows`) and 0.054 to 0.017 -(hydra-kll, unmerged; 0.056 to 0.017 over every merge count). But it is the -most expensive grid on every axis (3.3 GB per hydra-hll grid, 6.2 µs per -insert on `http`), and it is never chosen either. - -hydra-univmon-cardinality reads ≈ 100% error at N ≥ 1e6 at every W -(8 layers × a small heap saturate at ~1e6 distinct values per cell), so it is -ruled out by accuracy; cardinality's Hydra candidate is hydra-hll. - -## Figures - -- `fig_frontier.png`: version 1, cost vs. query latency per workload and - weight setting; frontiers swept over latency bounds (ASAP, ASAP without - roll-ups, PerQuery as lines; AutoSketch, which ignores latency, as a point). -- `fig_cost_vs_sla.png`: version 2, each method's cost at batch-latency SLAs - {100, 300, 1000, 3000, 10000} ms (AutoSketch: ● meets the SLA, × misses). - With the multi-grouping templates the tightest feasible bound is 490 ms - (the per-(service, endpoint) queries), so the 100 and 300 ms SLAs have no - plan. -- `fig_planning_time.png`: planning time vs. RQEs (multi-grouping set). -- `paper/`: the paper's §6.3 figures, from `scripts/plot_sec63_paper.py - rqe-optimizer/results/autosketch-vs-asap-hydra - rqe-optimizer/results/autosketch-vs-asap-hydra/paper`: `sec63_cost_bars` - (each method's cost at AutoSketch's latency, with each baseline's multiple - of ASAP's), `sec63_frontier` and `sec63_rollups`, as PDF and PNG. -- `fig_rollup_ablation.png`: ASAP vs. ASAP without roll-ups, cheapest plan per - workload (CPU only, log scale). - -## Plans - -`plans/
.plans.json.gz`: each workload's chosen plans, from a rerun with -the plans kept (`--runs 1`, without `--no-chosen`; costs are deterministic, so -they match the results above). `plans` lists, for each method and weight -setting, the cheapest plan (`kind: "cheapest"`) and the plan at each feasible -SLA (`kind: "sla"`, `sla_ms`), with its cost, latency and deployments. -`chosen` has one entry per RQE: - -| Field | Meaning | -|---|---| -| `rqe` | the RQE's id (table, query, kind, range) | -| `sketch`, `params` | the deployment's sketch and config | -| `window_secs`, `slide_secs` | the deployment's window and slide | -| `merged_shards` | windows the query merges (lookback / window) | -| `deployment` | the deployment's index; RQEs with one index share it | -| `deployment_grouping` | present for a roll-up or Hydra grid: the deployment's (finer) grouping | -| `ingest_workers`, `latency_ms` | ingest workers and the RQE's chain latency | -| `asap_accuracy`, `accuracy_source` | estimated error and where it was read | -| `covers_share` | the RQE's coverage rule, if any | - -```sh -zcat plans/synthetic-templatesmultigroup-shared1.plans.json.gz | jq '.plans[] - | select(.kind == "cheapest" and .method == "asap" and .weights == "cpu") | .chosen' -``` - -## Inputs - -- `inputs/saturation/optimizer_cost/rqe_atomic_costs.json`: the cost table, 63 - rows (56, plus #213's KLL `k` 25/100/400, DDSketch α 0.1 and Hydra W = 65536 - rows), measured serially on idle clnode138 (`study_saturation.py --phase - optimizer-cost --seeds 3 --hydra-variants hydra-hll,hydra-kll`), including - Hydra rows per dataset (`measured_at.dataset`) and #192's KLL footprint. -- `inputs/saturation/hydra_saturation.csv`: the Hydra study (`--phase hydra`, - #202), 7,778 runs on 4 CloudLab nodes (#213 added 999 at W = 65536); - hydra-kll on `hydra_http_latency` rerun with per-service latency scales - (×1–10 log-uniform), so groups differ in distribution as they do in - practice. -- The accuracy curves (`out_grid_1e7_cost/`, `out_1e9/`) are the committed - #138 study's, in `../autosketch-vs-asap-inputs/saturation/` (#194), with - #213's new KLL and DDSketch configs appended. -- `inputs/tables/`: the workloads, `export_autosketch_eval_table.py --synthetic - --keep-duplicate-queries` (classic, multigroup and all × r ∈ {1, 8} × m ∈ {1, 8, 16}). - -## Reproduce - -```sh -cargo build --release -p aqpbm-cli -p rqe-optimizer --bins --examples -DIR=rqe-optimizer/results/autosketch-vs-asap-hydra -# saturation dir = the #138 curves + this cost table and Hydra study: -SAT=$(mktemp -d); cp -r rqe-optimizer/results/autosketch-vs-asap-inputs/saturation/out_* $SAT/ -cp -r $DIR/inputs/saturation/* $SAT/ -while IFS=$'\t' read -r table target result; do - target/release/examples/autosketch_vs_asap synthetic --table $DIR/inputs/tables/$table \ - --target $target --runs 3 --no-chosen --saturation-dir $SAT --out $DIR/$result -done < $DIR/inputs/tables/plan.tsv -python3 scripts/autosketch_benchmark_time.py --binary target/release/approxbench \ - --out $DIR/autosketch-benchmark-times.json $DIR/synthetic-*-tp95.json -python3 scripts/plot_autosketch_vs_asap_synthetic.py $DIR -python3 scripts/plot_autosketch_vs_asap_sla.py $DIR $DIR/synthetic-*-tp95.json -``` - -Runs: Xeon E5-2683 v3, 56 cores, `--runs 3`. First run (superseded by the -reruns below): runner from sketch-bench `hydra/eval-integration` at 44ac160. -Classic: one large table per CloudLab node (clnode178, 167; the small ones -serially on clnode138), 2026-10-10. Multigroup and all (with the cube -template): serially on one idle node, 2026-10-11. - -Rerun without templates 7 and 9 (2026-10-13), runner at sketch-bench -eval/drop-group-rate-templates: all serially on idle clnode109; classic -m = 16 on clnode167, m = 8 on clnode155, the small ones serially on -clnode138; plans (`--runs 1`, kept) on clnode138 and clnode155. The -multigroup tables are unchanged, so their results stand (a rerun of -multigroup-shared1 reproduces its costs and plans exactly). AutoSketch's -benchmark times are reused: the dropped RQEs used exact accumulators or a -top-k config already probed on the same metric, so every probe was already -measured and each workload's benchmark time is unchanged. - -Denser grid (2026-10-14, #213, runner at sketch-bench eval/dense-grid): -multi-grouping on the 4 CloudLab nodes clnode109/155/167/178, then classic and -all on #210's tables on the same 4 nodes, each node running its share -serially. Plans: `--runs 1`, without `--no-chosen`, extracted with -`scripts/extract_autosketch_plans.py`. - -One-scrape instant RQEs (2026-10-14, #215, sketch-bench main at acc2db1): the -8 classic and all tables re-exported (`--keep-duplicate-queries`; only the 6 -instant RQEs per metric change) and rerun serially on a 16-core desktop, with -their plans and the 90%/99% runs of `../autosketch-vs-asap-targets/`. The 8 -95% results were then rerun on CloudLab (clnode109/155/167/178, the nodes of -the multi-grouping runs, each running its share serially), so every planning -time comes from one node type; every cost, latency and plan matched the -desktop's exactly. The multi-grouping JSONs and plans are the -denser-grid rerun's. AutoSketch's probes are unchanged, so every benchmark time -is reused from `autosketch-benchmark-times.json`. - -The JSONs, figures and plans here are from those last two reruns; the 95% -JSONs of the mixed and mixed + multi-grouping tables are the CloudLab ones. - -## Caveats - -- The cost model charges a per-group sketch its insert only, not routing a - sample to its group's sketch (~100 ns per record per deployment on the - real traces). Adding it raises ASAP's flows ingest from 0.11 to ~1.3 vCPU, - still far below a Hydra grid's 8.2; the Hydra conclusion holds. -- Per-group sketch memory is asap_sketchlib's full allocation from - construction (#192); sketches that grow with n would favour per-group - sketches further. -- Hydra datasets: no flows DDoS burst (datagen can't tie a source set to a - subnet), uniform proto, smaller UnivMon cells (3×256, 8 layers, heap 64). -- Template 12 groups by (dst_subnet, proto) rather than (dst_subnet, - dst_port): answering 1e6 groups every minute took ~49 s and set every - plan's batch latency above the largest SLA. diff --git a/rqe-optimizer/results/autosketch-vs-asap-hydra/autosketch-benchmark-times.json b/rqe-optimizer/results/autosketch-vs-asap-hydra/autosketch-benchmark-times.json deleted file mode 100644 index 6a390d31..00000000 --- a/rqe-optimizer/results/autosketch-vs-asap-hydra/autosketch-benchmark-times.json +++ /dev/null @@ -1,19 +0,0 @@ -{ - "n": 100000000.0, - "secs": { - "[\"cms-heap-topk-fastpath-vector2d\", {\"cols\": 1024, \"heap\": 32, \"rows\": 3}, 1.1, 10000.0, null]": 32.40410717698978, - "[\"cms-heap-topk-fastpath-vector2d\", {\"cols\": 256, \"heap\": 32, \"rows\": 3}, 1.1, 10000.0, null]": 32.35026514204219, - "[\"cms-heap-topk-fastpath-vector2d\", {\"cols\": 4096, \"heap\": 32, \"rows\": 5}, 1.1, 10000.0, null]": 37.245747498993296, - "[\"dd\", {\"alpha\": 0.01}, null, 0.0, 2.0]": 21.549985619960353, - "[\"dd\", {\"alpha\": 0.02}, null, 0.0, 2.0]": 22.274108413956128, - "[\"dd\", {\"alpha\": 0.05}, null, 0.0, 2.0]": 21.460699204995763, - "[\"dd\", {\"alpha\": 0.1}, null, 0.0, 2.0]": 22.29261576803401, - "[\"hll\", {\"lg_k\": 12}, 0.0, 1000000.0, null]": 38.143390018027276, - "[\"hll\", {\"lg_k\": 12}, 0.8, 1000000.0, null]": 39.95713640097529, - "[\"hll\", {\"lg_k\": 14}, 0.0, 1000000.0, null]": 40.32202102598967, - "[\"hll\", {\"lg_k\": 14}, 0.8, 1000000.0, null]": 40.74274828098714, - "[\"kll-percall\", {\"k\": 200}, null, 0.0, 2.0]": 22.40906461601844, - "[\"kll-percall\", {\"k\": 25}, null, 0.0, 2.0]": 22.67945937020704, - "[\"kll-percall\", {\"k\": 50}, null, 0.0, 2.0]": 22.422631791967433 - } -} \ No newline at end of file diff --git a/rqe-optimizer/results/autosketch-vs-asap-hydra/fig_cost_vs_sla.png b/rqe-optimizer/results/autosketch-vs-asap-hydra/fig_cost_vs_sla.png deleted file mode 100644 index 33514fb87be20bef8d347af545863075fdde594d..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 347954 zcmeFaWmuML*DcJ_<#O4}#=yJ*K@mm5AeQY7BApTj9U>(qV52CaH>HB0Qqm$Nt&0*+ zkuC)lkdp5H#`KNv+3NT0eH{D8-aj6XcRjD-9oKbU=Q-z?V~jbyA$#obyxA*fv$3(w zV;(sm&&IYwmW^%tl|TQ$PZ~Oo9>)LMZFNx9O2JIe%I2hnE}Qg8tFtC%Rwjn0)}7O} zurxF~vt#S_ZCiJ3S!ZBnb=Gn>KfmdJzhbMIg+4#mp;9$`%B-_T)GXQ9mM^0Je$x*q zk!Smjjg5I=ucEErw~sdG-G-*7dYX5wSJ-fXalrSlZ2ttw>(V)O4i%A6gMS7OZ%Qyq zNVqKPP#zMl9-SK8m7o3-tC zeDDAK*SQmkHvjWi(JLlXk35?8zw*+TXZ{bqkX60sr)%U7ILR(BH>>U>{mn}$U4i>L 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b/rqe-optimizer/results/autosketch-vs-asap-synthetic-sla/summary_sla.md deleted file mode 100644 index e83a8f2e..00000000 --- a/rqe-optimizer/results/autosketch-vs-asap-synthetic-sla/summary_sla.md +++ /dev/null @@ -1,151 +0,0 @@ -# Version 2: cost by use under a batch latency SLA - -Each method's cost by use (`w_cpu · AUC(CPU) + w_mem · AUC(memory)`) at each SLA; ASAP and PerQuery plan for the SLA (the cheapest plan whose batch latency, the longest chain, is at most it), AutoSketch's one plan ignores it. - -## mixed: 50 RQEs - -Sanity violations: 0. Tightest feasible SLA: ASAP 92 ms, PerQuery 92 ms. AutoSketch latency 1.01e+03 ms; planning time (search + measured benchmark) 197 s. - -| weights | SLA (ms) | method | cost | latency (ms) | CPU (vCPU) | GiB | meets SLA | -|---|---|---|---|---|---|---|---| -| cpu | 100 | ASAP | 34.5 | 98.1 | 34.5 | 33.6 | yes | -| cpu | 100 | PerQuery-CostAware | 59.2 | 98.1 | 59.2 | 170 | yes | -| cpu | 100 | AutoSketch-Adapted | 3.41e+03 | 1.01e+03 | 3.41e+03 | 5.65e+03 | **no** | -| cpu | 300 | ASAP | 12.4 | 295 | 12.4 | 16.3 | yes | -| cpu | 300 | PerQuery-CostAware | 23.6 | 295 | 23.6 | 68.3 | yes | -| cpu | 300 | AutoSketch-Adapted | 3.41e+03 | 1.01e+03 | 3.41e+03 | 5.65e+03 | **no** | -| cpu | 1e+03 | ASAP | 5.12 | 982 | 5.12 | 31.3 | yes | -| cpu | 1e+03 | PerQuery-CostAware | 12 | 982 | 12 | 157 | yes | -| cpu | 1e+03 | AutoSketch-Adapted | 3.41e+03 | 1.01e+03 | 3.41e+03 | 5.65e+03 | **no** | -| cpu | 3e+03 | ASAP | 3.32 | 2.94e+03 | 3.32 | 32.4 | yes | -| cpu | 3e+03 | PerQuery-CostAware | 9.74 | 2.94e+03 | 9.74 | 161 | yes | -| cpu | 3e+03 | AutoSketch-Adapted | 3.41e+03 | 1.01e+03 | 3.41e+03 | 5.65e+03 | yes | -| cpu | 1e+04 | ASAP | 3.32 | 2.94e+03 | 3.32 | 32.4 | yes | -| cpu | 1e+04 | PerQuery-CostAware | 9.55 | 3.84e+03 | 9.55 | 161 | yes | -| cpu | 1e+04 | AutoSketch-Adapted | 3.41e+03 | 1.01e+03 | 3.41e+03 | 5.65e+03 | yes | -| fargate | 100 | ASAP | 1.5 | 98.1 | 35 | 18.7 | yes | -| fargate | 100 | PerQuery-CostAware | 2.84 | 98.1 | 61.7 | 75.2 | yes | -| fargate | 100 | AutoSketch-Adapted | 163 | 1.01e+03 | 3.41e+03 | 5.65e+03 | **no** | -| fargate | 300 | ASAP | 0.573 | 295 | 12.4 | 16.2 | yes | -| fargate | 300 | PerQuery-CostAware | 1.26 | 295 | 23.7 | 67.6 | yes | -| fargate | 300 | AutoSketch-Adapted | 163 | 1.01e+03 | 3.41e+03 | 5.65e+03 | **no** | -| fargate | 1e+03 | ASAP | 0.284 | 989 | 5.13 | 17.1 | yes | -| fargate | 1e+03 | PerQuery-CostAware | 0.813 | 989 | 12.4 | 69.8 | yes | -| fargate | 1e+03 | AutoSketch-Adapted | 163 | 1.01e+03 | 3.41e+03 | 5.65e+03 | **no** | -| fargate | 3e+03 | ASAP | 0.22 | 2.94e+03 | 3.35 | 19 | yes | -| fargate | 3e+03 | PerQuery-CostAware | 0.72 | 2.97e+03 | 9.97 | 71.1 | yes | -| fargate | 3e+03 | AutoSketch-Adapted | 163 | 1.01e+03 | 3.41e+03 | 5.65e+03 | yes | -| fargate | 1e+04 | ASAP | 0.22 | 2.94e+03 | 3.35 | 19 | yes | -| fargate | 1e+04 | PerQuery-CostAware | 0.72 | 2.97e+03 | 9.97 | 71.1 | yes | -| fargate | 1e+04 | AutoSketch-Adapted | 163 | 1.01e+03 | 3.41e+03 | 5.65e+03 | yes | - -## mixed, r = 8: 92 RQEs - -Sanity violations: 0. Tightest feasible SLA: ASAP 92 ms, PerQuery 92 ms. AutoSketch latency 1.01e+03 ms; planning time (search + measured benchmark) 197 s. - -| weights | SLA (ms) | method | cost | latency (ms) | CPU (vCPU) | GiB | meets SLA | -|---|---|---|---|---|---|---|---| -| cpu | 100 | ASAP | 34.5 | 98.1 | 34.5 | 33.6 | yes | -| cpu | 100 | PerQuery-CostAware | 74.2 | 98.1 | 74.2 | 200 | yes | -| cpu | 100 | AutoSketch-Adapted | 4.09e+03 | 1.01e+03 | 4.09e+03 | 5.88e+03 | **no** | -| cpu | 300 | ASAP | 12.4 | 295 | 12.4 | 16.3 | yes | -| cpu | 300 | PerQuery-CostAware | 32.5 | 296 | 32.5 | 82 | yes | -| cpu | 300 | AutoSketch-Adapted | 4.09e+03 | 1.01e+03 | 4.09e+03 | 5.88e+03 | **no** | -| cpu | 1e+03 | ASAP | 5.15 | 982 | 5.15 | 31.3 | yes | -| cpu | 1e+03 | PerQuery-CostAware | 19.3 | 982 | 19.3 | 188 | yes | -| cpu | 1e+03 | AutoSketch-Adapted | 4.09e+03 | 1.01e+03 | 4.09e+03 | 5.88e+03 | **no** | -| cpu | 3e+03 | ASAP | 3.39 | 2.94e+03 | 3.39 | 32.4 | yes | -| cpu | 3e+03 | PerQuery-CostAware | 16.9 | 2.94e+03 | 16.9 | 191 | yes | -| cpu | 3e+03 | AutoSketch-Adapted | 4.09e+03 | 1.01e+03 | 4.09e+03 | 5.88e+03 | yes | -| cpu | 1e+04 | ASAP | 3.39 | 2.94e+03 | 3.39 | 32.4 | yes | -| cpu | 1e+04 | PerQuery-CostAware | 16.7 | 3.84e+03 | 16.7 | 191 | yes | -| cpu | 1e+04 | AutoSketch-Adapted | 4.09e+03 | 1.01e+03 | 4.09e+03 | 5.88e+03 | yes | -| fargate | 100 | ASAP | 1.5 | 98.1 | 35 | 18.7 | yes | -| fargate | 100 | PerQuery-CostAware | 3.52 | 98.1 | 77 | 88.8 | yes | -| fargate | 100 | AutoSketch-Adapted | 192 | 1.01e+03 | 4.09e+03 | 5.88e+03 | **no** | -| fargate | 300 | ASAP | 0.574 | 295 | 12.4 | 16.2 | yes | -| fargate | 300 | PerQuery-CostAware | 1.68 | 296 | 32.5 | 81.3 | yes | -| fargate | 300 | AutoSketch-Adapted | 192 | 1.01e+03 | 4.09e+03 | 5.88e+03 | **no** | -| fargate | 1e+03 | ASAP | 0.285 | 989 | 5.17 | 17.1 | yes | -| fargate | 1e+03 | PerQuery-CostAware | 1.18 | 989 | 19.9 | 83.8 | yes | -| fargate | 1e+03 | AutoSketch-Adapted | 192 | 1.01e+03 | 4.09e+03 | 5.88e+03 | **no** | -| fargate | 3e+03 | ASAP | 0.223 | 2.94e+03 | 3.42 | 19 | yes | -| fargate | 3e+03 | PerQuery-CostAware | 1.08 | 2.97e+03 | 17.2 | 85.1 | yes | -| fargate | 3e+03 | AutoSketch-Adapted | 192 | 1.01e+03 | 4.09e+03 | 5.88e+03 | yes | -| fargate | 1e+04 | ASAP | 0.223 | 2.94e+03 | 3.42 | 19 | yes | -| fargate | 1e+04 | PerQuery-CostAware | 1.08 | 2.97e+03 | 17.2 | 85.1 | yes | -| fargate | 1e+04 | AutoSketch-Adapted | 192 | 1.01e+03 | 4.09e+03 | 5.88e+03 | yes | - -## mixed, m = 8: 400 RQEs - -Sanity violations: 0. Tightest feasible SLA: ASAP 92 ms, PerQuery 92 ms. AutoSketch latency 1.01e+03 ms; planning time (search + measured benchmark) 1.58e+03 s. - -| weights | SLA (ms) | method | cost | latency (ms) | CPU (vCPU) | GiB | meets SLA | -|---|---|---|---|---|---|---|---| -| cpu | 100 | ASAP | 276 | 98.1 | 276 | 268 | yes | -| cpu | 100 | PerQuery-CostAware | 473 | 98.1 | 473 | 1.36e+03 | yes | -| cpu | 100 | AutoSketch-Adapted | 2.73e+04 | 1.01e+03 | 2.73e+04 | 4.52e+04 | **no** | -| cpu | 300 | ASAP | 98.9 | 295 | 98.9 | 131 | yes | -| cpu | 300 | PerQuery-CostAware | 189 | 295 | 189 | 547 | yes | -| cpu | 300 | AutoSketch-Adapted | 2.73e+04 | 1.01e+03 | 2.73e+04 | 4.52e+04 | **no** | -| cpu | 1e+03 | ASAP | 41 | 982 | 41 | 251 | yes | -| cpu | 1e+03 | PerQuery-CostAware | 96.2 | 982 | 96.2 | 1.26e+03 | yes | -| cpu | 1e+03 | AutoSketch-Adapted | 2.73e+04 | 1.01e+03 | 2.73e+04 | 4.52e+04 | **no** | -| cpu | 3e+03 | ASAP | 26.6 | 2.94e+03 | 26.6 | 259 | yes | -| cpu | 3e+03 | PerQuery-CostAware | 77.9 | 2.94e+03 | 77.9 | 1.29e+03 | yes | -| cpu | 3e+03 | AutoSketch-Adapted | 2.73e+04 | 1.01e+03 | 2.73e+04 | 4.52e+04 | yes | -| cpu | 1e+04 | ASAP | 26.6 | 2.94e+03 | 26.6 | 259 | yes | -| cpu | 1e+04 | PerQuery-CostAware | 76.4 | 3.84e+03 | 76.4 | 1.29e+03 | yes | -| cpu | 1e+04 | AutoSketch-Adapted | 2.73e+04 | 1.01e+03 | 2.73e+04 | 4.52e+04 | yes | -| fargate | 100 | ASAP | 12 | 98.1 | 280 | 149 | yes | -| fargate | 100 | PerQuery-CostAware | 22.7 | 98.1 | 494 | 602 | yes | -| fargate | 100 | AutoSketch-Adapted | 1.31e+03 | 1.01e+03 | 2.73e+04 | 4.52e+04 | **no** | -| fargate | 300 | ASAP | 4.59 | 295 | 99 | 129 | yes | -| fargate | 300 | PerQuery-CostAware | 10.1 | 295 | 190 | 541 | yes | -| fargate | 300 | AutoSketch-Adapted | 1.31e+03 | 1.01e+03 | 2.73e+04 | 4.52e+04 | **no** | -| fargate | 1e+03 | ASAP | 2.27 | 989 | 41.1 | 136 | yes | -| fargate | 1e+03 | PerQuery-CostAware | 6.5 | 989 | 99.1 | 559 | yes | -| fargate | 1e+03 | AutoSketch-Adapted | 1.31e+03 | 1.01e+03 | 2.73e+04 | 4.52e+04 | **no** | -| fargate | 3e+03 | ASAP | 1.76 | 2.94e+03 | 26.8 | 152 | yes | -| fargate | 3e+03 | PerQuery-CostAware | 5.76 | 2.97e+03 | 79.8 | 569 | yes | -| fargate | 3e+03 | AutoSketch-Adapted | 1.31e+03 | 1.01e+03 | 2.73e+04 | 4.52e+04 | yes | -| fargate | 1e+04 | ASAP | 1.76 | 2.94e+03 | 26.8 | 152 | yes | -| fargate | 1e+04 | PerQuery-CostAware | 5.76 | 2.97e+03 | 79.8 | 569 | yes | -| fargate | 1e+04 | AutoSketch-Adapted | 1.31e+03 | 1.01e+03 | 2.73e+04 | 4.52e+04 | yes | - -## mixed, m = 16: 800 RQEs - -Sanity violations: 0. Tightest feasible SLA: ASAP 92 ms, PerQuery 92 ms. 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"storage": 0.26404670619934656 - }, - "latency_ms": 1010.8325494137356, - "median_latency_ms": 1.1287128712871286, - "meets_sla": true, - "method": "autosketch", - "objective": 191.7546079526298, - "planning_secs": 0.003471374, - "probes": 296, - "rqes": 92, - "sla_ms": 10000.0, - "weights": "fargate" - } - ], - "streams": 8, - "weights": [ - { - "name": "cpu", - "w_cpu": 1.0, - "w_mem": 0.0 - }, - { - "name": "fargate", - "w_cpu": 0.0405, - "w_mem": 0.00445 - } - ], - "workload": "synthetic/templates=all/shared=8/tp95" -} diff --git a/rqe-optimizer/results/autosketch-vs-asap-synthetic/README.md b/rqe-optimizer/results/autosketch-vs-asap-synthetic/README.md deleted file mode 100644 index eecc39df..00000000 --- a/rqe-optimizer/results/autosketch-vs-asap-synthetic/README.md +++ /dev/null @@ -1,183 +0,0 @@ -# Synthetic workload: AutoSketch vs. ASAP - -Results of ProjectASAP/ASAPQuery#777's synthetic workload on the **mixed -template set** (the 10 templates, 50 RQEs), scaled by shared replicas r = 8 -(on one metric, 92 RQEs) and by m = 8 and 16 metrics (one copy of the -template set per metric, 50 · m RQEs). One accuracy level, p95: 95% in each -family's own metric (rank, relative value and relative error at most 0.05; -top-k precision at least 0.95). - -**Cost model: cost by use** (sketch-bench `docs/rqe_optimizer_cost_model.md`, -"Cost by use and batch latency", #188): `w_cpu · AUC(CPU) + w_mem · -AUC(memory)`, CPU elastic, at two weight settings (CPU only, in vCPU; AWS -Fargate's per-vCPU and per-GB prices, in $/hour). CPU covers ingest (on -`⌈ρ⌉` workers split by sample), compaction of each closed window, and one -query job (merge, then estimate) per firing; memory covers ingest, storage, -compaction and query memory, each counted once. - -**Latency is reported, not constrained (version 1, no SLA).** A plan's query -latency is its worst batch latency, which with elastic CPU is its longest -chain (the newest window's compaction, then the query). ASAP and -PerQuery-CostAware are solved without a bound (the cheapest plan) and for 12 -latency bounds log-spaced from the tightest feasible bound to the unbounded -plan's latency, plus a bound at AutoSketch's latency: each method's -**cost-latency frontier**. AutoSketch-Adapted ignores latency and is one -point. AutoSketch's planning time is its search **plus its measured -benchmark**: `scripts/autosketch_benchmark_time.py` ran approxbench's -accuracy benchmark (1e8 items, exact baseline and scoring) once per distinct -probed (config, data shape), serially on idle clnode155 -(`autosketch-benchmark-times.json`), and charged it once per probed -(metric, config). - -Runs: one workload per CloudLab node, all Intel Xeon E5-2683 v3 @ 2.00 GHz -with 56 cores (mixed on clnode155, r = 8 on clnode178, m = 8 on clnode167, -m = 16 on clnode138), 2026-10-08, `--runs 3`, -sketch-bench at #188 + this branch. No RQE was dropped and no sanity check -failed (ASAP ≤ PerQuery, ASAP ≤ AutoSketch, each frontier monotone). - -Files: `synthetic-*.json` (raw: every method's plan, unbounded and bounded, -per weight setting, without per-RQE choices; rerun with the runner for them), `summary_synthetic.md` (every point), -`fig_frontier.png` (cost vs. query latency per workload and weight setting; -ASAP and PerQuery frontiers as lines, AutoSketch as a point, every point -labeled), `fig_planning_time.png` (planning time vs. RQEs over m). - -## Results - -Each method's cheapest plan and its latency, and ASAP's and PerQuery's cost -at no more than AutoSketch's latency (absolute costs): - -| Workload | RQEs | Weights | ASAP: cheapest (latency) | PerQuery: cheapest (latency) | AutoSketch (latency) | ASAP at AutoSketch's latency | PerQuery at AutoSketch's latency | -|---|---|---|---|---|---|---|---| -| mixed (default) | 50 | cpu | 3.32 vCPU (1.21e+04 ms) | 9.55 vCPU (3.84e+03 ms) | 3.41e+03 vCPU (1.01e+03 ms) | 5.12 vCPU | 12 vCPU | -| mixed (default) | 50 | fargate | 0.22 $/h (2.94e+03 ms) | 0.72 $/h (2.97e+03 ms) | 163 $/h (1.01e+03 ms) | 0.284 $/h | 0.813 $/h | -| mixed, m = 8 | 400 | cpu | 26.5 vCPU (1.21e+04 ms) | 76.4 vCPU (3.84e+03 ms) | 2.73e+04 vCPU (1.01e+03 ms) | 41 vCPU | 96.2 vCPU | -| mixed, m = 8 | 400 | fargate | 1.76 $/h (2.94e+03 ms) | 5.76 $/h (2.97e+03 ms) | 1.31e+03 $/h (1.01e+03 ms) | 2.27 $/h | 6.5 $/h | -| mixed, m = 16 | 800 | cpu | 53.1 vCPU (1.21e+04 ms) | 153 vCPU (3.84e+03 ms) | 5.46e+04 vCPU (1.01e+03 ms) | 81.9 vCPU | 192 vCPU | -| mixed, m = 16 | 800 | fargate | 3.52 $/h (2.94e+03 ms) | 11.5 $/h (2.97e+03 ms) | 2.61e+03 $/h (1.01e+03 ms) | 4.54 $/h | 13 $/h | -| mixed, r = 8 | 92 | cpu | 3.39 vCPU (2.94e+03 ms) | 16.7 vCPU (3.84e+03 ms) | 4.09e+03 vCPU (1.01e+03 ms) | 5.15 vCPU | 19.3 vCPU | -| mixed, r = 8 | 92 | fargate | 0.223 $/h (2.94e+03 ms) | 1.08 $/h (2.97e+03 ms) | 192 $/h (1.01e+03 ms) | 0.285 $/h | 1.18 $/h | - -Planning time (CPU-only weights; AutoSketch's includes its measured -benchmark; the paper's 60 s per probe is a reference only): - -| RQEs | ASAP (candidates + MILP) | PerQuery-CostAware | AutoSketch: search | AutoSketch: search + measured benchmark | (reference: + 60 s per probe) | -|---|---|---|---|---|---| -| 50 | 0.758 s | 0.65 s | 0.00197 s | 212 s | 600 s | -| 400 | 6.82 s | 5.55 s | 0.0153 s | 1.7e+03 s | 4.8e+03 s | -| 800 | 14.7 s | 11.7 s | 0.031 s | 3.39e+03 s | 9.6e+03 s | - -## Template sets - -The results above are the 10-template mixed set, now named `classic` -(`--synthetic` writes it as `synthetic-templatesclassic-*.json`; the -committed `templatesall` tables and results predate the rename and hold -exactly today's `classic` tables). `all` is `classic` plus six -multi-grouping templates on two metrics with an ordered label schema. Each -metric value is one stream read at several groupings, so a fine deployment -can serve a coarse RQE (a roll-up) and one Hydra grid can serve them all. -Every RQE of these templates names its `grouping` and `covers_share` τ: the -accuracy target covers the groups holding at least τ of the stream's records, -plus always the largest group (`null`: every group). The generator computes -each group's share as the product of its labels' shares (labels are drawn -independently given their parents) and writes each RQE's `covered_groups`, -`min_covered_share` and `covered_min_N` (the smallest covered group's items -per window), and `max_covered_share` and `covered_max_N` (the largest's); the table's `schemas` gives each metric's labels. - -| Metric | Labels (values, Zipf skew) | Rate | Values | -|---|---|---|---| -| `http` | region 4 (0.5), service 25 (1.1), endpoint 25 per service (1.1; 625 in all), status 4 (2.0); 1e4 label combinations | 2e6 samples/s (as `classic`) | `user_id`: Zipf 0.8 over 1e6; `latency`: Pareto a = 2, scaled per service, one endpoint 10x slower | -| `flows` | dst_subnet 1e3 (1.1), dst_port 1e3 (1.2), proto 3 (uniform) | 4e6 samples/s | `src_ip`: uniform over 1e6, plus a DDoS burst: the least popular subnet (999) gets 5% of the traffic from 1e4 sources | - -`flows` runs at twice the rate so its finest grouping, {dst_subnet, -dst_port} (1e6 groups), holds 1,200 items per group in 5m, above the -curves' first N (1e3). The burst targets a tail subnet so that it is -visible: its share rises to just over 5%, so template 11 covers it. - -The runner reads each value's data shape (Zipf θ and K, or the Pareto a), the -groups per grouping and the coverage; the other fields (label skew beyond the -shares, latency scaling, the anomaly, the burst's sources) describe the data -and are not modeled. Accuracy is read at N items per group per window at -both the smallest covered group (`covered_min_N`; for `covers_share` = -`null` the smallest group of all) and the largest (`covered_max_N`), and -the worse of the two reads holds (unknown if either is): KLL, DD and HLL -error does not fall as N grows, so the largest group can be the hardest -(sketch-bench#189). The -generator fails if any RQE's `covered_min_N` is below the curves' first N -(1e3). The skew of the counted value is the same in every group; label skew -changes only the group sizes. (RQEs without a `grouping`, `classic`'s, are -read at the mean group's items, `max_N`, as before.) - -| # | Query | Groupings | Covers | Groups (mean items per group in 5m) | Covered groups (smallest covered group's items in 5m) | -|---|---|---|---|---|---| -| 11 | distinct src_ip | {dst_subnet} | share ≥ 5% | 1e3 (1.2e6) | 4, the burst subnet among them (6e7) | -| 12 | distinct src_ip | {dst_port}, {dst_subnet, proto}, {dst_port, proto} | share ≥ 5% | 1e3, 3e3, 3e3 (1.2e6, 4e5, 4e5) | 3, 3, 3 (7.4e7, 6.8e7, 9.2e7) | -| 14 | distinct user_id | {region}, {service}, {region, service}, {service, endpoint} | share ≥ 1% | 4, 25, 100, 625 (1.5e8 to 9.6e5) | 4, 21, 23, 16 (1.1e8, 6.3e6, 6.3e6, 6.3e6) | -| 15 | distinct user_id | all 15 non-empty subsets of http's labels | share ≥ 5% | 4 to 1e4 (1.5e8 to 6e4) | 1 to 5 (1.3e7 to 1.1e8) | -| 16 | p99 latency | {service}, {region, service}, {service, status} | all groups | 25, 100, 100 (2.4e7, 6e6, 6e6) | all (5.2e6, 9.3e5, 2.3e5) | -| 17 | distinct user_id | {service, endpoint} (negative control) | all groups | 625 (9.6e5) | all (4.5e4) | - -The plot scripts title `classic` "mixed" and `all` "mixed + multi-grouping" -(so the committed `templatesall` results, which are `classic`'s, need -regenerating under their new name before they are re-plotted). - -Each repeats every minute over 5m and 15m windows: 54 RQEs, so `all` has -104 (shared replicas repeat them at their own interval; each of the `m` -metrics has its own `data_i/http` and `data_i/flows`). Distinct counts are -HLL (Zipf θ and K of the value), the p99 KLL and DDSketch (Pareto a = 2), -at the p95 level. `covers_share` is carried through to each choice in the -runner's output. - -On a freshly generated `all` table (`--synthetic`; r = 1, p95, CPU -weights, `--runs 1`) with the committed saturation inputs (the committed -`templatesall` tables are `classic`'s), no RQE is dropped and no sanity -check fails; ASAP serves 40 of the 54 new RQEs from a finer deployment: -every RQE of templates 14, 15 and 17 from an HLL at {region, service, -status} or at the full label set (bar the RQEs at those two groupings), -{dst_port} from {dst_port, proto}, and the p99 by {service} from DDSketch -at {region, service} or {service, status}. 15 active deployments in all, -5.05 vCPU; PerQuery-CostAware 11.8 vCPU; AutoSketch 3.42e+03 vCPU. - -## Inputs - -Costs and accuracy come from one saturation-study directory `DIR`, the same -inputs the planner reads (#174, #178, #179). The one these results used is -committed in `../autosketch-vs-asap-inputs/saturation/`, and the tables in -`../autosketch-vs-asap-inputs/tables/`: -- `DIR/out_grid_1e7_cost/`: the accuracy grid to N = 1e7, including the - cost table's shape (Zipf 1.1 over 1e4 keys, Pareto a = 2), with merge - curves for the lossy sketches; -- `DIR/out_1e9/`: the targeted points past 1e7 that the workload reads; -- `DIR/optimizer_cost/rqe_atomic_costs.json`: the cost table. - -The tables from `export_autosketch_eval_table.py --synthetic` hold only the -workload: RQEs, streams, targets and data shapes. - -## Reproduce - -```sh -cargo build -p aqpbm-cli --release -# Accuracy grid with merge curves (parallel), then the targeted 1e9 points. -python3 scripts/study_saturation.py --phase accuracy --n-max 1e7 --seeds 3 \ - --jobs 48 --merge-shards-list 1,4,16,64 --out DIR/out_grid_1e7_cost -python3 scripts/study_saturation.py --phase accuracy --n-max 1e9 --seeds 3 \ - --jobs 16 --points-from POINTS.csv --out DIR/out_1e9 -# Cost table, serially on a quiet machine. -python3 scripts/study_saturation.py --phase optimizer-cost --out DIR/optimizer_cost - -# Workload tables and plan.tsv, then the runs. -python3 scripts/export_autosketch_eval_table.py --synthetic --keep-duplicate-queries --out TABLES -# Or use the committed inputs: TABLES=rqe-optimizer/results/autosketch-vs-asap-inputs/tables, -# DIR=rqe-optimizer/results/autosketch-vs-asap-inputs/saturation. -SATURATION_DIR=DIR scripts/run_autosketch_vs_asap_synthetic.sh TABLES run -# AutoSketch's measured benchmark (serially, on an idle machine; writes -# benchmark_secs_measured into the results), then the figures. -python3 scripts/autosketch_benchmark_time.py --binary ./target/release/approxbench \ - --out OUT/autosketch-benchmark-times.json OUT/synthetic-*.json -scripts/run_autosketch_vs_asap_synthetic.sh TABLES plot -``` - -`--merge-shards-list` and `POINTS.csv` follow the workloads' queries: the -merge counts their KLL deployments can reach (`L / x`), and the points whose -items per instance exceed 1e7. These are the synthetic workload's (Zipf 1.1 -over 1e4 keys, Pareto a = 2) and alibaba's quantiles (Pareto a = 3, up to -1.6e8 items). diff --git a/rqe-optimizer/results/autosketch-vs-asap-synthetic/autosketch-benchmark-times.json b/rqe-optimizer/results/autosketch-vs-asap-synthetic/autosketch-benchmark-times.json deleted file mode 100644 index 1bab328d..00000000 --- a/rqe-optimizer/results/autosketch-vs-asap-synthetic/autosketch-benchmark-times.json +++ /dev/null @@ -1,13 +0,0 @@ -{ - "n": 100000000.0, - "secs": { - "[\"cms-heap-topk-fastpath-vector2d\", {\"cols\": 1024, \"heap\": 32, \"rows\": 3}, 1.1, 10000.0, null]": 32.40410717698978, - "[\"cms-heap-topk-fastpath-vector2d\", {\"cols\": 256, \"heap\": 32, \"rows\": 3}, 1.1, 10000.0, null]": 32.35026514204219, - "[\"cms-heap-topk-fastpath-vector2d\", {\"cols\": 4096, \"heap\": 32, \"rows\": 5}, 1.1, 10000.0, null]": 37.245747498993296, - "[\"dd\", {\"alpha\": 0.01}, null, 0.0, 2.0]": 21.549985619960353, - "[\"dd\", {\"alpha\": 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zDqhV#K%*B*M%lwMR!%x4`UeEatE#?#9M63pd3OASQBY9R(9jUm2NMgc1GIz*Kw{p- z$G^DVk};k2!4vdE2RKLE06h0Qm}&e+ClF;R|B-jxHpE1HSM8bY&F~i$&9|QUWIAoY z58wsqaTynv_};yHE8~?#wE%NvU~uBXmXZRIa|^ZVr>4@Q5-v#T0QCJ9CdF{3m9lb_ z$kBZCK%NE{G!H5hn*8b*4W-vdP~BG>hrV}(Eb=qNWXQKNAYcDtIm!!+pq_dPaE$sh zVc&-`UTuXUxv!s}3B2uj2h;aP{@`jP_J}~3kq6hMJ?_B$Py{5YfC=2(#TL%X$@UhEQqGyKlpNE z^KJ+1Y&}0My;1p3M~^42c4?RoGt}rTWP~)K=8!~ z0q2idn4Zt7UKvE|VfK&dx~(wf>()_%AeV$V2bI@in`tprEQj|okUqIuI7bBq6%zlC zp9rr%6r|!z)_xTnf?W?G1w|03-dVkiV+{-EoDNvS{h=rVWv)v#&Rf_BuucDo$KvNZ z1fZ8I?FfQFz%>1j2c&}s{2y->181oJ^X)4 **Performance monitoring.** [workload as in the draft] Without a latency -> target, LiteOlly keeps one DDSketch per series in 15 s tumbling windows and -> answers every lookback, 1 min to 24 h, by merging 4 to 5,760 windows -> (0.10 vCPU). Two changes an -> operator makes without thinking about synopses change that plan. First, -> refreshing the 24 h dashboard every minute instead of every 30 min makes -> the merges dominate: LiteOlly adds a 2 min window, sliding every minute, -> for all lookbacks of 10 min and up (0.22 vCPU). Second, a -> 1 s latency target on the dashboards gives the 6 h and 24 h queries a -> 30 min tumbling MV, while the 30 min query stays on the 15 s one. Less -> obviously, lower sample rates lead to *more* MVs: at 20 samples/s per -> series ingest is cheap enough that LiteOlly maintains three per-series -> window sizes (15 s, 1 min, 30 min) to save merges, while at 200 samples/s -> one suffices. -> -> **Anomaly detection.** [workload as in the draft] LiteOlly keeps one -> DDSketch per (region, service, status) group in 15 s windows and rolls it up -> for four queries; the two endpoint groupings get their own MVs (3 MVs, -> 0.11 vCPU). When the operator adds a seventh query, a drill-down by all -> four labels, the plan gets *cheaper*: one MV at the finest grouping now -> serves all seven queries and ingest is paid once (0.05 vCPU). This sharing has a limit LiteOlly finds on its own: with 100 -> endpoints per service the finest grouping has 40,000 groups, and merging -> them every 15 s for the per-service and per-region queries costs more than -> a second MV, so LiteOlly splits those queries off to a (service, region) MV. - -## Fargate prices - -The result files now hold both weight settings (rerun without `--weights cpu`; -the CPU-only results are unchanged, cost for cost). With memory priced -(AWS Fargate), three plans differ from the CPU-only ones: - -- **Performance monitoring, no SLA:** 3 MVs, 0.0075 $/h. The 6h and 24h - queries share a 30 min tumbling MV, and the 1m–30m queries stay on 15 s; - CPU-only keeps all six on 15 s (18 GiB). A 1 s SLA therefore no longer - changes the plan. At 100 ms the planner moves 10m/30m to 2 min windows - sliding every minute and 6h/24h to 1 h windows sliding every 30 min (4 MVs, - 0.0105 $/h). -- **Performance monitoring, 15 s refresh of the long dashboard:** a 2 min - window sliding every 15 s serves 10m–24h, as under CPU weights (0.106 $/h; - LiteOllyPerQuery 0.140, AutoSketch 29.6). -- **Anomaly detection:** the same plans as CPU-only (3 MVs for the six - queries; with the drill-down, 1 MV at 25 endpoints and 2 at 100). - - -- CPU weights only: memory is free, so the base performance plan holds - 18 GiB of 15 s windows. Fargate weights would favor coarser windows. -- The drill-down covers groups holding ≥ 1% of requests: at every group, the - smallest (region, service, endpoint, status) groups fall below the - saturation curves' first N (1e3 items) and the query is unservable. -- The anomaly metric is named `requests`, not `http`, so no Hydra grid (whose - accuracy is measured at `http`'s schema only) is a candidate. -- Sweep grids stop where a query falls below the curves: per-series rates - below 20/s, anomaly ingest below 2.5e5/s, and 16 statuses at most (status - has Zipf skew 2.0, so its tail groups are tiny), and series from 1e4 (at - 1e3 the per-job instant queries fall below the curves). -- One run (`--runs 1`); costs are deterministic, planning times are not - reported. - -## Files - -| File | What it is | -|---|---| -| `inputs/tables/--.json` | one table per sweep point (`scripts/case_study_tables.py`); `base-0` is the draft's workload | -| `inputs/tables/plan.tsv` | each table and its SLA grid (ms) | -| `---tp95.json` | the runner's result, with each plan's `chosen` | -| `summary.md` | every table: ASAP's cost, MVs and latency with no SLA and per SLA, and each distinct plan (`scripts/summarize_case_studies.py`) | - -## Reproduce - -```sh -cargo build --release -p rqe-optimizer --examples -DIR=rqe-optimizer/results/case-studies -SAT=$(mktemp -d); cp -r rqe-optimizer/results/autosketch-vs-asap-inputs/saturation/out_* $SAT/ -cp -r rqe-optimizer/results/autosketch-vs-asap-hydra/inputs/saturation/* $SAT/ -python3 scripts/case_study_tables.py --out $DIR/inputs/tables -while IFS=$'\t' read -r table slas; do - target/release/examples/autosketch_vs_asap synthetic --table $DIR/inputs/tables/$table \ - --target p95 --runs 1 --slas-ms $slas --saturation-dir $SAT \ - --out $DIR/${table%.json}-tp95.json -done < $DIR/inputs/tables/plan.tsv -python3 scripts/summarize_case_studies.py $DIR/*-tp95.json > $DIR/summary.md -``` - -The whole sweep (38 tables) plans in under a minute. diff --git a/rqe-optimizer/results/case-studies/anomaly-base-0-tp95.json b/rqe-optimizer/results/case-studies/anomaly-base-0-tp95.json deleted file mode 100644 index 3abfd8ae..00000000 --- a/rqe-optimizer/results/case-studies/anomaly-base-0-tp95.json +++ /dev/null @@ -1,15928 +0,0 @@ -{ - "autosketch": { - "benchmark_secs_paper_rate": 180.0, - "distinct_probes": 3, - "probed_configs": [ - { - "distinct_keys": 0.0, - "metric": "requests", - "params": { - "alpha": 0.1 - }, - "sketch": "dd", - "tail_index": 2.0, - "zipf_s": null - }, - { - "distinct_keys": 0.0, - "metric": "requests", - "params": { - "k": 25 - }, - "sketch": "kll-percall", - "tail_index": 2.0, - "zipf_s": null - }, - { - "distinct_keys": 0.0, - "metric": "requests", - "params": { - "k": 50 - }, - "sketch": "kll-percall", - "tail_index": 2.0, - "zipf_s": null - } - ], - "probes": 18, - "search_secs": 0.000316186 - }, - "cost_model": "w_cpu * AUC(CPU) + w_mem * AUC(memory), billed by use; latency = longest chain, reported", - "dropped_unservable": { - "asap": [], - "autosketch": [] - }, - "excluded_high_cardinality": [], - "frontier": { - "asap_norollup_tightest_bound_ms": 0.297029702970297, - "asap_tightest_bound_ms": 0.297029702970297, - "autosketch_latency_ms": 0.297029702970297, - "perquery_tightest_bound_ms": 0.297029702970297, - 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"sketch": "dd", - "slide_secs": 0.005, - "window_secs": 0.005 - }, - { - "accuracy_source": "Measured", - "asap_accuracy": 0.04879801440596865, - "deployment": 1, - "ingest_workers": 1.0, - "latency_ms": 0.001188118811881188, - "merged_shards": 1, - "params": { - "alpha": 0.1 - }, - "rqe": "synthetic/case/perf/series=30000/quantile_by_job_p0.9/values/0.005s", - "sketch": "dd", - "slide_secs": 0.005, - "window_secs": 0.005 - }, - { - "accuracy_source": "Measured", - "asap_accuracy": 0.04849697375156901, - "deployment": 2, - "ingest_workers": 1.0, - "latency_ms": 40.76435643564356, - "merged_shards": 4, - "params": { - "alpha": 0.1 - }, - "rqe": "synthetic/case/perf/series=30000/quantile_over_time_p0.5/values/60s", - "sketch": "dd", - "slide_secs": 15, - "window_secs": 15 - }, - { - "accuracy_source": "Measured", - "asap_accuracy": 0.04849697375156901, - "deployment": 5, - "ingest_workers": 1.0, - "latency_ms": 40.76435643564356, - "merged_shards": 4, - "params": { - "alpha": 0.1 - }, - "rqe": "synthetic/case/perf/series=30000/quantile_over_time_p0.9/values/60s", - "sketch": "dd", - "slide_secs": 15, - "window_secs": 15 - }, - { - "accuracy_source": "Measured", - "asap_accuracy": 0.04825214331846647, - "deployment": 8, - "ingest_workers": 1.0, - "latency_ms": 115.16435643564358, - "merged_shards": 10, - "params": { - "alpha": 0.1 - }, - "rqe": "synthetic/case/perf/series=30000/quantile_over_time_p0.9/values/600s", - "sketch": "dd", - "slide_secs": 60, - "window_secs": 60 - }, - { - "accuracy_source": "Measured", - "asap_accuracy": 0.048337479744294316, - "deployment": 12, - "ingest_workers": 1.0, - "latency_ms": 363.1643564356436, - "merged_shards": 30, - "params": { - "alpha": 0.1 - }, - "rqe": "synthetic/case/perf/series=30000/quantile_over_time_p0.9/values/1800s", - "sketch": "dd", - "slide_secs": 60, - "window_secs": 60 - }, - { - "accuracy_source": "Measured", - "asap_accuracy": 0.048400135938572286, - "deployment": 20, - "ingest_workers": 1.0, - "latency_ms": 139.9643564356436, - "merged_shards": 12, - "params": { - "alpha": 0.1 - }, - "rqe": "synthetic/case/perf/series=30000/quantile_over_time_p0.9/values/21600s", - "sketch": "dd", - "slide_secs": 1800, - "window_secs": 1800 - }, - { - "accuracy_source": "Measured", - "asap_accuracy": 0.04837709996663131, - "deployment": 26, - "ingest_workers": 1.0, - "latency_ms": 586.3643564356437, - "merged_shards": 48, - "params": { - "alpha": 0.1 - }, - "rqe": "synthetic/case/perf/series=30000/quantile_over_time_p0.9/values/86400s", - "sketch": "dd", - "slide_secs": 1800, - "window_secs": 1800 - } - ], - "cpu": 0.9176990671067108, - "cpu_parts": { - "compaction": 0.0, - "ingest": 0.9038880000000001, - "query": 0.013811067106710673 - }, - "gib": 1.0703466539994837, - "gib_parts": { - "compaction": 0.0, - "ingest": 0.05633264780044556, - "query": 0.0001327400819421208, - "storage": 1.013881266117096 - }, - "infeasible": false, - "latency_ms": 586.3643564356437, - "median_latency_ms": 115.16435643564358, - "method": "perquery", - "milp_solve_secs": 0.002951584, - "objective": 0.04192985482811949, - "rolled_up_rqes": 0, - "rqes": 8, - "sla_ms": 10000.0, - "weights": "fargate" - }, - { - "active_deployments": 8, - "benchmark_secs_paper_rate": 180.0, - "chosen": [ - { - "accuracy_source": "Measured", - "asap_accuracy": 0.04879801440596865, - "deployment": 0, - "ingest_workers": 1.0, - "latency_ms": 0.001188118811881188, - "merged_shards": 1, - "params": { - "alpha": 0.1 - }, - "rqe": "synthetic/case/perf/series=30000/quantile_by_job_p0.5/values/0.005s", - "sketch": "dd", - "slide_secs": 0.005, - "window_secs": 0.005 - }, - { - "accuracy_source": "Measured", - "asap_accuracy": 0.04879801440596865, - "deployment": 1, - "ingest_workers": 1.0, - "latency_ms": 0.001188118811881188, - "merged_shards": 1, - "params": { - "alpha": 0.1 - }, - "rqe": "synthetic/case/perf/series=30000/quantile_by_job_p0.9/values/0.005s", - "sketch": "dd", - "slide_secs": 0.005, - "window_secs": 0.005 - }, - { - "accuracy_source": "Measured", - "asap_accuracy": 0.04849697375156901, - "deployment": 2, - "ingest_workers": 1.0, - "latency_ms": 3.564356435643564, - "merged_shards": 1, - "params": { - "alpha": 0.1 - }, - "rqe": "synthetic/case/perf/series=30000/quantile_over_time_p0.5/values/60s", - "sketch": "dd", - "slide_secs": 15, - "window_secs": 60 - }, - { - "accuracy_source": "Measured", - "asap_accuracy": 0.04849697375156901, - "deployment": 3, - "ingest_workers": 1.0, - "latency_ms": 3.564356435643564, - "merged_shards": 1, - "params": { - "alpha": 0.1 - }, - "rqe": "synthetic/case/perf/series=30000/quantile_over_time_p0.9/values/60s", - "sketch": "dd", - "slide_secs": 15, - "window_secs": 60 - }, - { - "accuracy_source": "Measured", - "asap_accuracy": 0.04825214331846647, - "deployment": 4, - "ingest_workers": 2.0, - "latency_ms": 15.964356435643566, - "merged_shards": 1, - "params": { - "alpha": 0.1 - }, - "rqe": "synthetic/case/perf/series=30000/quantile_over_time_p0.9/values/600s", - "sketch": "dd", - "slide_secs": 60, - "window_secs": 600 - }, - { - "accuracy_source": "Measured", - "asap_accuracy": 0.048337479744294316, - "deployment": 5, - "ingest_workers": 4.0, - "latency_ms": 40.76435643564356, - "merged_shards": 1, - "params": { - "alpha": 0.1 - }, - "rqe": "synthetic/case/perf/series=30000/quantile_over_time_p0.9/values/1800s", - "sketch": "dd", - "slide_secs": 60, - "window_secs": 1800 - }, - { - "accuracy_source": "Measured", - "asap_accuracy": 0.048400135938572286, - "deployment": 6, - "ingest_workers": 2.0, - "latency_ms": 15.964356435643566, - "merged_shards": 1, - "params": { - "alpha": 0.1 - }, - "rqe": "synthetic/case/perf/series=30000/quantile_over_time_p0.9/values/21600s", - "sketch": "dd", - "slide_secs": 1800, - "window_secs": 21600 - }, - { - "accuracy_source": "Measured", - "asap_accuracy": 0.04837709996663131, - "deployment": 7, - "ingest_workers": 6.0, - "latency_ms": 65.56435643564357, - "merged_shards": 1, - "params": { - "alpha": 0.1 - }, - "rqe": "synthetic/case/perf/series=30000/quantile_over_time_p0.9/values/86400s", - "sketch": "dd", - "slide_secs": 1800, - "window_secs": 86400 - } - ], - "cpu": 12.429926178217823, - "cpu_parts": { - "compaction": 0.0008680000000000001, - "ingest": 12.428460000000001, - "query": 0.0005981782178217821 - }, - "gib": 4.374724785242942, - "gib_parts": { - "compaction": 0.000029231309890747078, - "ingest": 4.318362772464752, - "query": 1.3366785379919676e-7, - "storage": 0.05633264780044556 - }, - "latency_ms": 65.56435643564357, - "median_latency_ms": 15.964356435643566, - "meets_sla": true, - "method": "autosketch", - "objective": 0.5228795355121529, - "planning_secs": 0.000463067, - "probes": 24, - "rolled_up_rqes": 0, - "rqes": 8, - "sla_ms": 10000.0, - "weights": "fargate" - } - ], - "streams": 2, - "weights": [ - { - "name": "cpu", - "w_cpu": 1.0, - "w_mem": 0.0 - }, - { - "name": "fargate", - "w_cpu": 0.0405, - "w_mem": 0.00445 - } - ], - "workload": "synthetic/case/perf/series=30000/tp95" -} \ No newline at end of file diff --git a/rqe-optimizer/results/case-studies/summary.md b/rqe-optimizer/results/case-studies/summary.md deleted file mode 100644 index 8772de0a..00000000 --- a/rqe-optimizer/results/case-studies/summary.md +++ /dev/null @@ -1,1425 +0,0 @@ -## synthetic/case/anomaly/base=0/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.114 | 3 | 3.31 | -| 0.3 | 0.377 | 6 | 0.297 | -| 0.5 | 0.339 | 4 | 0.412 | -| 1.0 | 0.301 | 4 | 0.825 | -| 2.0 | 0.151 | 4 | 1.11 | -| 3.0 | 0.151 | 4 | 1.11 | - -Plan at SLA none: -- dd(alpha=0.1) by (region,service,status) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20), p99_by_service,region,status[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 0.3: -- dd(alpha=0.1) by (service) window 15s slide 15s: p99_by_service[5m] (merges 20) -- dd(alpha=0.1) by (region) window 15s slide 15s: p99_by_region[5m] (merges 20) -- dd(alpha=0.1) by (service,region) window 60s slide 60s: p99_by_service,region[5m] (merges 5) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 60s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 0.5: -- dd(alpha=0.1) by (region,service) window 30s slide 15s: p99_by_service[5m] (merges 10), p99_by_region[5m] (merges 10), p99_by_service,region[5m] (merges 10) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 60s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 1.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 60s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 2.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -## synthetic/case/anomaly/drilldown_endpoints=100/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.0776 | 2 | 82.5 | -| 3.0 | infeasible | | | -| 10.0 | 0.151 | 4 | 4.75 | -| 30.0 | 0.113 | 3 | 16.4 | -| 100.0 | 0.0776 | 2 | 82.5 | - -Plan at SLA none: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (endpoint,region,service,status) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5), p99_by_service,region,status[5m] (merges 5), p99_by_region,endpoint[5m] (merges 5), p99_by_region,service,endpoint,status[5m] (merges 5) - -Plan at SLA 10.0: -- dd(alpha=0.1) by (region,service,status) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20), p99_by_service,region,status[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (region,service,endpoint,status) window 300s slide 300s: p99_by_region,service,endpoint,status[5m] (merges 1) - -Plan at SLA 30.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (endpoint,region,service,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1), p99_by_region,endpoint[5m] (merges 1), p99_by_region,service,endpoint,status[5m] (merges 1) - -## synthetic/case/anomaly/drilldown_endpoints=10/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.0435 | 1 | 33.1 | -| 3.0 | 0.113 | 3 | 1.54 | -| 10.0 | 0.0757 | 2 | 8.19 | -| 30.0 | 0.0757 | 2 | 8.19 | -| 100.0 | 0.0435 | 1 | 33.1 | - -Plan at SLA none: -- dd(alpha=0.1) by (endpoint,region,service,status) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20), p99_by_service,endpoint[5m] (merges 20), p99_by_service,region,status[5m] (merges 20), p99_by_region,endpoint[5m] (merges 20), p99_by_region,service,endpoint,status[5m] (merges 20) - -Plan at SLA 3.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (endpoint,region,service,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1), p99_by_region,endpoint[5m] (merges 1), p99_by_region,service,endpoint,status[5m] (merges 1) - -Plan at SLA 10.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (endpoint,region,service,status) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5), p99_by_service,region,status[5m] (merges 5), p99_by_region,endpoint[5m] (merges 5), p99_by_region,service,endpoint,status[5m] (merges 5) - -## synthetic/case/anomaly/drilldown_endpoints=25/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.0522 | 1 | 82.7 | -| 3.0 | 0.188 | 5 | 1.19 | -| 10.0 | 0.113 | 3 | 4.02 | -| 30.0 | 0.076 | 2 | 20.5 | -| 100.0 | 0.0522 | 1 | 82.7 | - -Plan at SLA none: -- dd(alpha=0.1) by (endpoint,region,service,status) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20), p99_by_service,endpoint[5m] (merges 20), p99_by_service,region,status[5m] (merges 20), p99_by_region,endpoint[5m] (merges 20), p99_by_region,service,endpoint,status[5m] (merges 20) - -Plan at SLA 3.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (region,service,endpoint,status) window 300s slide 300s: p99_by_region,service,endpoint,status[5m] (merges 1) - -Plan at SLA 10.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (endpoint,region,service,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1), p99_by_region,endpoint[5m] (merges 1), p99_by_region,service,endpoint,status[5m] (merges 1) - -Plan at SLA 30.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (endpoint,region,service,status) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5), p99_by_service,region,status[5m] (merges 5), p99_by_region,endpoint[5m] (merges 5), p99_by_region,service,endpoint,status[5m] (merges 5) - -## synthetic/case/anomaly/drilldown_endpoints=2/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.0388 | 1 | 6.61 | -| 3.0 | 0.0755 | 2 | 1.64 | -| 10.0 | 0.0388 | 1 | 6.61 | -| 30.0 | 0.0388 | 1 | 6.61 | -| 100.0 | 0.0388 | 1 | 6.61 | - -Plan at SLA none: -- dd(alpha=0.1) by (endpoint,region,service,status) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20), p99_by_service,endpoint[5m] (merges 20), p99_by_service,region,status[5m] (merges 20), p99_by_region,endpoint[5m] (merges 20), p99_by_region,service,endpoint,status[5m] (merges 20) - -Plan at SLA 3.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (endpoint,region,service,status) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5), p99_by_service,region,status[5m] (merges 5), p99_by_region,endpoint[5m] (merges 5), p99_by_region,service,endpoint,status[5m] (merges 5) - -## synthetic/case/anomaly/drilldown_endpoints=50/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.0668 | 1 | 165 | -| 3.0 | 0.188 | 5 | 2.38 | -| 10.0 | 0.113 | 3 | 8.15 | -| 30.0 | 0.113 | 3 | 8.15 | -| 100.0 | 0.0765 | 2 | 41.2 | - -Plan at SLA none: -- dd(alpha=0.1) by (endpoint,region,service,status) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20), p99_by_service,endpoint[5m] (merges 20), p99_by_service,region,status[5m] (merges 20), p99_by_region,endpoint[5m] (merges 20), p99_by_region,service,endpoint,status[5m] (merges 20) - -Plan at SLA 3.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (region,service,endpoint,status) window 300s slide 300s: p99_by_region,service,endpoint,status[5m] (merges 1) - -Plan at SLA 10.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (endpoint,region,service,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1), p99_by_region,endpoint[5m] (merges 1), p99_by_region,service,endpoint,status[5m] (merges 1) - -Plan at SLA 100.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (endpoint,region,service,status) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5), p99_by_service,region,status[5m] (merges 5), p99_by_region,endpoint[5m] (merges 5), p99_by_region,service,endpoint,status[5m] (merges 5) - -## synthetic/case/anomaly/drilldown_endpoints=5/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.0406 | 1 | 16.5 | -| 3.0 | 0.113 | 3 | 0.825 | -| 10.0 | 0.0756 | 2 | 4.1 | -| 30.0 | 0.0406 | 1 | 16.5 | -| 100.0 | 0.0406 | 1 | 16.5 | - -Plan at SLA none: -- dd(alpha=0.1) by (endpoint,region,service,status) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20), p99_by_service,endpoint[5m] (merges 20), p99_by_service,region,status[5m] (merges 20), p99_by_region,endpoint[5m] (merges 20), p99_by_region,service,endpoint,status[5m] (merges 20) - -Plan at SLA 3.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (endpoint,region,service,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1), p99_by_region,endpoint[5m] (merges 1), p99_by_region,service,endpoint,status[5m] (merges 1) - -Plan at SLA 10.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (endpoint,region,service,status) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5), p99_by_service,region,status[5m] (merges 5), p99_by_region,endpoint[5m] (merges 5), p99_by_region,service,endpoint,status[5m] (merges 5) - -## synthetic/case/anomaly/endpoints=100/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.114 | 3 | 4.43 | -| 0.3 | infeasible | | | -| 0.5 | infeasible | | | -| 1.0 | infeasible | | | -| 2.0 | 0.301 | 4 | 1.19 | -| 3.0 | 0.301 | 4 | 1.19 | - -Plan at SLA none: -- dd(alpha=0.1) by (region,service,status) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20), p99_by_service,region,status[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 2.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 60s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -## synthetic/case/anomaly/endpoints=10/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.113 | 3 | 3.31 | -| 0.3 | 0.377 | 6 | 0.199 | -| 0.5 | 0.188 | 4 | 0.443 | -| 1.0 | 0.151 | 4 | 0.825 | -| 2.0 | 0.151 | 4 | 0.825 | -| 3.0 | 0.151 | 4 | 0.825 | - -Plan at SLA none: -- dd(alpha=0.1) by (endpoint,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_service,endpoint[5m] (merges 20) -- dd(alpha=0.1) by (region,service,status) window 15s slide 15s: p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20), p99_by_service,region,status[5m] (merges 20) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 0.3: -- dd(alpha=0.1) by (service) window 15s slide 15s: p99_by_service[5m] (merges 20) -- dd(alpha=0.1) by (region) window 15s slide 15s: p99_by_region[5m] (merges 20) -- dd(alpha=0.1) by (service,region) window 60s slide 60s: p99_by_service,region[5m] (merges 5) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 60s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 0.5: -- dd(alpha=0.1) by (region,service) window 30s slide 15s: p99_by_service[5m] (merges 10), p99_by_region[5m] (merges 10), p99_by_service,region[5m] (merges 10) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 1.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -## synthetic/case/anomaly/endpoints=25/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.114 | 3 | 3.31 | -| 0.3 | 0.377 | 6 | 0.297 | -| 0.5 | 0.339 | 4 | 0.412 | -| 1.0 | 0.301 | 4 | 0.825 | -| 2.0 | 0.151 | 4 | 1.11 | -| 3.0 | 0.151 | 4 | 1.11 | - -Plan at SLA none: -- dd(alpha=0.1) by (region,service,status) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20), p99_by_service,region,status[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 0.3: -- dd(alpha=0.1) by (service) window 15s slide 15s: p99_by_service[5m] (merges 20) -- dd(alpha=0.1) by (region) window 15s slide 15s: p99_by_region[5m] (merges 20) -- dd(alpha=0.1) by (service,region) window 60s slide 60s: p99_by_service,region[5m] (merges 5) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 60s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 0.5: -- dd(alpha=0.1) by (region,service) window 30s slide 15s: p99_by_service[5m] (merges 10), p99_by_region[5m] (merges 10), p99_by_service,region[5m] (merges 10) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 60s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 1.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 60s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 2.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -## synthetic/case/anomaly/endpoints=2/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.113 | 3 | 1.65 | -| 0.3 | 0.226 | 6 | 0.199 | -| 0.5 | 0.188 | 5 | 0.406 | -| 1.0 | 0.151 | 4 | 0.797 | -| 2.0 | 0.113 | 3 | 1.65 | -| 3.0 | 0.113 | 3 | 1.65 | - -Plan at SLA none: -- dd(alpha=0.1) by (endpoint,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_service,endpoint[5m] (merges 20) -- dd(alpha=0.1) by (endpoint,region) window 15s slide 15s: p99_by_region[5m] (merges 20), p99_by_region,endpoint[5m] (merges 20) -- dd(alpha=0.1) by (region,service,status) window 60s slide 60s: p99_by_service,region[5m] (merges 5), p99_by_service,region,status[5m] (merges 5) - -Plan at SLA 0.3: -- dd(alpha=0.1) by (service) window 15s slide 15s: p99_by_service[5m] (merges 20) -- dd(alpha=0.1) by (region) window 15s slide 15s: p99_by_region[5m] (merges 20) -- dd(alpha=0.1) by (service,region) window 60s slide 60s: p99_by_service,region[5m] (merges 5) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 0.5: -- dd(alpha=0.1) by (endpoint,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_service,endpoint[5m] (merges 20) -- dd(alpha=0.1) by (region) window 15s slide 15s: p99_by_region[5m] (merges 20) -- dd(alpha=0.1) by (service,region) window 60s slide 60s: p99_by_service,region[5m] (merges 5) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 1.0: -- dd(alpha=0.1) by (endpoint,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_service,endpoint[5m] (merges 20) -- dd(alpha=0.1) by (region) window 15s slide 15s: p99_by_region[5m] (merges 20) -- dd(alpha=0.1) by (region,service,status) window 60s slide 60s: p99_by_service,region[5m] (merges 5), p99_by_service,region,status[5m] (merges 5) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -## synthetic/case/anomaly/endpoints=50/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.114 | 3 | 3.31 | -| 0.3 | infeasible | | | -| 0.5 | infeasible | | | -| 1.0 | 0.301 | 4 | 0.825 | -| 2.0 | 0.301 | 4 | 0.825 | -| 3.0 | 0.151 | 4 | 2.22 | - -Plan at SLA none: -- dd(alpha=0.1) by (region,service,status) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20), p99_by_service,region,status[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 1.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 60s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 3.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -## synthetic/case/anomaly/endpoints=5/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.113 | 3 | 3.31 | -| 0.3 | 0.226 | 6 | 0.222 | -| 0.5 | 0.188 | 4 | 0.412 | -| 1.0 | 0.151 | 4 | 0.825 | -| 2.0 | 0.151 | 4 | 1.03 | -| 3.0 | 0.151 | 4 | 1.03 | - -Plan at SLA none: -- dd(alpha=0.1) by (endpoint,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_service,endpoint[5m] (merges 20) -- dd(alpha=0.1) by (region,service,status) window 15s slide 15s: p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20), p99_by_service,region,status[5m] (merges 20) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 0.3: -- dd(alpha=0.1) by (service) window 15s slide 15s: p99_by_service[5m] (merges 20) -- dd(alpha=0.1) by (region) window 15s slide 15s: p99_by_region[5m] (merges 20) -- dd(alpha=0.1) by (service,region) window 60s slide 60s: p99_by_service,region[5m] (merges 5) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 0.5: -- dd(alpha=0.1) by (region,service) window 30s slide 15s: p99_by_service[5m] (merges 10), p99_by_region[5m] (merges 10), p99_by_service,region[5m] (merges 10) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 1.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 2.0: -- dd(alpha=0.1) by (endpoint,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_service,endpoint[5m] (merges 20) -- dd(alpha=0.1) by (region) window 15s slide 15s: p99_by_region[5m] (merges 20) -- dd(alpha=0.1) by (region,service,status) window 60s slide 60s: p99_by_service,region[5m] (merges 5), p99_by_service,region,status[5m] (merges 5) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -## synthetic/case/anomaly/interval=15/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.116 | 3 | 19.9 | -| 0.3 | 2.03 | 4 | 0.297 | -| 0.5 | 1.51 | 3 | 0.333 | -| 1.0 | 1.09 | 3 | 0.849 | -| 2.0 | 0.603 | 3 | 1.65 | -| 3.0 | 0.528 | 3 | 2.4 | - -Plan at SLA none: -- dd(alpha=0.1) by (region,service,status) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20), p99_by_service,region,status[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 15s slide 15s: p99_by_service,endpoint[5m] (merges 20) -- dd(alpha=0.1) by (region,endpoint) window 15s slide 15s: p99_by_region,endpoint[5m] (merges 20) - -Plan at SLA 0.3: -- dd(alpha=0.1) by (region,service) window 60s slide 15s: p99_by_service[5m] (merges 5), p99_by_region[5m] (merges 5), p99_by_service,region[5m] (merges 5) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 15s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (service,region,status) window 150s slide 15s: p99_by_service,region,status[5m] (merges 2) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 15s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 0.5: -- dd(alpha=0.1) by (region,service,status) window 150s slide 15s: p99_by_service[5m] (merges 2), p99_by_region[5m] (merges 2), p99_by_service,region[5m] (merges 2), p99_by_service,region,status[5m] (merges 2) -- dd(alpha=0.1) by (service,endpoint) window 150s slide 15s: p99_by_service,endpoint[5m] (merges 2) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 15s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 1.0: -- dd(alpha=0.1) by (region,service,status) window 60s slide 15s: p99_by_service[5m] (merges 5), p99_by_region[5m] (merges 5), p99_by_service,region[5m] (merges 5), p99_by_service,region,status[5m] (merges 5) -- dd(alpha=0.1) by (service,endpoint) window 75s slide 15s: p99_by_service,endpoint[5m] (merges 4) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 15s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 2.0: -- dd(alpha=0.1) by (endpoint,service) window 60s slide 15s: p99_by_service[5m] (merges 5), p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (region,service,status) window 30s slide 15s: p99_by_region[5m] (merges 10), p99_by_service,region[5m] (merges 10), p99_by_service,region,status[5m] (merges 10) -- dd(alpha=0.1) by (region,endpoint) window 150s slide 15s: p99_by_region,endpoint[5m] (merges 2) - -Plan at SLA 3.0: -- dd(alpha=0.1) by (region,service,status) window 30s slide 15s: p99_by_service[5m] (merges 10), p99_by_region[5m] (merges 10), p99_by_service,region[5m] (merges 10), p99_by_service,region,status[5m] (merges 10) -- dd(alpha=0.1) by (service,endpoint) window 30s slide 15s: p99_by_service,endpoint[5m] (merges 10) -- dd(alpha=0.1) by (region,endpoint) window 150s slide 15s: p99_by_region,endpoint[5m] (merges 2) - -## synthetic/case/anomaly/interval=300/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.113 | 3 | 0.297 | -| 0.3 | 0.113 | 3 | 0.297 | -| 0.5 | 0.113 | 3 | 0.297 | -| 1.0 | 0.113 | 3 | 0.297 | -| 2.0 | 0.113 | 3 | 0.297 | -| 3.0 | 0.113 | 3 | 0.297 | - -Plan at SLA none: -- dd(alpha=0.1) by (region,service,status) window 300s slide 300s: p99_by_service[5m] (merges 1), p99_by_region[5m] (merges 1), p99_by_service,region[5m] (merges 1), p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 300s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -## synthetic/case/anomaly/interval=60/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.113 | 3 | 4.43 | -| 0.3 | 0.565 | 3 | 0.297 | -| 0.5 | 0.565 | 3 | 0.297 | -| 1.0 | 0.414 | 3 | 0.825 | -| 2.0 | 0.264 | 3 | 1.11 | -| 3.0 | 0.264 | 3 | 1.11 | - -Plan at SLA none: -- dd(alpha=0.1) by (region,service,status) window 60s slide 60s: p99_by_service[5m] (merges 5), p99_by_region[5m] (merges 5), p99_by_service,region[5m] (merges 5), p99_by_service,region,status[5m] (merges 5) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (region,endpoint) window 60s slide 60s: p99_by_region,endpoint[5m] (merges 5) - -Plan at SLA 0.3: -- dd(alpha=0.1) by (region,service,status) window 300s slide 60s: p99_by_service[5m] (merges 1), p99_by_region[5m] (merges 1), p99_by_service,region[5m] (merges 1), p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 60s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 60s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 1.0: -- dd(alpha=0.1) by (endpoint,service) window 300s slide 60s: p99_by_service[5m] (merges 1), p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (region,service,status) window 60s slide 60s: p99_by_region[5m] (merges 5), p99_by_service,region[5m] (merges 5), p99_by_service,region,status[5m] (merges 5) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 60s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 2.0: -- dd(alpha=0.1) by (region,service,status) window 60s slide 60s: p99_by_service[5m] (merges 5), p99_by_region[5m] (merges 5), p99_by_service,region[5m] (merges 5), p99_by_service,region,status[5m] (merges 5) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 60s: p99_by_region,endpoint[5m] (merges 1) - -## synthetic/case/anomaly/rate=20000000/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 1.13 | 3 | 3.31 | -| 0.3 | infeasible | | | -| 0.5 | 3.39 | 4 | 0.412 | -| 1.0 | 3.01 | 4 | 0.825 | -| 2.0 | 1.51 | 4 | 1.11 | -| 3.0 | 1.51 | 4 | 1.11 | - -Plan at SLA none: -- dd(alpha=0.1) by (region,service,status) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20), p99_by_service,region,status[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 0.5: -- dd(alpha=0.1) by (region,service) window 30s slide 15s: p99_by_service[5m] (merges 10), p99_by_region[5m] (merges 10), p99_by_service,region[5m] (merges 10) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 60s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 1.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 60s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 2.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -## synthetic/case/anomaly/rate=2000000/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.114 | 3 | 3.31 | -| 0.3 | 0.377 | 6 | 0.297 | -| 0.5 | 0.339 | 4 | 0.412 | -| 1.0 | 0.301 | 4 | 0.825 | -| 2.0 | 0.151 | 4 | 1.11 | -| 3.0 | 0.151 | 4 | 1.11 | - -Plan at SLA none: -- dd(alpha=0.1) by (region,service,status) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20), p99_by_service,region,status[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 0.3: -- dd(alpha=0.1) by (service) window 15s slide 15s: p99_by_service[5m] (merges 20) -- dd(alpha=0.1) by (region) window 15s slide 15s: p99_by_region[5m] (merges 20) -- dd(alpha=0.1) by (service,region) window 60s slide 60s: p99_by_service,region[5m] (merges 5) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 60s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 0.5: -- dd(alpha=0.1) by (region,service) window 30s slide 15s: p99_by_service[5m] (merges 10), p99_by_region[5m] (merges 10), p99_by_service,region[5m] (merges 10) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 60s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 1.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 60s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 2.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -## synthetic/case/anomaly/rate=250000/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.0146 | 3 | 3.31 | -| 0.3 | 0.0471 | 6 | 0.297 | -| 0.5 | 0.0424 | 4 | 0.412 | -| 1.0 | 0.0378 | 4 | 0.825 | -| 2.0 | 0.019 | 4 | 1.11 | -| 3.0 | 0.019 | 4 | 1.11 | - -Plan at SLA none: -- dd(alpha=0.1) by (region,service,status) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20), p99_by_service,region,status[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 0.3: -- dd(alpha=0.1) by (service) window 15s slide 15s: p99_by_service[5m] (merges 20) -- dd(alpha=0.1) by (region) window 15s slide 15s: p99_by_region[5m] (merges 20) -- dd(alpha=0.1) by (service,region) window 60s slide 60s: p99_by_service,region[5m] (merges 5) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 60s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 0.5: -- dd(alpha=0.1) by (region,service) window 30s slide 15s: p99_by_service[5m] (merges 10), p99_by_region[5m] (merges 10), p99_by_service,region[5m] (merges 10) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 60s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 1.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 60s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 2.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -## synthetic/case/anomaly/rate=500000/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.0288 | 3 | 3.31 | -| 0.3 | 0.0942 | 6 | 0.297 | -| 0.5 | 0.0848 | 4 | 0.412 | -| 1.0 | 0.0754 | 4 | 0.825 | -| 2.0 | 0.0378 | 4 | 1.11 | -| 3.0 | 0.0378 | 4 | 1.11 | - -Plan at SLA none: -- dd(alpha=0.1) by (region,service,status) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20), p99_by_service,region,status[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 0.3: -- dd(alpha=0.1) by (service) window 15s slide 15s: p99_by_service[5m] (merges 20) -- dd(alpha=0.1) by (region) window 15s slide 15s: p99_by_region[5m] (merges 20) -- dd(alpha=0.1) by (service,region) window 60s slide 60s: p99_by_service,region[5m] (merges 5) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 60s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 0.5: -- dd(alpha=0.1) by (region,service) window 30s slide 15s: p99_by_service[5m] (merges 10), p99_by_region[5m] (merges 10), p99_by_service,region[5m] (merges 10) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 60s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 1.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 60s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 2.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -## synthetic/case/anomaly/statuses=16/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.115 | 3 | 13.2 | -| 0.3 | 0.377 | 6 | 0.297 | -| 0.5 | 0.339 | 4 | 0.412 | -| 1.0 | 0.301 | 4 | 0.825 | -| 2.0 | 0.151 | 4 | 1.11 | -| 3.0 | 0.151 | 4 | 1.11 | - -Plan at SLA none: -- dd(alpha=0.1) by (endpoint,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_service,endpoint[5m] (merges 20) -- dd(alpha=0.1) by (region,service,status) window 15s slide 15s: p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20), p99_by_service,region,status[5m] (merges 20) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 0.3: -- dd(alpha=0.1) by (service) window 15s slide 15s: p99_by_service[5m] (merges 20) -- dd(alpha=0.1) by (region) window 15s slide 15s: p99_by_region[5m] (merges 20) -- dd(alpha=0.1) by (service,region) window 60s slide 60s: p99_by_service,region[5m] (merges 5) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 60s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 0.5: -- dd(alpha=0.1) by (region,service) window 30s slide 15s: p99_by_service[5m] (merges 10), p99_by_region[5m] (merges 10), p99_by_service,region[5m] (merges 10) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 60s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 1.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 60s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 2.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -## synthetic/case/anomaly/statuses=2/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.113 | 3 | 1.65 | -| 0.3 | 0.377 | 6 | 0.297 | -| 0.5 | 0.339 | 5 | 0.384 | -| 1.0 | 0.301 | 4 | 0.825 | -| 2.0 | 0.113 | 3 | 1.65 | -| 3.0 | 0.113 | 3 | 1.65 | - -Plan at SLA none: -- dd(alpha=0.1) by (region,service,status) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20), p99_by_service,region,status[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 0.3: -- dd(alpha=0.1) by (service) window 15s slide 15s: p99_by_service[5m] (merges 20) -- dd(alpha=0.1) by (region) window 15s slide 15s: p99_by_region[5m] (merges 20) -- dd(alpha=0.1) by (service,region) window 60s slide 60s: p99_by_service,region[5m] (merges 5) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 60s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 0.5: -- dd(alpha=0.1) by (service) window 15s slide 15s: p99_by_service[5m] (merges 20) -- dd(alpha=0.1) by (region) window 15s slide 15s: p99_by_region[5m] (merges 20) -- dd(alpha=0.1) by (region,service,status) window 60s slide 60s: p99_by_service,region[5m] (merges 5), p99_by_service,region,status[5m] (merges 5) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 60s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 1.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20) -- dd(alpha=0.1) by (region,service,status) window 60s slide 60s: p99_by_service,region[5m] (merges 5), p99_by_service,region,status[5m] (merges 5) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 60s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -## synthetic/case/anomaly/statuses=4/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.114 | 3 | 3.31 | -| 0.3 | 0.377 | 6 | 0.297 | -| 0.5 | 0.339 | 4 | 0.412 | -| 1.0 | 0.301 | 4 | 0.825 | -| 2.0 | 0.151 | 4 | 1.11 | -| 3.0 | 0.151 | 4 | 1.11 | - -Plan at SLA none: -- dd(alpha=0.1) by (region,service,status) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20), p99_by_service,region,status[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 0.3: -- dd(alpha=0.1) by (service) window 15s slide 15s: p99_by_service[5m] (merges 20) -- dd(alpha=0.1) by (region) window 15s slide 15s: p99_by_region[5m] (merges 20) -- dd(alpha=0.1) by (service,region) window 60s slide 60s: p99_by_service,region[5m] (merges 5) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 60s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 0.5: -- dd(alpha=0.1) by (region,service) window 30s slide 15s: p99_by_service[5m] (merges 10), p99_by_region[5m] (merges 10), p99_by_service,region[5m] (merges 10) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 60s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 1.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 60s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 2.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -## synthetic/case/anomaly/statuses=8/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.114 | 3 | 6.61 | -| 0.3 | 0.377 | 6 | 0.297 | -| 0.5 | 0.339 | 4 | 0.412 | -| 1.0 | 0.301 | 4 | 0.825 | -| 2.0 | 0.151 | 4 | 1.11 | -| 3.0 | 0.151 | 4 | 1.11 | - -Plan at SLA none: -- dd(alpha=0.1) by (endpoint,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_service,endpoint[5m] (merges 20) -- dd(alpha=0.1) by (region,service,status) window 15s slide 15s: p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20), p99_by_service,region,status[5m] (merges 20) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 0.3: -- dd(alpha=0.1) by (service) window 15s slide 15s: p99_by_service[5m] (merges 20) -- dd(alpha=0.1) by (region) window 15s slide 15s: p99_by_region[5m] (merges 20) -- dd(alpha=0.1) by (service,region) window 60s slide 60s: p99_by_service,region[5m] (merges 5) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 60s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 0.5: -- dd(alpha=0.1) by (region,service) window 30s slide 15s: p99_by_service[5m] (merges 10), p99_by_region[5m] (merges 10), p99_by_service,region[5m] (merges 10) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 60s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 1.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 300s slide 60s: p99_by_service,endpoint[5m] (merges 1) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -Plan at SLA 2.0: -- dd(alpha=0.1) by (region,service) window 15s slide 15s: p99_by_service[5m] (merges 20), p99_by_region[5m] (merges 20), p99_by_service,region[5m] (merges 20) -- dd(alpha=0.1) by (service,endpoint) window 60s slide 60s: p99_by_service,endpoint[5m] (merges 5) -- dd(alpha=0.1) by (service,region,status) window 300s slide 300s: p99_by_service,region,status[5m] (merges 1) -- dd(alpha=0.1) by (region,endpoint) window 300s slide 300s: p99_by_region,endpoint[5m] (merges 1) - -## synthetic/case/perf/base=0/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.105 | 2 | 2.38e+04 | -| 10.0 | 1.24 | 5 | 9.45 | -| 30.0 | 0.643 | 5 | 21.9 | -| 100.0 | 0.229 | 4 | 96.3 | -| 300.0 | 0.155 | 4 | 195 | -| 1000.0 | 0.126 | 3 | 493 | -| 3000.0 | 0.126 | 3 | 493 | -| 10000.0 | 0.122 | 3 | 5.95e+03 | - -Plan at SLA none: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4), quantile_over_time_p0.9[10m] (merges 40), quantile_over_time_p0.9[30m] (merges 120), quantile_over_time_p0.9[6h] (merges 1440), quantile_over_time_p0.9[1d] (merges 5760) - -Plan at SLA 10.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 30s slide 15s: quantile_over_time_p0.5[1m] (merges 2), quantile_over_time_p0.9[1m] (merges 2) -- dd(alpha=0.1) by (series) window 600s slide 60s: quantile_over_time_p0.9[10m] (merges 1), quantile_over_time_p0.9[30m] (merges 3) -- dd(alpha=0.1) by (series) window 7200s slide 1800s: quantile_over_time_p0.9[6h] (merges 3) -- dd(alpha=0.1) by (series) window 28800s slide 1800s: quantile_over_time_p0.9[1d] (merges 3) - -Plan at SLA 30.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 300s slide 60s: quantile_over_time_p0.9[10m] (merges 2), quantile_over_time_p0.9[30m] (merges 6) -- dd(alpha=0.1) by (series) window 3600s slide 1800s: quantile_over_time_p0.9[6h] (merges 6) -- dd(alpha=0.1) by (series) window 14400s slide 1800s: quantile_over_time_p0.9[1d] (merges 6) - -Plan at SLA 100.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 120s slide 60s: quantile_over_time_p0.9[10m] (merges 5), quantile_over_time_p0.9[30m] (merges 15) -- dd(alpha=0.1) by (series) window 3600s slide 1800s: quantile_over_time_p0.9[6h] (merges 6), quantile_over_time_p0.9[1d] (merges 24) - -Plan at SLA 300.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 60s slide 60s: quantile_over_time_p0.9[10m] (merges 10), quantile_over_time_p0.9[30m] (merges 30) -- dd(alpha=0.1) by (series) window 1800s slide 1800s: quantile_over_time_p0.9[6h] (merges 12), quantile_over_time_p0.9[1d] (merges 48) - -Plan at SLA 1000.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4), quantile_over_time_p0.9[10m] (merges 40), quantile_over_time_p0.9[30m] (merges 120) -- dd(alpha=0.1) by (series) window 1800s slide 1800s: quantile_over_time_p0.9[6h] (merges 12), quantile_over_time_p0.9[1d] (merges 48) - -Plan at SLA 10000.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 60s slide 60s: quantile_over_time_p0.9[10m] (merges 10), quantile_over_time_p0.9[30m] (merges 30), quantile_over_time_p0.9[6h] (merges 360), quantile_over_time_p0.9[1d] (merges 1440) - -## synthetic/case/perf/long_interval=15/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.627 | 3 | 2.97e+03 | -| 10.0 | infeasible | | | -| 30.0 | infeasible | | | -| 100.0 | infeasible | | | -| 300.0 | 3.53 | 4 | 274 | -| 1000.0 | 1.07 | 3 | 989 | -| 3000.0 | 0.627 | 3 | 2.97e+03 | -| 10000.0 | 0.627 | 3 | 2.97e+03 | - -Plan at SLA none: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 120s slide 15s: quantile_over_time_p0.9[10m] (merges 5), quantile_over_time_p0.9[30m] (merges 15), quantile_over_time_p0.9[6h] (merges 180), quantile_over_time_p0.9[1d] (merges 720) - -Plan at SLA 300.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 60s slide 60s: quantile_over_time_p0.9[10m] (merges 10), quantile_over_time_p0.9[30m] (merges 30) -- dd(alpha=0.1) by (series) window 1350s slide 15s: quantile_over_time_p0.9[6h] (merges 16), quantile_over_time_p0.9[1d] (merges 64) - -Plan at SLA 1000.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4), quantile_over_time_p0.9[10m] (merges 40) -- dd(alpha=0.1) by (series) window 360s slide 15s: quantile_over_time_p0.9[30m] (merges 5), quantile_over_time_p0.9[6h] (merges 60), quantile_over_time_p0.9[1d] (merges 240) - -## synthetic/case/perf/long_interval=1800/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.105 | 2 | 2.38e+04 | -| 10.0 | 1.24 | 5 | 9.45 | -| 30.0 | 0.643 | 5 | 21.9 | -| 100.0 | 0.229 | 4 | 96.3 | -| 300.0 | 0.155 | 4 | 195 | -| 1000.0 | 0.126 | 3 | 493 | -| 3000.0 | 0.126 | 3 | 493 | -| 10000.0 | 0.122 | 3 | 5.95e+03 | - -Plan at SLA none: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4), quantile_over_time_p0.9[10m] (merges 40), quantile_over_time_p0.9[30m] (merges 120), quantile_over_time_p0.9[6h] (merges 1440), quantile_over_time_p0.9[1d] (merges 5760) - -Plan at SLA 10.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 30s slide 15s: quantile_over_time_p0.5[1m] (merges 2), quantile_over_time_p0.9[1m] (merges 2) -- dd(alpha=0.1) by (series) window 600s slide 60s: quantile_over_time_p0.9[10m] (merges 1), quantile_over_time_p0.9[30m] (merges 3) -- dd(alpha=0.1) by (series) window 7200s slide 1800s: quantile_over_time_p0.9[6h] (merges 3) -- dd(alpha=0.1) by (series) window 28800s slide 1800s: quantile_over_time_p0.9[1d] (merges 3) - -Plan at SLA 30.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 300s slide 60s: quantile_over_time_p0.9[10m] (merges 2), quantile_over_time_p0.9[30m] (merges 6) -- dd(alpha=0.1) by (series) window 3600s slide 1800s: quantile_over_time_p0.9[6h] (merges 6) -- dd(alpha=0.1) by (series) window 14400s slide 1800s: quantile_over_time_p0.9[1d] (merges 6) - -Plan at SLA 100.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 120s slide 60s: quantile_over_time_p0.9[10m] (merges 5), quantile_over_time_p0.9[30m] (merges 15) -- dd(alpha=0.1) by (series) window 3600s slide 1800s: quantile_over_time_p0.9[6h] (merges 6), quantile_over_time_p0.9[1d] (merges 24) - -Plan at SLA 300.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 60s slide 60s: quantile_over_time_p0.9[10m] (merges 10), quantile_over_time_p0.9[30m] (merges 30) -- dd(alpha=0.1) by (series) window 1800s slide 1800s: quantile_over_time_p0.9[6h] (merges 12), quantile_over_time_p0.9[1d] (merges 48) - -Plan at SLA 1000.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4), quantile_over_time_p0.9[10m] (merges 40), quantile_over_time_p0.9[30m] (merges 120) -- dd(alpha=0.1) by (series) window 1800s slide 1800s: quantile_over_time_p0.9[6h] (merges 12), quantile_over_time_p0.9[1d] (merges 48) - -Plan at SLA 10000.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 60s slide 60s: quantile_over_time_p0.9[10m] (merges 10), quantile_over_time_p0.9[30m] (merges 30), quantile_over_time_p0.9[6h] (merges 360), quantile_over_time_p0.9[1d] (merges 1440) - -## synthetic/case/perf/long_interval=300/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.131 | 3 | 1.19e+03 | -| 10.0 | infeasible | | | -| 30.0 | 2.53 | 5 | 26 | -| 100.0 | 0.606 | 4 | 96.3 | -| 300.0 | 0.269 | 4 | 295 | -| 1000.0 | 0.166 | 3 | 592 | -| 3000.0 | 0.131 | 3 | 1.19e+03 | -| 10000.0 | 0.131 | 3 | 1.19e+03 | - -Plan at SLA none: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4), quantile_over_time_p0.9[10m] (merges 40), quantile_over_time_p0.9[30m] (merges 120) -- dd(alpha=0.1) by (series) window 300s slide 300s: quantile_over_time_p0.9[6h] (merges 72), quantile_over_time_p0.9[1d] (merges 288) - -Plan at SLA 30.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 300s slide 60s: quantile_over_time_p0.9[10m] (merges 2), quantile_over_time_p0.9[30m] (merges 6) -- dd(alpha=0.1) by (series) window 3600s slide 300s: quantile_over_time_p0.9[6h] (merges 6) -- dd(alpha=0.1) by (series) window 14400s slide 300s: quantile_over_time_p0.9[1d] (merges 6) - -Plan at SLA 100.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 120s slide 60s: quantile_over_time_p0.9[10m] (merges 5), quantile_over_time_p0.9[30m] (merges 15) -- dd(alpha=0.1) by (series) window 3600s slide 300s: quantile_over_time_p0.9[6h] (merges 6), quantile_over_time_p0.9[1d] (merges 24) - -Plan at SLA 300.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 60s slide 60s: quantile_over_time_p0.9[10m] (merges 10), quantile_over_time_p0.9[30m] (merges 30) -- dd(alpha=0.1) by (series) window 1200s slide 300s: quantile_over_time_p0.9[6h] (merges 18), quantile_over_time_p0.9[1d] (merges 72) - -Plan at SLA 1000.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4), quantile_over_time_p0.9[10m] (merges 40), quantile_over_time_p0.9[30m] (merges 120) -- dd(alpha=0.1) by (series) window 600s slide 300s: quantile_over_time_p0.9[6h] (merges 36), quantile_over_time_p0.9[1d] (merges 144) - -## synthetic/case/perf/long_interval=60/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.216 | 3 | 2.97e+03 | -| 10.0 | infeasible | | | -| 30.0 | infeasible | | | -| 100.0 | 2.87 | 4 | 88 | -| 300.0 | 0.877 | 4 | 295 | -| 1000.0 | 0.327 | 3 | 989 | -| 3000.0 | 0.216 | 3 | 2.97e+03 | -| 10000.0 | 0.216 | 3 | 2.97e+03 | - -Plan at SLA none: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 120s slide 60s: quantile_over_time_p0.9[10m] (merges 5), quantile_over_time_p0.9[30m] (merges 15), quantile_over_time_p0.9[6h] (merges 180), quantile_over_time_p0.9[1d] (merges 720) - -Plan at SLA 100.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 120s slide 60s: quantile_over_time_p0.9[10m] (merges 5), quantile_over_time_p0.9[30m] (merges 15) -- dd(alpha=0.1) by (series) window 4320s slide 60s: quantile_over_time_p0.9[6h] (merges 5), quantile_over_time_p0.9[1d] (merges 20) - -Plan at SLA 300.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 60s slide 60s: quantile_over_time_p0.9[10m] (merges 10), quantile_over_time_p0.9[30m] (merges 30) -- dd(alpha=0.1) by (series) window 1200s slide 60s: quantile_over_time_p0.9[6h] (merges 18), quantile_over_time_p0.9[1d] (merges 72) - -Plan at SLA 1000.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4), quantile_over_time_p0.9[10m] (merges 40) -- dd(alpha=0.1) by (series) window 360s slide 60s: quantile_over_time_p0.9[30m] (merges 5), quantile_over_time_p0.9[6h] (merges 60), quantile_over_time_p0.9[1d] (merges 240) - -## synthetic/case/perf/per_series=1000/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.406 | 2 | 2.38e+04 | -| 10.0 | infeasible | | | -| 30.0 | 3.2 | 5 | 26 | -| 100.0 | 1.13 | 4 | 96.3 | -| 300.0 | 0.758 | 4 | 195 | -| 1000.0 | 0.578 | 3 | 493 | -| 3000.0 | 0.578 | 3 | 493 | -| 10000.0 | 0.574 | 3 | 5.95e+03 | - -Plan at SLA none: -- dd(alpha=0.1) by (job) window 0.001s slide 0.001s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4), quantile_over_time_p0.9[10m] (merges 40), quantile_over_time_p0.9[30m] (merges 120), quantile_over_time_p0.9[6h] (merges 1440), quantile_over_time_p0.9[1d] (merges 5760) - -Plan at SLA 30.0: -- dd(alpha=0.1) by (job) window 0.001s slide 0.001s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 300s slide 60s: quantile_over_time_p0.9[10m] (merges 2), quantile_over_time_p0.9[30m] (merges 6) -- dd(alpha=0.1) by (series) window 3600s slide 1800s: quantile_over_time_p0.9[6h] (merges 6) -- dd(alpha=0.1) by (series) window 14400s slide 1800s: quantile_over_time_p0.9[1d] (merges 6) - -Plan at SLA 100.0: -- dd(alpha=0.1) by (job) window 0.001s slide 0.001s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 120s slide 60s: quantile_over_time_p0.9[10m] (merges 5), quantile_over_time_p0.9[30m] (merges 15) -- dd(alpha=0.1) by (series) window 3600s slide 1800s: quantile_over_time_p0.9[6h] (merges 6), quantile_over_time_p0.9[1d] (merges 24) - -Plan at SLA 300.0: -- dd(alpha=0.1) by (job) window 0.001s slide 0.001s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 60s slide 60s: quantile_over_time_p0.9[10m] (merges 10), quantile_over_time_p0.9[30m] (merges 30) -- dd(alpha=0.1) by (series) window 1800s slide 1800s: quantile_over_time_p0.9[6h] (merges 12), quantile_over_time_p0.9[1d] (merges 48) - -Plan at SLA 1000.0: -- dd(alpha=0.1) by (job) window 0.001s slide 0.001s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4), quantile_over_time_p0.9[10m] (merges 40), quantile_over_time_p0.9[30m] (merges 120) -- dd(alpha=0.1) by (series) window 1800s slide 1800s: quantile_over_time_p0.9[6h] (merges 12), quantile_over_time_p0.9[1d] (merges 48) - -Plan at SLA 10000.0: -- dd(alpha=0.1) by (job) window 0.001s slide 0.001s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 60s slide 60s: quantile_over_time_p0.9[10m] (merges 10), quantile_over_time_p0.9[30m] (merges 30), quantile_over_time_p0.9[6h] (merges 360), quantile_over_time_p0.9[1d] (merges 1440) - -## synthetic/case/perf/per_series=100/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.0651 | 3 | 5.95e+03 | -| 10.0 | 0.622 | 5 | 9.45 | -| 30.0 | 0.322 | 5 | 21.9 | -| 100.0 | 0.116 | 4 | 96.3 | -| 300.0 | 0.0799 | 4 | 195 | -| 1000.0 | 0.0694 | 3 | 493 | -| 3000.0 | 0.0694 | 3 | 493 | -| 10000.0 | 0.0651 | 3 | 5.95e+03 | - -Plan at SLA none: -- dd(alpha=0.1) by (job) window 0.01s slide 0.01s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 60s slide 60s: quantile_over_time_p0.9[10m] (merges 10), quantile_over_time_p0.9[30m] (merges 30), quantile_over_time_p0.9[6h] (merges 360), quantile_over_time_p0.9[1d] (merges 1440) - -Plan at SLA 10.0: -- dd(alpha=0.1) by (job) window 0.01s slide 0.01s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 30s slide 15s: quantile_over_time_p0.5[1m] (merges 2), quantile_over_time_p0.9[1m] (merges 2) -- dd(alpha=0.1) by (series) window 600s slide 60s: quantile_over_time_p0.9[10m] (merges 1), quantile_over_time_p0.9[30m] (merges 3) -- dd(alpha=0.1) by (series) window 7200s slide 1800s: quantile_over_time_p0.9[6h] (merges 3) -- dd(alpha=0.1) by (series) window 28800s slide 1800s: quantile_over_time_p0.9[1d] (merges 3) - -Plan at SLA 30.0: -- dd(alpha=0.1) by (job) window 0.01s slide 0.01s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 300s slide 60s: quantile_over_time_p0.9[10m] (merges 2), quantile_over_time_p0.9[30m] (merges 6) -- dd(alpha=0.1) by (series) window 3600s slide 1800s: quantile_over_time_p0.9[6h] (merges 6) -- dd(alpha=0.1) by (series) window 14400s slide 1800s: quantile_over_time_p0.9[1d] (merges 6) - -Plan at SLA 100.0: -- dd(alpha=0.1) by (job) window 0.01s slide 0.01s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 120s slide 60s: quantile_over_time_p0.9[10m] (merges 5), quantile_over_time_p0.9[30m] (merges 15) -- dd(alpha=0.1) by (series) window 3600s slide 1800s: quantile_over_time_p0.9[6h] (merges 6), quantile_over_time_p0.9[1d] (merges 24) - -Plan at SLA 300.0: -- dd(alpha=0.1) by (job) window 0.01s slide 0.01s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 60s slide 60s: quantile_over_time_p0.9[10m] (merges 10), quantile_over_time_p0.9[30m] (merges 30) -- dd(alpha=0.1) by (series) window 1800s slide 1800s: quantile_over_time_p0.9[6h] (merges 12), quantile_over_time_p0.9[1d] (merges 48) - -Plan at SLA 1000.0: -- dd(alpha=0.1) by (job) window 0.01s slide 0.01s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4), quantile_over_time_p0.9[10m] (merges 40), quantile_over_time_p0.9[30m] (merges 120) -- dd(alpha=0.1) by (series) window 1800s slide 1800s: quantile_over_time_p0.9[6h] (merges 12), quantile_over_time_p0.9[1d] (merges 48) - -## synthetic/case/perf/per_series=200/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.105 | 2 | 2.38e+04 | -| 10.0 | 1.24 | 5 | 9.45 | -| 30.0 | 0.643 | 5 | 21.9 | -| 100.0 | 0.229 | 4 | 96.3 | -| 300.0 | 0.155 | 4 | 195 | -| 1000.0 | 0.126 | 3 | 493 | -| 3000.0 | 0.126 | 3 | 493 | -| 10000.0 | 0.122 | 3 | 5.95e+03 | - -Plan at SLA none: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4), quantile_over_time_p0.9[10m] (merges 40), quantile_over_time_p0.9[30m] (merges 120), quantile_over_time_p0.9[6h] (merges 1440), quantile_over_time_p0.9[1d] (merges 5760) - -Plan at SLA 10.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 30s slide 15s: quantile_over_time_p0.5[1m] (merges 2), quantile_over_time_p0.9[1m] (merges 2) -- dd(alpha=0.1) by (series) window 600s slide 60s: quantile_over_time_p0.9[10m] (merges 1), quantile_over_time_p0.9[30m] (merges 3) -- dd(alpha=0.1) by (series) window 7200s slide 1800s: quantile_over_time_p0.9[6h] (merges 3) -- dd(alpha=0.1) by (series) window 28800s slide 1800s: quantile_over_time_p0.9[1d] (merges 3) - -Plan at SLA 30.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 300s slide 60s: quantile_over_time_p0.9[10m] (merges 2), quantile_over_time_p0.9[30m] (merges 6) -- dd(alpha=0.1) by (series) window 3600s slide 1800s: quantile_over_time_p0.9[6h] (merges 6) -- dd(alpha=0.1) by (series) window 14400s slide 1800s: quantile_over_time_p0.9[1d] (merges 6) - -Plan at SLA 100.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 120s slide 60s: quantile_over_time_p0.9[10m] (merges 5), quantile_over_time_p0.9[30m] (merges 15) -- dd(alpha=0.1) by (series) window 3600s slide 1800s: quantile_over_time_p0.9[6h] (merges 6), quantile_over_time_p0.9[1d] (merges 24) - -Plan at SLA 300.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 60s slide 60s: quantile_over_time_p0.9[10m] (merges 10), quantile_over_time_p0.9[30m] (merges 30) -- dd(alpha=0.1) by (series) window 1800s slide 1800s: quantile_over_time_p0.9[6h] (merges 12), quantile_over_time_p0.9[1d] (merges 48) - -Plan at SLA 1000.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4), quantile_over_time_p0.9[10m] (merges 40), quantile_over_time_p0.9[30m] (merges 120) -- dd(alpha=0.1) by (series) window 1800s slide 1800s: quantile_over_time_p0.9[6h] (merges 12), quantile_over_time_p0.9[1d] (merges 48) - -Plan at SLA 10000.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 60s slide 60s: quantile_over_time_p0.9[10m] (merges 10), quantile_over_time_p0.9[30m] (merges 30), quantile_over_time_p0.9[6h] (merges 360), quantile_over_time_p0.9[1d] (merges 1440) - -## synthetic/case/perf/per_series=20/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.0197 | 4 | 195 | -| 10.0 | 0.125 | 5 | 9.45 | -| 30.0 | 0.0663 | 5 | 21.9 | -| 100.0 | 0.0258 | 4 | 96.3 | -| 300.0 | 0.0197 | 4 | 195 | -| 1000.0 | 0.0197 | 4 | 195 | -| 3000.0 | 0.0197 | 4 | 195 | -| 10000.0 | 0.0197 | 4 | 195 | - -Plan at SLA none: -- dd(alpha=0.1) by (job) window 0.05s slide 0.05s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 60s slide 60s: quantile_over_time_p0.9[10m] (merges 10), quantile_over_time_p0.9[30m] (merges 30) -- dd(alpha=0.1) by (series) window 1800s slide 1800s: quantile_over_time_p0.9[6h] (merges 12), quantile_over_time_p0.9[1d] (merges 48) - -Plan at SLA 10.0: -- dd(alpha=0.1) by (job) window 0.05s slide 0.05s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 30s slide 15s: quantile_over_time_p0.5[1m] (merges 2), quantile_over_time_p0.9[1m] (merges 2) -- dd(alpha=0.1) by (series) window 600s slide 60s: quantile_over_time_p0.9[10m] (merges 1), quantile_over_time_p0.9[30m] (merges 3) -- dd(alpha=0.1) by (series) window 7200s slide 1800s: quantile_over_time_p0.9[6h] (merges 3) -- dd(alpha=0.1) by (series) window 28800s slide 1800s: quantile_over_time_p0.9[1d] (merges 3) - -Plan at SLA 30.0: -- dd(alpha=0.1) by (job) window 0.05s slide 0.05s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 300s slide 60s: quantile_over_time_p0.9[10m] (merges 2), quantile_over_time_p0.9[30m] (merges 6) -- dd(alpha=0.1) by (series) window 3600s slide 1800s: quantile_over_time_p0.9[6h] (merges 6) -- dd(alpha=0.1) by (series) window 14400s slide 1800s: quantile_over_time_p0.9[1d] (merges 6) - -Plan at SLA 100.0: -- dd(alpha=0.1) by (job) window 0.05s slide 0.05s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 120s slide 60s: quantile_over_time_p0.9[10m] (merges 5), quantile_over_time_p0.9[30m] (merges 15) -- dd(alpha=0.1) by (series) window 3600s slide 1800s: quantile_over_time_p0.9[6h] (merges 6), quantile_over_time_p0.9[1d] (merges 24) - -## synthetic/case/perf/per_series=500/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.218 | 2 | 2.38e+04 | -| 10.0 | infeasible | | | -| 30.0 | 1.6 | 5 | 21.9 | -| 100.0 | 0.568 | 4 | 96.3 | -| 300.0 | 0.381 | 4 | 195 | -| 1000.0 | 0.295 | 3 | 493 | -| 3000.0 | 0.295 | 3 | 493 | -| 10000.0 | 0.291 | 3 | 5.95e+03 | - -Plan at SLA none: -- dd(alpha=0.1) by (job) window 0.002s slide 0.002s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4), quantile_over_time_p0.9[10m] (merges 40), quantile_over_time_p0.9[30m] (merges 120), quantile_over_time_p0.9[6h] (merges 1440), quantile_over_time_p0.9[1d] (merges 5760) - -Plan at SLA 30.0: -- dd(alpha=0.1) by (job) window 0.002s slide 0.002s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 300s slide 60s: quantile_over_time_p0.9[10m] (merges 2), quantile_over_time_p0.9[30m] (merges 6) -- dd(alpha=0.1) by (series) window 3600s slide 1800s: quantile_over_time_p0.9[6h] (merges 6) -- dd(alpha=0.1) by (series) window 14400s slide 1800s: quantile_over_time_p0.9[1d] (merges 6) - -Plan at SLA 100.0: -- dd(alpha=0.1) by (job) window 0.002s slide 0.002s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 120s slide 60s: quantile_over_time_p0.9[10m] (merges 5), quantile_over_time_p0.9[30m] (merges 15) -- dd(alpha=0.1) by (series) window 3600s slide 1800s: quantile_over_time_p0.9[6h] (merges 6), quantile_over_time_p0.9[1d] (merges 24) - -Plan at SLA 300.0: -- dd(alpha=0.1) by (job) window 0.002s slide 0.002s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 60s slide 60s: quantile_over_time_p0.9[10m] (merges 10), quantile_over_time_p0.9[30m] (merges 30) -- dd(alpha=0.1) by (series) window 1800s slide 1800s: quantile_over_time_p0.9[6h] (merges 12), quantile_over_time_p0.9[1d] (merges 48) - -Plan at SLA 1000.0: -- dd(alpha=0.1) by (job) window 0.002s slide 0.002s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4), quantile_over_time_p0.9[10m] (merges 40), quantile_over_time_p0.9[30m] (merges 120) -- dd(alpha=0.1) by (series) window 1800s slide 1800s: quantile_over_time_p0.9[6h] (merges 12), quantile_over_time_p0.9[1d] (merges 48) - -Plan at SLA 10000.0: -- dd(alpha=0.1) by (job) window 0.002s slide 0.002s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 60s slide 60s: quantile_over_time_p0.9[10m] (merges 10), quantile_over_time_p0.9[30m] (merges 30), quantile_over_time_p0.9[6h] (merges 360), quantile_over_time_p0.9[1d] (merges 1440) - -## synthetic/case/perf/per_series=50/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.0368 | 3 | 5.95e+03 | -| 10.0 | 0.312 | 5 | 9.45 | -| 30.0 | 0.162 | 5 | 21.9 | -| 100.0 | 0.0597 | 4 | 96.3 | -| 300.0 | 0.0423 | 4 | 195 | -| 1000.0 | 0.0411 | 3 | 493 | -| 3000.0 | 0.0411 | 3 | 493 | -| 10000.0 | 0.0368 | 3 | 5.95e+03 | - -Plan at SLA none: -- dd(alpha=0.1) by (job) window 0.02s slide 0.02s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 60s slide 60s: quantile_over_time_p0.9[10m] (merges 10), quantile_over_time_p0.9[30m] (merges 30), quantile_over_time_p0.9[6h] (merges 360), quantile_over_time_p0.9[1d] (merges 1440) - -Plan at SLA 10.0: -- dd(alpha=0.1) by (job) window 0.02s slide 0.02s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 30s slide 15s: quantile_over_time_p0.5[1m] (merges 2), quantile_over_time_p0.9[1m] (merges 2) -- dd(alpha=0.1) by (series) window 600s slide 60s: quantile_over_time_p0.9[10m] (merges 1), quantile_over_time_p0.9[30m] (merges 3) -- dd(alpha=0.1) by (series) window 7200s slide 1800s: quantile_over_time_p0.9[6h] (merges 3) -- dd(alpha=0.1) by (series) window 28800s slide 1800s: quantile_over_time_p0.9[1d] (merges 3) - -Plan at SLA 30.0: -- dd(alpha=0.1) by (job) window 0.02s slide 0.02s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 300s slide 60s: quantile_over_time_p0.9[10m] (merges 2), quantile_over_time_p0.9[30m] (merges 6) -- dd(alpha=0.1) by (series) window 3600s slide 1800s: quantile_over_time_p0.9[6h] (merges 6) -- dd(alpha=0.1) by (series) window 14400s slide 1800s: quantile_over_time_p0.9[1d] (merges 6) - -Plan at SLA 100.0: -- dd(alpha=0.1) by (job) window 0.02s slide 0.02s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 120s slide 60s: quantile_over_time_p0.9[10m] (merges 5), quantile_over_time_p0.9[30m] (merges 15) -- dd(alpha=0.1) by (series) window 3600s slide 1800s: quantile_over_time_p0.9[6h] (merges 6), quantile_over_time_p0.9[1d] (merges 24) - -Plan at SLA 300.0: -- dd(alpha=0.1) by (job) window 0.02s slide 0.02s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 60s slide 60s: quantile_over_time_p0.9[10m] (merges 10), quantile_over_time_p0.9[30m] (merges 30) -- dd(alpha=0.1) by (series) window 1800s slide 1800s: quantile_over_time_p0.9[6h] (merges 12), quantile_over_time_p0.9[1d] (merges 48) - -Plan at SLA 1000.0: -- dd(alpha=0.1) by (job) window 0.02s slide 0.02s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4), quantile_over_time_p0.9[10m] (merges 40), quantile_over_time_p0.9[30m] (merges 120) -- dd(alpha=0.1) by (series) window 1800s slide 1800s: quantile_over_time_p0.9[6h] (merges 12), quantile_over_time_p0.9[1d] (merges 48) - -## synthetic/case/perf/series=100000/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 1.05 | 2 | 2.38e+05 | -| 10.0 | infeasible | | | -| 30.0 | infeasible | | | -| 100.0 | infeasible | | | -| 300.0 | infeasible | | | -| 1000.0 | 2.29 | 4 | 963 | -| 3000.0 | 1.55 | 4 | 1.95e+03 | -| 10000.0 | 1.26 | 3 | 4.93e+03 | - -Plan at SLA none: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4), quantile_over_time_p0.9[10m] (merges 40), quantile_over_time_p0.9[30m] (merges 120), quantile_over_time_p0.9[6h] (merges 1440), quantile_over_time_p0.9[1d] (merges 5760) - -Plan at SLA 1000.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 120s slide 60s: quantile_over_time_p0.9[10m] (merges 5), quantile_over_time_p0.9[30m] (merges 15) -- dd(alpha=0.1) by (series) window 3600s slide 1800s: quantile_over_time_p0.9[6h] (merges 6), quantile_over_time_p0.9[1d] (merges 24) - -Plan at SLA 3000.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 60s slide 60s: quantile_over_time_p0.9[10m] (merges 10), quantile_over_time_p0.9[30m] (merges 30) -- dd(alpha=0.1) by (series) window 1800s slide 1800s: quantile_over_time_p0.9[6h] (merges 12), quantile_over_time_p0.9[1d] (merges 48) - -Plan at SLA 10000.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4), quantile_over_time_p0.9[10m] (merges 40), quantile_over_time_p0.9[30m] (merges 120) -- dd(alpha=0.1) by (series) window 1800s slide 1800s: quantile_over_time_p0.9[6h] (merges 12), quantile_over_time_p0.9[1d] (merges 48) - -## synthetic/case/perf/series=10000/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.105 | 2 | 2.38e+04 | -| 10.0 | 1.24 | 5 | 9.45 | -| 30.0 | 0.643 | 5 | 21.9 | -| 100.0 | 0.229 | 4 | 96.3 | -| 300.0 | 0.155 | 4 | 195 | -| 1000.0 | 0.126 | 3 | 493 | -| 3000.0 | 0.126 | 3 | 493 | -| 10000.0 | 0.122 | 3 | 5.95e+03 | - -Plan at SLA none: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4), quantile_over_time_p0.9[10m] (merges 40), quantile_over_time_p0.9[30m] (merges 120), quantile_over_time_p0.9[6h] (merges 1440), quantile_over_time_p0.9[1d] (merges 5760) - -Plan at SLA 10.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 30s slide 15s: quantile_over_time_p0.5[1m] (merges 2), quantile_over_time_p0.9[1m] (merges 2) -- dd(alpha=0.1) by (series) window 600s slide 60s: quantile_over_time_p0.9[10m] (merges 1), quantile_over_time_p0.9[30m] (merges 3) -- dd(alpha=0.1) by (series) window 7200s slide 1800s: quantile_over_time_p0.9[6h] (merges 3) -- dd(alpha=0.1) by (series) window 28800s slide 1800s: quantile_over_time_p0.9[1d] (merges 3) - -Plan at SLA 30.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 300s slide 60s: quantile_over_time_p0.9[10m] (merges 2), quantile_over_time_p0.9[30m] (merges 6) -- dd(alpha=0.1) by (series) window 3600s slide 1800s: quantile_over_time_p0.9[6h] (merges 6) -- dd(alpha=0.1) by (series) window 14400s slide 1800s: quantile_over_time_p0.9[1d] (merges 6) - -Plan at SLA 100.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 120s slide 60s: quantile_over_time_p0.9[10m] (merges 5), quantile_over_time_p0.9[30m] (merges 15) -- dd(alpha=0.1) by (series) window 3600s slide 1800s: quantile_over_time_p0.9[6h] (merges 6), quantile_over_time_p0.9[1d] (merges 24) - -Plan at SLA 300.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 60s slide 60s: quantile_over_time_p0.9[10m] (merges 10), quantile_over_time_p0.9[30m] (merges 30) -- dd(alpha=0.1) by (series) window 1800s slide 1800s: quantile_over_time_p0.9[6h] (merges 12), quantile_over_time_p0.9[1d] (merges 48) - -Plan at SLA 1000.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4), quantile_over_time_p0.9[10m] (merges 40), quantile_over_time_p0.9[30m] (merges 120) -- dd(alpha=0.1) by (series) window 1800s slide 1800s: quantile_over_time_p0.9[6h] (merges 12), quantile_over_time_p0.9[1d] (merges 48) - -Plan at SLA 10000.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 60s slide 60s: quantile_over_time_p0.9[10m] (merges 10), quantile_over_time_p0.9[30m] (merges 30), quantile_over_time_p0.9[6h] (merges 360), quantile_over_time_p0.9[1d] (merges 1440) - -## synthetic/case/perf/series=30000/tp95 - -| SLA (ms) | vCPU | MVs | latency (ms) | -|---|---|---|---| -| none | 0.314 | 2 | 7.14e+04 | -| 10.0 | infeasible | | | -| 30.0 | infeasible | | | -| 100.0 | 1.48 | 4 | 90.4 | -| 300.0 | 0.687 | 4 | 289 | -| 1000.0 | 0.466 | 4 | 586 | -| 3000.0 | 0.378 | 3 | 1.48e+03 | -| 10000.0 | 0.378 | 3 | 1.48e+03 | - -Plan at SLA none: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4), quantile_over_time_p0.9[10m] (merges 40), quantile_over_time_p0.9[30m] (merges 120), quantile_over_time_p0.9[6h] (merges 1440), quantile_over_time_p0.9[1d] (merges 5760) - -Plan at SLA 100.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 300s slide 60s: quantile_over_time_p0.9[10m] (merges 2), quantile_over_time_p0.9[30m] (merges 6) -- dd(alpha=0.1) by (series) window 10800s slide 1800s: quantile_over_time_p0.9[6h] (merges 2), quantile_over_time_p0.9[1d] (merges 8) - -Plan at SLA 300.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 120s slide 60s: quantile_over_time_p0.9[10m] (merges 5), quantile_over_time_p0.9[30m] (merges 15) -- dd(alpha=0.1) by (series) window 3600s slide 1800s: quantile_over_time_p0.9[6h] (merges 6), quantile_over_time_p0.9[1d] (merges 24) - -Plan at SLA 1000.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4) -- dd(alpha=0.1) by (series) window 60s slide 60s: quantile_over_time_p0.9[10m] (merges 10), quantile_over_time_p0.9[30m] (merges 30) -- dd(alpha=0.1) by (series) window 1800s slide 1800s: quantile_over_time_p0.9[6h] (merges 12), quantile_over_time_p0.9[1d] (merges 48) - -Plan at SLA 3000.0: -- dd(alpha=0.1) by (job) window 0.005s slide 0.005s: quantile_by_job_p0.5[1 scrape] (merges 1), quantile_by_job_p0.9[1 scrape] (merges 1) -- dd(alpha=0.1) by (series) window 15s slide 15s: quantile_over_time_p0.5[1m] (merges 4), quantile_over_time_p0.9[1m] (merges 4), quantile_over_time_p0.9[10m] (merges 40), quantile_over_time_p0.9[30m] (merges 120) -- dd(alpha=0.1) by (series) window 1800s slide 1800s: quantile_over_time_p0.9[6h] (merges 12), quantile_over_time_p0.9[1d] (merges 48) diff --git a/rqe-optimizer/results/e2e-rate10/README.md b/rqe-optimizer/results/e2e-rate10/README.md deleted file mode 100644 index 677113b8..00000000 --- a/rqe-optimizer/results/e2e-rate10/README.md +++ /dev/null @@ -1,132 +0,0 @@ -# §6.3 workloads at 10 samples/s, for the end-to-end run - -The classic, multi-grouping and all (mixed + multi-grouping) workloads of -`../autosketch-vs-asap-hydra/` (r = 1, m = 1), re-planned at 1/20 of the data -rate: each series is scraped 10 times a second (every 100 ms) instead of 200, -so the classic metric carries 1e5 samples/s, `http` 1e5 records/s and `flows` -2e5 records/s. The templates, windows, accuracy curves, cost table, p95 -targets and runner are those of `../autosketch-vs-asap-hydra/`, with two -differences for the end-to-end run: - -- **Instant RQEs read one scrape.** `sum by (job)` and `quantile by (job)` - look back 100 ms, one sample per series (N = 1e3 per job), and are served - by 100 ms tumbling windows. -- **One RQE per schema query.** A query that several templates ask (14, 15, - 17) is only its strictest template's RQE, so every e2e query is answered by - one deployment. §6.3 keeps every copy (`--keep-duplicate-queries`). - -## Files - -- `inputs/tables/`: the tables, `export_autosketch_eval_table.py --synthetic - --samples-per-sec 10` (classic, all, multigroup at shared = 1). Each one's - `workloads[0].e2e` holds the queries and metrics to generate. Each query - but `rate` carries `expr`, its RQE expression tree (leaves name the plans' - RQE ids), added after planning: the RQEs and plans are unchanged. -- `plans/.plans.json.gz`: each method's chosen plans, in the format of - `../autosketch-vs-asap-hydra/README.md`, "Plans" (`--runs 1`, kept, then - `scripts/extract_autosketch_plans.py`). -- `
    -tp95.json`: the runner's results (`--runs 3 --no-chosen`). - -## Served and left-out RQEs - -At 10 samples/s, two RQEs of template 16 (p99 latency `GROUP BY CUBE(region, -service, endpoint)`) fall below the accuracy curves' first N (1e3 items): - -- `t16_p99_latency_by_region,endpoint/values/5m` -- `t16_p99_latency_by_region,service,endpoint/values/5m` - -Their smallest group (all 2,500 groups are covered) holds 403 items in 5m. The -optimizer can't read accuracy below the curves (`SaturationCurves::accuracy` -returns `None`), so no method can serve them. The export leaves them out of -the RQEs and the e2e queries, and logs it. The same groupings at 15m (1,210 -items) are kept. With the two RQEs left in, the runner drops exactly these two -as unservable (ASAP and AutoSketch alike) and picks the same plans. - -| Workload | RQEs at 200/s (§6.3) | Duplicate queries | Below the curves | RQEs at 10/s, all served | -|---|---|---|---|---| -| classic | 42 | 0 | 0 | 42 | -| multigroup | 62 | 10 | 2 | 50 | -| all | 104 | 10 | 2 | 92 | - -Every chosen accuracy is measured (no theory reads), and no Hydra grid is chosen. - -## Predicted cost - -Cheapest plan (no latency bound) at 10/s: - -| Workload | Weights | ASAP | ASAP (no roll-ups) | PerQuery | AutoSketch | -|---|---|---|---|---|---| -| classic | CPU (vCPU) | 0.318 | 0.318 | 0.699 | 135 | -| classic | Fargate ($/h) | 0.0585 | 0.0585 | 0.192 | 6.41 | -| multigroup | CPU | 0.0911 | 0.110 | 0.132 | 0.487 | -| multigroup | Fargate | 0.00846 | 0.0155 | 0.0204 | 0.0348 | -| all | CPU | 0.409 | 0.428 | 0.831 | 136 | -| all | Fargate | 0.0670 | 0.0740 | 0.212 | 6.45 | - -ASAP, CPU weights: - -| Workload | Memory (GiB) | Deployments | RQEs rolled up | -|---|---|---|---| -| classic | 10.7 | 7 | 0 | -| multigroup | 1.33 | 9 | 33 | -| all | 12.0 | 16 | 33 | - -Classic deployments (ASAP, CPU weights): - -| Deployment | Window/slide (s) | -|---|---| -| `sum by (job)` (exact-sum) | 0.1/0.1 | -| `quantile by (job)` (DDSketch α = 0.1) | 0.1/0.1 | -| `sum by (job) (sum_over_time)` (exact-sum) | 60/60 | -| `sum_over_time` (exact-sum) | 60/60 | -| `quantile_over_time` (DDSketch α = 0.1) | 900/60 | -| `rate` (exact-increase) | 180/60 | -| top-k (CMS 3×1024, heap 9,216) | 300/60 | - -Against the previous 10/s plans (1 s instant lookback, every duplicate query, -the cost table and curves before the denser grid): - -- **Memory falls ~30%** (classic 15.3 → 10.7 GiB), and Fargate costs ~25% - (classic 0.0787 → 0.0585 $/h): with the denser grid, plans pick DDSketch - α = 0.1, the coarsest that meets the target. -- **CPU rises ~3%** (classic 0.309 → 0.318 vCPU, +1 deployment): - `sum by (job) (sum_over_time)` no longer shares the instant `sum by (job)` - deployment, whose window is now 100 ms. -- **Multigroup serves 10 fewer RQEs** and costs ~4% less CPU; 33 RQEs are - rolled up (was 41). -- The tightest feasible latency bounds are unchanged: classic 21 ms, multigroup - and all 490 ms. - -Every e2e query's RQEs are served by one deployment in every plan. An -independent re-plan in another session gave identical tables and identical -plans in all 120 (method, weights, kind, SLA) combinations. - -## Converting the plans in ASAPQuery - -`--planner saved` needs `--data-ingestion-interval-ms 100` on these tables: -the scrape interval, which instant RQEs' 0.1 s windows are whole multiples -of. With it, on ASAPQuery `main` (ProjectASAP/ASAPQuery#866), every ASAP plan -converts: classic PromQL 12/12, multigroup SQL 8/8, all SQL 8/8 and PromQL -8/8. - -## Reproduce - -```sh -cargo build --release -p rqe-optimizer --examples -DIR=rqe-optimizer/results/e2e-rate10; TMP=$(mktemp -d) -# SAT: the saturation dir of ../autosketch-vs-asap-hydra/README.md, "Reproduce" -python3 scripts/export_autosketch_eval_table.py --synthetic --samples-per-sec 10 \ - --revision acc2db1 --out $TMP -for t in classic all multigroup; do - f=synthetic-templates$t-shared1 - cp $TMP/$f.json $DIR/inputs/tables/ - target/release/examples/autosketch_vs_asap synthetic --table $DIR/inputs/tables/$f.json \ - --target p95 --runs 3 --no-chosen --saturation-dir $SAT --out $DIR/$f-tp95.json - target/release/examples/autosketch_vs_asap synthetic --table $DIR/inputs/tables/$f.json \ - --target p95 --runs 1 --saturation-dir $SAT --out $TMP/$f-tp95.json - python3 scripts/extract_autosketch_plans.py $TMP/$f-tp95.json $DIR/plans/$f.plans.json.gz -done -``` - -Runs: 2026-10-14, sketch-bench `main` (acc2db1), on a 16-core desktop (not an -idle cluster node), so `planning_secs` are indicative only. diff --git a/rqe-optimizer/results/e2e-rate10/inputs/tables/synthetic-templatesall-shared1.json b/rqe-optimizer/results/e2e-rate10/inputs/tables/synthetic-templatesall-shared1.json deleted file mode 100644 index 3102ab21..00000000 --- a/rqe-optimizer/results/e2e-rate10/inputs/tables/synthetic-templatesall-shared1.json +++ /dev/null @@ -1 +0,0 @@ -{"inputs": {}, "plan": "ProjectASAP/ASAPQuery#777", "schema_version": 1, "sketch_bench_revision": "acc2db1", "workloads": [{"dataset": "synthetic/templates=all/shared=1", "e2e": {"metrics": {"data": {"labels": [{"cardinality": 10, "name": "job"}, {"cardinality": 1000, "child_of": "job", "name": "label_0"}], "samples_per_sec_per_series": 10, "type": "gauge", "values": {"dist": {"pareto": 2.0}, "value": {"keys": 10000, "zipf": 1.1}}}, "data_total": {"labels": 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file mode 100644 index 813e7da5..00000000 --- a/rqe-optimizer/results/e2e-rate10/inputs/tables/synthetic-templatesmultigroup-shared1.json +++ /dev/null @@ -1 +0,0 @@ -{"inputs": {}, "plan": "ProjectASAP/ASAPQuery#777", "schema_version": 1, "sketch_bench_revision": "acc2db1", "workloads": [{"dataset": "synthetic/templates=multigroup/shared=1", "e2e": {"metrics": {"flows": {"labels": [{"cardinality": 1000, "name": "dst_subnet", "skew": 1.1}, {"cardinality": 1000, "name": "dst_port", "skew": 1.2}, {"cardinality": 3, "name": "proto", "skew": 0.0}], "records_per_sec": 200000, "type": "records", "values": {"src_ip": {"burst": {"keys": 10000, "label": "dst_subnet", "share": 0.05, "value": 999}, "keys": 1000000, "zipf": 0.0}}}, "http": {"labels": [{"cardinality": 4, "name": "region", "skew": 0.5}, {"cardinality": 25, "name": "service", "skew": 1.1}, {"cardinality": 25, "child_of": "service", "name": "endpoint", "skew": 1.1}, {"cardinality": 4, "name": "status", "skew": 2.0}], "records_per_sec": 100000, "type": "records", "values": {"latency": {"anomaly": {"factor": 10, "label": "endpoint"}, "pareto": 2.0, "scaled_by": "service"}, "user_id": {"keys": 1000000, "zipf": 0.8}}}}, "queries": [{"capability": "cardinality", "expr": {"rqe": {"capability": "cardinality", "grouping": ["dst_subnet"], "id": "synthetic/templates=multigroup/shared=1/t11_distinct_src_by_dst_subnet/keys/5m", "lookback_ms": 300000, "metric": "flows", "value": "src_ip"}}, "grouping": ["dst_subnet"], "interval_secs": 60, "rqes": ["synthetic/templates=multigroup/shared=1/t11_distinct_src_by_dst_subnet/keys/5m"], "sql": "SELECT dst_subnet, COUNT(DISTINCT src_ip) FROM flows WHERE ts BETWEEN DATEADD(s, -300, NOW()) AND NOW() GROUP BY dst_subnet", "table": "flows", "value": "src_ip", "window_secs": 300}, {"capability": "cardinality", "expr": {"rqe": {"capability": "cardinality", "grouping": ["dst_subnet"], "id": "synthetic/templates=multigroup/shared=1/t11_distinct_src_by_dst_subnet/keys/15m", "lookback_ms": 900000, "metric": "flows", "value": "src_ip"}}, "grouping": ["dst_subnet"], "interval_secs": 60, "rqes": ["synthetic/templates=multigroup/shared=1/t11_distinct_src_by_dst_subnet/keys/15m"], "sql": "SELECT dst_subnet, COUNT(DISTINCT src_ip) FROM flows WHERE ts BETWEEN DATEADD(s, -900, NOW()) AND NOW() GROUP BY dst_subnet", "table": "flows", "value": "src_ip", "window_secs": 900}, {"capability": "cardinality", "expr": {"rqe": {"capability": "cardinality", "grouping": ["dst_port"], "id": "synthetic/templates=multigroup/shared=1/t12_distinct_src_by_dst_port/keys/5m", "lookback_ms": 300000, "metric": "flows", "value": "src_ip"}}, "grouping": ["dst_port"], "interval_secs": 60, "rqes": ["synthetic/templates=multigroup/shared=1/t12_distinct_src_by_dst_port/keys/5m"], "sql": "SELECT dst_port, COUNT(DISTINCT src_ip) FROM flows WHERE ts BETWEEN DATEADD(s, -300, NOW()) AND NOW() GROUP BY dst_port", "table": "flows", "value": "src_ip", "window_secs": 300}, {"capability": "cardinality", "expr": {"rqe": {"capability": "cardinality", "grouping": ["dst_port"], "id": "synthetic/templates=multigroup/shared=1/t12_distinct_src_by_dst_port/keys/15m", "lookback_ms": 900000, "metric": "flows", "value": "src_ip"}}, "grouping": ["dst_port"], "interval_secs": 60, "rqes": ["synthetic/templates=multigroup/shared=1/t12_distinct_src_by_dst_port/keys/15m"], "sql": "SELECT dst_port, COUNT(DISTINCT src_ip) FROM flows WHERE ts BETWEEN DATEADD(s, -900, NOW()) AND NOW() GROUP BY dst_port", "table": "flows", "value": "src_ip", "window_secs": 900}, {"capability": "cardinality", "expr": {"rqe": {"capability": "cardinality", "grouping": ["dst_subnet", "proto"], "id": "synthetic/templates=multigroup/shared=1/t12_distinct_src_by_dst_subnet,proto/keys/5m", "lookback_ms": 300000, "metric": "flows", "value": "src_ip"}}, "grouping": ["dst_subnet", "proto"], "interval_secs": 60, "rqes": ["synthetic/templates=multigroup/shared=1/t12_distinct_src_by_dst_subnet,proto/keys/5m"], "sql": "SELECT dst_subnet, proto, COUNT(DISTINCT src_ip) FROM flows WHERE ts BETWEEN DATEADD(s, -300, NOW()) AND NOW() GROUP BY dst_subnet, proto", "table": "flows", "value": "src_ip", "window_secs": 300}, {"capability": "cardinality", "expr": {"rqe": {"capability": "cardinality", "grouping": ["dst_subnet", "proto"], "id": "synthetic/templates=multigroup/shared=1/t12_distinct_src_by_dst_subnet,proto/keys/15m", "lookback_ms": 900000, "metric": "flows", "value": "src_ip"}}, "grouping": ["dst_subnet", "proto"], "interval_secs": 60, "rqes": ["synthetic/templates=multigroup/shared=1/t12_distinct_src_by_dst_subnet,proto/keys/15m"], "sql": "SELECT dst_subnet, proto, COUNT(DISTINCT src_ip) FROM flows WHERE ts BETWEEN DATEADD(s, -900, NOW()) AND NOW() GROUP BY dst_subnet, proto", "table": "flows", "value": "src_ip", "window_secs": 900}, {"capability": "cardinality", "expr": {"rqe": {"capability": "cardinality", "grouping": ["dst_port", "proto"], "id": "synthetic/templates=multigroup/shared=1/t12_distinct_src_by_dst_port,proto/keys/5m", "lookback_ms": 300000, "metric": "flows", "value": "src_ip"}}, "grouping": ["dst_port", "proto"], "interval_secs": 60, "rqes": ["synthetic/templates=multigroup/shared=1/t12_distinct_src_by_dst_port,proto/keys/5m"], "sql": "SELECT dst_port, proto, COUNT(DISTINCT src_ip) FROM flows WHERE ts BETWEEN DATEADD(s, -300, NOW()) AND NOW() GROUP BY dst_port, proto", "table": "flows", "value": "src_ip", "window_secs": 300}, {"capability": "cardinality", "expr": {"rqe": {"capability": "cardinality", "grouping": ["dst_port", "proto"], "id": "synthetic/templates=multigroup/shared=1/t12_distinct_src_by_dst_port,proto/keys/15m", "lookback_ms": 900000, "metric": "flows", "value": "src_ip"}}, "grouping": ["dst_port", "proto"], "interval_secs": 60, "rqes": ["synthetic/templates=multigroup/shared=1/t12_distinct_src_by_dst_port,proto/keys/15m"], "sql": "SELECT dst_port, proto, COUNT(DISTINCT src_ip) FROM flows WHERE ts BETWEEN DATEADD(s, -900, NOW()) AND NOW() GROUP BY dst_port, proto", "table": "flows", "value": "src_ip", "window_secs": 900}, {"capability": "cardinality", "expr": {"rqe": {"capability": "cardinality", "grouping": ["region"], "id": "synthetic/templates=multigroup/shared=1/t14_distinct_users_by_region/keys/5m", "lookback_ms": 300000, "metric": "http", "value": "user_id"}}, "grouping": ["region"], "interval_secs": 60, "rqes": ["synthetic/templates=multigroup/shared=1/t14_distinct_users_by_region/keys/5m"], "sql": "SELECT region, COUNT(DISTINCT user_id) FROM http WHERE ts BETWEEN DATEADD(s, -300, NOW()) AND NOW() GROUP BY region", "table": "http", "value": "user_id", "window_secs": 300}, {"capability": "cardinality", "expr": {"rqe": {"capability": "cardinality", "grouping": ["region"], "id": "synthetic/templates=multigroup/shared=1/t14_distinct_users_by_region/keys/15m", "lookback_ms": 900000, "metric": "http", "value": "user_id"}}, "grouping": ["region"], "interval_secs": 60, "rqes": ["synthetic/templates=multigroup/shared=1/t14_distinct_users_by_region/keys/15m"], "sql": "SELECT region, COUNT(DISTINCT user_id) FROM http WHERE ts BETWEEN DATEADD(s, -900, NOW()) AND NOW() GROUP BY region", "table": "http", "value": "user_id", "window_secs": 900}, {"capability": "cardinality", "expr": {"rqe": {"capability": "cardinality", "grouping": ["service"], "id": "synthetic/templates=multigroup/shared=1/t14_distinct_users_by_service/keys/5m", "lookback_ms": 300000, "metric": "http", "value": "user_id"}}, "grouping": ["service"], "interval_secs": 60, "rqes": ["synthetic/templates=multigroup/shared=1/t14_distinct_users_by_service/keys/5m"], "sql": "SELECT service, COUNT(DISTINCT user_id) FROM http WHERE ts BETWEEN DATEADD(s, -300, NOW()) AND NOW() GROUP BY service", "table": "http", "value": "user_id", "window_secs": 300}, {"capability": "cardinality", "expr": {"rqe": {"capability": "cardinality", "grouping": ["service"], "id": "synthetic/templates=multigroup/shared=1/t14_distinct_users_by_service/keys/15m", "lookback_ms": 900000, "metric": "http", "value": "user_id"}}, "grouping": ["service"], "interval_secs": 60, "rqes": ["synthetic/templates=multigroup/shared=1/t14_distinct_users_by_service/keys/15m"], "sql": "SELECT service, COUNT(DISTINCT user_id) FROM http WHERE ts BETWEEN DATEADD(s, -900, NOW()) AND NOW() GROUP BY service", "table": "http", "value": "user_id", "window_secs": 900}, {"capability": "cardinality", "expr": {"rqe": {"capability": "cardinality", "grouping": ["region", "service"], "id": "synthetic/templates=multigroup/shared=1/t14_distinct_users_by_region,service/keys/5m", "lookback_ms": 300000, "metric": "http", "value": "user_id"}}, "grouping": ["region", "service"], "interval_secs": 60, "rqes": ["synthetic/templates=multigroup/shared=1/t14_distinct_users_by_region,service/keys/5m"], "sql": "SELECT region, service, COUNT(DISTINCT user_id) FROM http WHERE ts BETWEEN DATEADD(s, -300, NOW()) AND NOW() GROUP BY region, service", "table": "http", "value": "user_id", "window_secs": 300}, {"capability": "cardinality", "expr": {"rqe": {"capability": "cardinality", "grouping": ["region", "service"], "id": "synthetic/templates=multigroup/shared=1/t14_distinct_users_by_region,service/keys/15m", "lookback_ms": 900000, "metric": "http", "value": "user_id"}}, "grouping": ["region", "service"], "interval_secs": 60, "rqes": ["synthetic/templates=multigroup/shared=1/t14_distinct_users_by_region,service/keys/15m"], "sql": "SELECT region, service, COUNT(DISTINCT user_id) FROM http WHERE ts BETWEEN DATEADD(s, -900, NOW()) AND NOW() GROUP BY region, service", "table": "http", "value": "user_id", "window_secs": 900}, {"capability": "cardinality", "expr": {"rqe": {"capability": "cardinality", "grouping": ["endpoint"], "id": "synthetic/templates=multigroup/shared=1/t15_distinct_users_by_endpoint/keys/5m", "lookback_ms": 300000, "metric": "http", "value": "user_id"}}, "grouping": ["endpoint"], "interval_secs": 60, "rqes": ["synthetic/templates=multigroup/shared=1/t15_distinct_users_by_endpoint/keys/5m"], "sql": "SELECT endpoint, COUNT(DISTINCT user_id) FROM http WHERE ts BETWEEN DATEADD(s, -300, NOW()) AND NOW() GROUP BY endpoint", "table": "http", "value": "user_id", "window_secs": 300}, {"capability": "cardinality", "expr": {"rqe": {"capability": "cardinality", "grouping": ["endpoint"], "id": "synthetic/templates=multigroup/shared=1/t15_distinct_users_by_endpoint/keys/15m", "lookback_ms": 900000, "metric": "http", "value": "user_id"}}, "grouping": ["endpoint"], "interval_secs": 60, "rqes": ["synthetic/templates=multigroup/shared=1/t15_distinct_users_by_endpoint/keys/15m"], "sql": "SELECT endpoint, COUNT(DISTINCT user_id) FROM http WHERE ts BETWEEN DATEADD(s, -900, NOW()) AND NOW() GROUP BY endpoint", "table": "http", "value": "user_id", "window_secs": 900}, {"capability": "cardinality", "expr": {"rqe": {"capability": "cardinality", "grouping": ["status"], "id": "synthetic/templates=multigroup/shared=1/t15_distinct_users_by_status/keys/5m", "lookback_ms": 300000, "metric": "http", "value": "user_id"}}, "grouping": ["status"], "interval_secs": 60, "rqes": ["synthetic/templates=multigroup/shared=1/t15_distinct_users_by_status/keys/5m"], "sql": "SELECT status, COUNT(DISTINCT user_id) FROM http WHERE ts BETWEEN DATEADD(s, -300, NOW()) AND NOW() GROUP BY status", "table": "http", "value": "user_id", "window_secs": 300}, {"capability": "cardinality", "expr": {"rqe": {"capability": "cardinality", "grouping": ["status"], "id": "synthetic/templates=multigroup/shared=1/t15_distinct_users_by_status/keys/15m", "lookback_ms": 900000, "metric": "http", "value": "user_id"}}, "grouping": ["status"], "interval_secs": 60, "rqes": ["synthetic/templates=multigroup/shared=1/t15_distinct_users_by_status/keys/15m"], "sql": "SELECT status, COUNT(DISTINCT user_id) FROM http WHERE ts BETWEEN DATEADD(s, -900, NOW()) AND NOW() GROUP BY status", "table": "http", "value": 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["synthetic/templates=multigroup/shared=1/t15_distinct_users_by_region,endpoint/keys/15m"], "sql": "SELECT region, endpoint, COUNT(DISTINCT user_id) FROM http WHERE ts BETWEEN DATEADD(s, -900, NOW()) AND NOW() GROUP BY region, endpoint", "table": "http", "value": "user_id", "window_secs": 900}, {"capability": "cardinality", "expr": {"rqe": {"capability": "cardinality", "grouping": ["region", "status"], "id": "synthetic/templates=multigroup/shared=1/t15_distinct_users_by_region,status/keys/5m", "lookback_ms": 300000, "metric": "http", "value": "user_id"}}, "grouping": ["region", "status"], "interval_secs": 60, "rqes": ["synthetic/templates=multigroup/shared=1/t15_distinct_users_by_region,status/keys/5m"], "sql": "SELECT region, status, COUNT(DISTINCT user_id) FROM http WHERE ts BETWEEN DATEADD(s, -300, NOW()) AND NOW() GROUP BY region, status", "table": "http", "value": "user_id", "window_secs": 300}, {"capability": "cardinality", "expr": {"rqe": {"capability": "cardinality", "grouping": ["region", "status"], "id": "synthetic/templates=multigroup/shared=1/t15_distinct_users_by_region,status/keys/15m", "lookback_ms": 900000, "metric": "http", "value": "user_id"}}, "grouping": ["region", "status"], "interval_secs": 60, "rqes": ["synthetic/templates=multigroup/shared=1/t15_distinct_users_by_region,status/keys/15m"], "sql": "SELECT region, status, COUNT(DISTINCT user_id) FROM http WHERE ts BETWEEN DATEADD(s, -900, NOW()) AND NOW() GROUP BY region, status", "table": "http", "value": "user_id", "window_secs": 900}, {"capability": "cardinality", "expr": {"rqe": {"capability": "cardinality", "grouping": ["service", "status"], "id": "synthetic/templates=multigroup/shared=1/t15_distinct_users_by_service,status/keys/5m", "lookback_ms": 300000, "metric": "http", "value": "user_id"}}, "grouping": ["service", "status"], "interval_secs": 60, "rqes": ["synthetic/templates=multigroup/shared=1/t15_distinct_users_by_service,status/keys/5m"], "sql": "SELECT service, status, COUNT(DISTINCT user_id) FROM http WHERE ts BETWEEN DATEADD(s, -300, NOW()) AND NOW() GROUP BY service, status", "table": "http", "value": "user_id", "window_secs": 300}, {"capability": "cardinality", "expr": {"rqe": {"capability": "cardinality", "grouping": ["service", "status"], "id": "synthetic/templates=multigroup/shared=1/t15_distinct_users_by_service,status/keys/15m", "lookback_ms": 900000, "metric": "http", "value": "user_id"}}, "grouping": ["service", "status"], "interval_secs": 60, "rqes": ["synthetic/templates=multigroup/shared=1/t15_distinct_users_by_service,status/keys/15m"], "sql": "SELECT service, status, COUNT(DISTINCT user_id) FROM http WHERE ts BETWEEN DATEADD(s, -900, NOW()) AND NOW() GROUP BY service, status", "table": "http", "value": "user_id", "window_secs": 900}, {"capability": "cardinality", "expr": {"rqe": {"capability": "cardinality", "grouping": ["endpoint", "status"], "id": 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"rolled_up_rqes": 0, - "rqes": 50, - "sla_ms": 10000.0, - "weights": "fargate" - } - ], - "streams": 3, - "weights": [ - { - "name": "cpu", - "w_cpu": 1.0, - "w_mem": 0.0 - }, - { - "name": "fargate", - "w_cpu": 0.0405, - "w_mem": 0.00445 - } - ], - "workload": "synthetic/templates=multigroup/shared=1/tp95" -} diff --git a/rqe-optimizer/src/analytical_cost_model.rs b/rqe-optimizer/src/analytical_cost_model.rs deleted file mode 100644 index 42729b39..00000000 --- a/rqe-optimizer/src/analytical_cost_model.rs +++ /dev/null @@ -1,641 +0,0 @@ -//! Analytical scoring built from measured per-operation costs (`docs/rqe_optimizer_cost_model.md`). -//! -//! Costs split into four phases, each with CPU and memory: -//! - ingest: inserts into open windows, and their state; -//! - merge: folding a query's windows, and the merged state; -//! - query: reading the merged state, and the output; -//! - storage: closed windows kept for lookbacks (no CPU). -//! -//! [`score`] and the MILP share the per-phase functions below. -//! -//! Measured costs are per instance. A deployment holds `card(G)` instances -//! per window, or one if its family keeps [one fixed-size sketch for all -//! groups](crate::FamilyProperties::one_fixed_size_sketch_for_all_groups). -//! A RAQE grouped by `G_r`, a subset of the deployment's `G_d`, is read by -//! merging each `G_r` group's instances into one (a roll-up), so merge and -//! query are priced at `G_r`, ingest and storage at `G_d`. -//! Exact accumulators hold many groups in one instance, but the export -//! divides their memory and merge cost by the group count, so `card(G) ×` -//! prices them correctly too. A [key tracker](crate::Deployment::key_tracker) -//! is priced like an exact accumulator on the same windows, in every phase. -//! A Hydra grid over a schema `Λ` (the deployment's grouping) is one such -//! fixed-size sketch, `I_d = I_r = 1`: its measured insert is per record with -//! the subset fan-out included, a query probes it once per `G_r` group, and -//! its tracker keeps `card(Λ)` keys per window. - -use crate::{ - secs, AtomicCostEntry, Capability, Deployment, LabelSet, Mapping, Millis, Raqe, WorkloadFacts, -}; -use std::collections::{BTreeMap, BTreeSet}; - -pub(crate) const BYTES_PER_GIB: f64 = (1u64 << 30) as f64; - -/// Query output per group: one f64. -// ponytail: estimated, not measured; tiny next to sketch state. -pub const OUTPUT_BYTES_PER_VALUE: f64 = 8.0; -/// Query output per top-k entry: a 64-bit key hash and a count. -pub const OUTPUT_BYTES_PER_TOPK_ENTRY: f64 = 16.0; -/// Bytes per DDSketch bucket: one `u64` count, as sketch-bench's -/// `dd_footprint` counts them. -pub const DD_BYTES_PER_BUCKET: f64 = 8.0; - -/// One phase's resource use. -#[derive(Debug, Clone, Copy, Default, PartialEq)] -pub struct PhaseCost { - /// Mean CPU-seconds per second: the area under the CPU curve over time. - pub cpu_secs_per_sec: f64, - pub memory_bytes: f64, -} - -impl std::ops::AddAssign for PhaseCost { - fn add_assign(&mut self, other: Self) { - self.cpu_secs_per_sec += other.cpu_secs_per_sec; - self.memory_bytes += other.memory_bytes; - } -} - -#[derive(Debug, Clone)] -pub struct PlanCost { - /// Paid once per active deployment. - pub ingest: PhaseCost, - /// Paid per RAQE; memory as if every query runs at once. - pub merge: PhaseCost, - /// Paid per RAQE; memory as if every query runs at once. - pub query: PhaseCost, - /// Paid once per active deployment, sized for its longest lookback. - pub storage: PhaseCost, - /// Index-aligned with the input RAQE slice; display IDs need not be unique. - pub query_latency_ms: Vec, -} - -impl PlanCost { - pub fn cpu_secs_per_sec(&self) -> f64 { - self.phases().map(|phase| phase.cpu_secs_per_sec).sum() - } - - pub fn memory_bytes(&self) -> f64 { - self.phases().map(|phase| phase.memory_bytes).sum() - } - - fn phases(&self) -> impl Iterator { - [&self.ingest, &self.merge, &self.query, &self.storage].into_iter() - } -} - -/// `card(grouping)` on `deployment`'s metric: the number of groups. -fn group_count(deployment: &Deployment, grouping: &LabelSet, facts: &WorkloadFacts) -> f64 { - facts[&deployment.metric].cardinality[grouping] as f64 -} - -/// The sketch and its key tracker, if any, each with its instances per -/// window: 1 for a fixed-size sketch shared by all groups, else `card(G)`. -/// Which part of a deployment a priced config is. -#[derive(Debug, Clone, Copy, PartialEq, Eq)] -enum Part { - Sketch, - KeyTracker, -} - -fn priced_parts<'a>( - deployment: &'a Deployment, - facts: &WorkloadFacts, -) -> impl Iterator { - let properties = deployment.properties(); - assert_eq!( - properties.needs_delta_set_key_tracker, - deployment.key_tracker.is_some(), - "{}: key tracker present iff the family needs one, or the plan is mispriced", - deployment.config.sketch - ); - let groups = group_count(deployment, &deployment.grouping_labels, facts); - let sketch_instances = if properties.one_fixed_size_sketch_for_all_groups { - 1.0 - } else { - groups - }; - std::iter::once((&deployment.config, sketch_instances, Part::Sketch)).chain( - deployment - .key_tracker - .iter() - .map(move |tracker| (tracker, groups, Part::KeyTracker)), - ) -} - -/// `m`: memory of one `config` instance (`deployment`'s sketch or its key -/// tracker) holding `span_ms` of one `grouping` group's samples: a window `x` -/// of `G_d`, or the lookback `L` of `G_r` for a query's merge accumulator. -/// DDSketch on a metric with a known value range holds one store (values are -/// positive) of [`dd_bucket_count`] buckets, capped by the values it holds, -/// `λ · span / card(G)`, as `dd_footprint` caps them by count. The range is the metric's, so this is -/// an upper bound for a group whose own values span less. Every other row, -/// and DDSketch without a range, keeps the measured size. -pub(crate) fn instance_memory_bytes( - config: &AtomicCostEntry, - deployment: &Deployment, - facts: &WorkloadFacts, - grouping: &LabelSet, - span_ms: Millis, -) -> f64 { - let metric = &facts[&deployment.metric]; - let alpha = config.sketch_config["params"]["alpha"].as_f64(); - match (config.sketch.as_str(), metric.value_range, alpha) { - ("dd", Some((lo, hi)), Some(alpha)) => { - let values = metric.arrival_rate_per_sec() * secs(span_ms) - / group_count(deployment, grouping, facts); - dd_bucket_count(lo, hi, alpha).min(values.ceil().max(1.0)) * DD_BYTES_PER_BUCKET - } - _ => config.mem_bytes_per_instance, - } -} - -/// Buckets DDSketch needs for values in `[lo, hi]` at relative accuracy -/// `alpha`: `⌊ln(hi/lo) / ln((1+α)/(1−α))⌋ + 1`, as `dd_footprint` counts them. -pub(crate) fn dd_bucket_count(lo: f64, hi: f64, alpha: f64) -> f64 { - ((hi / lo).ln() / ((1.0 + alpha) / (1.0 - alpha)).ln()).floor() + 1.0 -} - -/// `x / y`: windows each sample is inserted into. -fn open_window_count(deployment: &Deployment) -> f64 { - deployment - .active_instance_count() - .expect("candidate window and slide align") as f64 -} - -/// `L / x`: windows merged per query. -fn merged_window_count(raqe: &Raqe, deployment: &Deployment) -> f64 { - deployment - .query_instance_count(raqe.lookback_ms) - .expect("only eligible pairs are costed") as f64 -} - -/// States one query of `raqe` ends with on `part`: one per `G_r` group, or -/// one for a sketch shared by all groups. -fn answered_instances( - raqe: &Raqe, - deployment: &Deployment, - facts: &WorkloadFacts, - part: Part, -) -> f64 { - if part == Part::Sketch && deployment.properties().one_fixed_size_sketch_for_all_groups { - 1.0 - } else { - group_count(deployment, &raqe.grouping_labels, facts) - } -} - -/// Merges one query of `raqe` does on `part`, which holds `sketch_instances` -/// per window. Each folds two states into one, so it is the states read, -/// `sketch_instances · L/x`, less the `answered_instances` left. Without a -/// roll-up, `card(G) · (L/x − 1)`. -fn merge_count( - raqe: &Raqe, - deployment: &Deployment, - facts: &WorkloadFacts, - sketch_instances: f64, - part: Part, -) -> f64 { - sketch_instances * merged_window_count(raqe, deployment) - - answered_instances(raqe, deployment, facts, part) -} - -/// Per part: CPU `λ · (x/y) · c_ins`; memory `instances · m · (x/y)`. -pub(crate) fn ingest(deployment: &Deployment, facts: &WorkloadFacts) -> PhaseCost { - let open_windows = open_window_count(deployment); - let arrival_rate = facts[&deployment.metric].arrival_rate_per_sec(); - let mut cost = PhaseCost::default(); - for (config, instances, _) in priced_parts(deployment, facts) { - cost += PhaseCost { - cpu_secs_per_sec: arrival_rate * open_windows * config.insert_cpu_secs, - memory_bytes: instances - * instance_memory_bytes( - config, - deployment, - facts, - &deployment.grouping_labels, - deployment.window_ms, - ) - * open_windows, - }; - } - cost -} - -/// Per part: CPU [`merge_count`]` · c_mrg / T`; memory `answered_instances · -/// m`, every merged accumulator held at once, each holding the whole lookback. -/// A direct query (`L == x`) without a roll-up merges nothing and costs neither. -pub(crate) fn merge(raqe: &Raqe, deployment: &Deployment, facts: &WorkloadFacts) -> PhaseCost { - let mut cost = PhaseCost::default(); - for (config, sketch_instances, part) in priced_parts(deployment, facts) { - let merges = merge_count(raqe, deployment, facts, sketch_instances, part); - if merges == 0.0 { - continue; - } - cost += PhaseCost { - cpu_secs_per_sec: merges * config.merge_cpu_secs / secs(raqe.interval_ms), - memory_bytes: answered_instances(raqe, deployment, facts, part) - * instance_memory_bytes( - config, - deployment, - facts, - &raqe.grouping_labels, - raqe.lookback_ms, - ), - }; - } - cost -} - -/// One query's CPU on a deployment's `part`: the cost table's `c_qry`. A -/// top-k sketch's is measured at `TOPK_K` and scaled by the `k` it answers -/// (its `topk_k`, whatever the query's), since the answer cut and output are -/// linear in it; a first approximation. -fn query_cpu_secs(raqe: &Raqe, config: &AtomicCostEntry, part: Part) -> f64 { - if part == Part::Sketch && crate::candidates::is_topk(raqe.capability) { - config.query_cpu_secs * crate::answered_k(config) as f64 / crate::TOPK_K as f64 - } else { - config.query_cpu_secs - } -} - -/// CPU `card(G_r) · Σ c_qry / T`, one probe per answered group of each part; -/// memory `card(G_r) ·` output bytes per group. -pub(crate) fn query(raqe: &Raqe, deployment: &Deployment, facts: &WorkloadFacts) -> PhaseCost { - let groups = group_count(deployment, &raqe.grouping_labels, facts); - let output_bytes_per_group = match raqe.capability { - Capability::TopKByValue | Capability::TopKByCount => { - crate::answered_k(&deployment.config) as f64 * OUTPUT_BYTES_PER_TOPK_ENTRY - } - _ => OUTPUT_BYTES_PER_VALUE, - }; - let query_cpu_secs_per_group: f64 = priced_parts(deployment, facts) - .map(|(config, _, part)| query_cpu_secs(raqe, config, part)) - .sum(); - PhaseCost { - cpu_secs_per_sec: groups * query_cpu_secs_per_group / secs(raqe.interval_ms), - memory_bytes: groups * output_bytes_per_group, - } -} - -/// Closed windows kept to answer `raqe`, per part: -/// `instances · m · ((L − x)/y + 1)`. -pub(crate) fn storage_bytes(raqe: &Raqe, deployment: &Deployment, facts: &WorkloadFacts) -> f64 { - let closed_windows = deployment - .closed_instance_count(raqe.lookback_ms) - .expect("only eligible pairs are costed") as f64; - priced_parts(deployment, facts) - .map(|(config, instances, _)| { - instances - * instance_memory_bytes( - config, - deployment, - facts, - &deployment.grouping_labels, - deployment.window_ms, - ) - * closed_windows - }) - .sum() -} - -/// Serial CPU time of one query, in ms, summed over parts: -/// `card(G_r) · c_qry + `[`merge_count`]` · c_mrg`. -pub(crate) fn query_latency_ms(raqe: &Raqe, deployment: &Deployment, facts: &WorkloadFacts) -> f64 { - let groups = group_count(deployment, &raqe.grouping_labels, facts); - 1000.0 - * priced_parts(deployment, facts) - .map(|(config, sketch_instances, part)| { - groups * query_cpu_secs(raqe, config, part) - + merge_count(raqe, deployment, facts, sketch_instances, part) - * config.merge_cpu_secs - }) - .sum::() -} - -/// Parallel ingest workers of `deployment`, split by sample: `k = ⌈ρ⌉` for -/// its ingest CPU `ρ`, at least one, since sketch-bench's operations are -/// single-threaded (a worker uses at most one core). -pub fn ingest_workers(deployment: &Deployment, facts: &WorkloadFacts) -> f64 { - ingest(deployment, facts).cpu_secs_per_sec.ceil().max(1.0) -} - -/// CPU-seconds to compact one closed window: merge the `k` workers' partial -/// instances of every group into one, `(k − 1) · Σ_parts instances · c_mrg`. -/// Zero with one worker. -pub fn compaction_secs(deployment: &Deployment, facts: &WorkloadFacts) -> f64 { - let extra = ingest_workers(deployment, facts) - 1.0; - extra - * priced_parts(deployment, facts) - .map(|(config, instances, _)| instances * config.merge_cpu_secs) - .sum::() -} - -/// Bytes a compaction holds while it runs: the closed window's `k` partial -/// copies, `k · Σ_parts instances · m` (one window's ingest memory per -/// worker, times `k`). The compacted result takes the storage slot the oldest -/// window frees, so it is not counted again. Zero with one worker. -pub fn compaction_bytes(deployment: &Deployment, facts: &WorkloadFacts) -> f64 { - let workers = ingest_workers(deployment, facts); - if workers <= 1.0 { - return 0.0; - } - workers * ingest(deployment, facts).memory_bytes / open_window_count(deployment) -} - -/// One firing's chain on its own cores, ms: the newest window's compaction, -/// then the query (merge, then estimate). With elastic CPU this is the -/// firing's latency. -pub fn chain_ms(raqe: &Raqe, deployment: &Deployment, facts: &WorkloadFacts) -> f64 { - 1000.0 * compaction_secs(deployment, facts) + query_latency_ms(raqe, deployment, facts) -} - -pub fn score( - raqes: &[Raqe], - deployments: &[Deployment], - mapping: &Mapping, - facts: &WorkloadFacts, -) -> PlanCost { - assert_eq!( - mapping.len(), - raqes.len(), - "mapping must have one deployment per RAQE" - ); - let active_deployments: BTreeSet = mapping.iter().copied().collect(); - let mut ingest_cost = PhaseCost::default(); - for &deployment_index in &active_deployments { - ingest_cost += ingest(&deployments[deployment_index], facts); - } - - let mut storage_bytes_by_deployment: BTreeMap = BTreeMap::new(); - let mut merge_cost = PhaseCost::default(); - let mut query_cost = PhaseCost::default(); - let query_latency_ms = raqes - .iter() - .zip(mapping) - .map(|(raqe, &deployment_index)| { - let deployment = &deployments[deployment_index]; - let stored = storage_bytes_by_deployment - .entry(deployment_index) - .or_default(); - *stored = stored.max(storage_bytes(raqe, deployment, facts)); - merge_cost += merge(raqe, deployment, facts); - query_cost += query(raqe, deployment, facts); - query_latency_ms(raqe, deployment, facts) - }) - .collect(); - - PlanCost { - ingest: ingest_cost, - merge: merge_cost, - query: query_cost, - storage: PhaseCost { - cpu_secs_per_sec: 0.0, - memory_bytes: storage_bytes_by_deployment.values().sum(), - }, - query_latency_ms, - } -} - -#[cfg(test)] -mod tests { - use super::*; - use crate::test_support::{deployment, facts, raqe}; - - #[test] - fn dd_bucket_count_is_log_range_over_log_gamma() { - // alpha = 1/3 gives gamma = 2: buckets [1, 2), [2, 4), ... up to 1000. - assert_eq!(dd_bucket_count(1.0, 1000.0, 1.0 / 3.0), 10.0); - assert_eq!(dd_bucket_count(5.0, 5.0, 0.01), 1.0); - // 1 ms to 60 s at 1%: ln(60000) / ln(1.01 / 0.99) = 550.1. - assert_eq!(dd_bucket_count(0.001, 60.0, 0.01), 551.0); - } - - #[test] - fn dd_memory_comes_from_the_value_range_when_known() { - let mut dd = deployment(1_000.0, 0.0, 0.0, 0.0, 60_000, 60_000); - dd.config.sketch = "dd".into(); - dd.config.sketch_config = serde_json::json!({"params": {"alpha": 1.0 / 3.0}}); - let mut ranged = facts(1, 1); - ranged - .get_mut(crate::test_support::METRIC) - .unwrap() - .value_range = Some((1.0, 1000.0)); - - let window = |d: &Deployment| d.window_ms; - assert_eq!( - instance_memory_bytes(&dd.config, &dd, &ranged, &dd.grouping_labels, window(&dd)), - 80.0 - ); // 10 buckets × 8 B - assert_eq!(ingest(&dd, &ranged).memory_bytes, 80.0); - - // 1 sample/sec into a 5 s window: 5 values, fewer than the 10 buckets. - let short = Deployment { - window_ms: 5_000, - slide_ms: 5_000, - ..dd.clone() - }; - assert_eq!(ingest(&short, &ranged).memory_bytes, 40.0); - // A 15 s query folds 3 windows: its accumulator holds 15 values, so - // the 10-bucket range is the cap again, not the window's 5. - assert_eq!( - merge(&raqe(15_000, 15_000), &short, &ranged).memory_bytes, - 80.0 - ); - assert_eq!( - merge(&raqe(10_000, 10_000), &short, &ranged).memory_bytes, - 80.0 - ); - - // No range, or not DDSketch: the measured size. - assert_eq!( - instance_memory_bytes( - &dd.config, - &dd, - &facts(1, 1), - &dd.grouping_labels, - window(&dd) - ), - 1_000.0 - ); - let cms = deployment(1_000.0, 0.0, 0.0, 0.0, 60_000, 60_000); - assert_eq!( - instance_memory_bytes( - &cms.config, - &cms, - &ranged, - &cms.grouping_labels, - window(&cms) - ), - 1_000.0 - ); - } - - #[test] - fn scores_each_phase() { - // 4 groups and 7 samples/sec. Window 20 s sliding by 10 s: 2 open - // windows. Lookback 60 s every 30 s: 3 windows merged, and - // (60 − 20)/10 + 1 = 5 closed windows stored. - let deployment = deployment(10.0, 2.0, 3.0, 5.0, 20_000, 10_000); - let result = score( - &[raqe(60_000, 30_000)], - &[deployment], - &vec![0], - &facts(4, 7), - ); - - assert_eq!(result.ingest.cpu_secs_per_sec, 28.0); // 7 × 2 × 2 - assert_eq!(result.ingest.memory_bytes, 80.0); // 4 × 10 × 2 - assert_eq!(result.merge.cpu_secs_per_sec, 24.0 / 30.0); // 4 × 2 × 3 / 30 - assert_eq!(result.merge.memory_bytes, 40.0); // 4 × 10 - assert_eq!(result.query.cpu_secs_per_sec, 20.0 / 30.0); // 4 × 5 / 30 - assert_eq!(result.query.memory_bytes, 2048.0); // 4 × 32 × 16 - assert_eq!(result.storage.cpu_secs_per_sec, 0.0); - assert_eq!(result.storage.memory_bytes, 200.0); // 4 × 10 × 5 - assert_eq!(result.query_latency_ms, vec![44_000.0]); // 4 × (5 + 2 × 3) s - assert!((result.cpu_secs_per_sec() - (28.0 + 44.0 / 30.0)).abs() < 1e-12); - assert_eq!(result.memory_bytes(), 80.0 + 40.0 + 2048.0 + 200.0); - } - - #[test] - fn a_direct_query_stores_one_closed_window_and_never_merges() { - let deployment = deployment(10.0, 1.0, 1.0, 1.0, 60_000, 60_000); - let result = score( - &[raqe(60_000, 60_000)], - &[deployment], - &vec![0], - &facts(1, 1), - ); - assert_eq!(result.storage.memory_bytes, 10.0); - assert_eq!(result.merge, PhaseCost::default()); - } - - #[test] - fn shared_deployment_stores_for_its_longest_lookback() { - let deployment = deployment(10.0, 1.0, 1.0, 1.0, 10_000, 10_000); - let raqes = [raqe(60_000, 10_000), raqe(600_000, 10_000)]; - let result = score(&raqes, &[deployment], &vec![0, 0], &facts(2, 2)); - // 2 groups × 10 bytes × ((600 − 10)/10 + 1) closed windows. - assert_eq!(result.storage.memory_bytes, 1200.0); - // Ingest is paid once; merge once per RAQE. - assert_eq!(result.ingest.cpu_secs_per_sec, 2.0); - // Two RAQEs × 2 groups × 10 bytes. - assert_eq!(result.merge.memory_bytes, 2.0 * 2.0 * 10.0); - } - #[test] - fn a_shared_sketch_is_priced_once_and_its_tracker_per_group() { - // Same windows as `scores_each_phase`: 2 open, 3 merged, 5 closed; - // 4 groups, 7 samples/sec. Hydra: 100 bytes for all groups. Tracker: - // 10 bytes per group. - let mut deployment = deployment(100.0, 2.0, 3.0, 5.0, 20_000, 10_000); - deployment.capability = Capability::Quantile; - deployment.config.sketch = "hydra-kll".into(); - deployment.key_tracker = Some(AtomicCostEntry { - sketch: crate::KEY_TRACKER_FAMILY.into(), - mem_bytes_per_instance: 10.0, - insert_cpu_secs: 1.0, - merge_cpu_secs: 1.0, - query_cpu_secs: 1.0, - ..deployment.config.clone() - }); - let raqe = Raqe { - capability: Capability::Quantile, - ..raqe(60_000, 30_000) - }; - let result = score(&[raqe], &[deployment], &vec![0], &facts(4, 7)); - - assert_eq!(result.ingest.cpu_secs_per_sec, 7.0 * 2.0 * (2.0 + 1.0)); - assert_eq!(result.ingest.memory_bytes, 100.0 * 2.0 + 4.0 * 10.0 * 2.0); - assert_eq!( - result.merge.cpu_secs_per_sec, - (2.0 * 3.0 + 4.0 * 2.0 * 1.0) / 30.0 - ); - assert_eq!(result.merge.memory_bytes, 100.0 + 4.0 * 10.0); - assert_eq!(result.query.cpu_secs_per_sec, 4.0 * (5.0 + 1.0) / 30.0); - assert_eq!(result.query.memory_bytes, 4.0 * OUTPUT_BYTES_PER_VALUE); - assert_eq!(result.storage.memory_bytes, 100.0 * 5.0 + 4.0 * 10.0 * 5.0); - // 4 × (5 + 1) query + 2 × 3 Hydra merges + 4 × 2 × 1 tracker merges, in s. - assert_eq!(result.query_latency_ms, vec![38_000.0]); - } - - /// Query CPU and output scale by the k the deployment answers, not the - /// RAQE's: a k = 32 deployment serving a top-10 query still cuts and - /// returns 32; one built at `topk_k = 10` cuts and returns 10. - #[test] - fn query_cost_scales_by_the_deployments_answered_k() { - let ten = Raqe { - topk_k: Some(10), - ..raqe(60_000, 60_000) - }; - let at_32 = deployment(10.0, 1.0, 1.0, 32.0, 60_000, 60_000); - let cost = query(&ten, &at_32, &facts(1, 1)); - assert_eq!(cost.cpu_secs_per_sec, 32.0 / 60.0); - assert_eq!(cost.memory_bytes, 32.0 * OUTPUT_BYTES_PER_TOPK_ENTRY); - - let mut at_10 = at_32.clone(); - at_10.config.sketch_config["params"]["topk_k"] = 10.into(); - let cost = query(&ten, &at_10, &facts(1, 1)); - assert_eq!(cost.cpu_secs_per_sec, 10.0 / 60.0); - assert_eq!(cost.memory_bytes, 10.0 * OUTPUT_BYTES_PER_TOPK_ENTRY); - } - - #[test] - #[should_panic(expected = "key tracker present iff the family needs one")] - fn a_family_that_needs_a_tracker_is_never_priced_without_one() { - let mut deployment = deployment(100.0, 1.0, 1.0, 1.0, 60_000, 60_000); - deployment.config.sketch = "hydra-kll".into(); - ingest(&deployment, &facts(1, 1)); - } - - /// p99 by service from KLL by (service, endpoint), every per-instance - /// cost 1: each query reads 50 fine states per window and leaves 5. - #[test] - fn a_roll_up_merges_every_fine_window_into_its_coarse_group() { - use crate::test_support::{kll_by, quantile_by, service_endpoint_facts}; - let facts = service_endpoint_facts(); - let by_endpoint = kll_by(&["service", "endpoint"], 60_000); - let hourly = quantile_by(&["service"], 3_600_000, 60_000); - let merges = 50.0 * 60.0 - 5.0; - assert_eq!( - merge(&hourly, &by_endpoint, &facts), - PhaseCost { - cpu_secs_per_sec: merges / 60.0, - memory_bytes: 5.0, - } - ); - assert_eq!( - query(&hourly, &by_endpoint, &facts).cpu_secs_per_sec, - 5.0 / 60.0 - ); - assert_eq!( - query_latency_ms(&hourly, &by_endpoint, &facts), - 1000.0 * (5.0 + merges) - ); - // A direct query still folds each service's 10 endpoints into one. - let direct = quantile_by(&["service"], 60_000, 60_000); - assert_eq!( - merge(&direct, &by_endpoint, &facts).cpu_secs_per_sec, - 45.0 / 60.0 - ); - } - - /// A roll-up's DDSketch accumulator holds a coarse group's values: 50 - /// samples/s over a 2 s lookback into 5 services is 20 values each, past - /// the 10-bucket range, so 80 B per service, not the 16 B two per-endpoint - /// values would need. - #[test] - fn a_dd_roll_up_sizes_its_accumulator_at_the_coarse_grouping() { - use crate::test_support::{kll_by, quantile_by, service_endpoint_facts}; - let mut facts = service_endpoint_facts(); - facts - .get_mut(crate::test_support::METRIC) - .unwrap() - .value_range = Some((1.0, 1000.0)); - let mut by_endpoint = kll_by(&["service", "endpoint"], 1_000); - by_endpoint.config.sketch = "dd".into(); - by_endpoint.config.sketch_config = serde_json::json!({"params": {"alpha": 1.0 / 3.0}}); - let by_service = quantile_by(&["service"], 2_000, 2_000); - assert_eq!( - merge(&by_service, &by_endpoint, &facts).memory_bytes, - 5.0 * 80.0 - ); - } -} diff --git a/rqe-optimizer/src/autosketch.rs b/rqe-optimizer/src/autosketch.rs deleted file mode 100644 index b82dd56e..00000000 --- a/rqe-optimizer/src/autosketch.rs +++ /dev/null @@ -1,732 +0,0 @@ -//! AutoSketch-Adapted: the per-query configuration search of AutoSketch -//! (Sun et al., NSDI '24, Algorithm 4), used as a baseline against the -//! batch MILP. Ported from ASAPQuery-backend -//! `data_plane/examples/autosketch_comparison.rs` and generalized from its -//! fixed CMS width/depth grid to each variant's measured parameter axes. -//! -//! AutoSketch plans one query at a time, never shares state across queries, -//! and constrains accuracy only. So every RAQE is searched independently, -//! gets its own deployment, and latency is not considered. -//! -//! One extension over the paper: AutoSketch's compiler maps an operator to one -//! sketch algorithm and tunes its parameters, while this search covers every -//! deployable variant serving the RAQE's capability and keeps the cheapest, -//! so it chooses from the same sketches as the MILP, except those shared by -//! all groups (sketch-bench#159). - -use crate::candidates::gcd; -use crate::{AtomicCostEntry, Deployment, Mapping, Millis, Raqe}; -use std::collections::{BTreeMap, VecDeque}; -use std::time::Instant; - -/// One RAQE's search. `selected` and `probes` index into the cost table. -#[derive(Debug, Clone)] -pub struct RaqeSearch { - pub raqe_id: String, - /// Smallest feasible configuration found; `None` means unservable. - pub selected: Option, - /// Distinct configurations whose accuracy was evaluated, in order. - pub probes: Vec, - pub search_wall_secs: f64, -} - -/// A whole-workload plan: one dedicated deployment per RAQE, so identical -/// choices are still paid for once per RAQE, and the identity mapping. -#[derive(Debug, Clone)] -pub struct AutoSketchPlan { - pub deployments: Vec, - pub mapping: Mapping, - pub searches: Vec, -} - -/// One sliding sketch per query window: `x = S`, `y = T`, or `gcd(S, T)` -/// when `T` does not divide `S`, so the window still tiles into slides. -pub fn window_adapter(raqe: &Raqe) -> (Millis, Millis) { - (raqe.lookback_ms, gcd(raqe.lookback_ms, raqe.interval_ms)) -} - -/// Search every RAQE independently. `accuracy` is the benchmark oracle: a -/// configuration's accuracy in its family's metric ([`crate::accuracy_key`]), -/// or `None` when it was not measured. Returns the IDs of unservable RAQEs. -/// `allow_undeployable_families` as in -/// [`crate::Capability::candidate_families`]. -pub fn plan( - raqes: &[Raqe], - costs: &[AtomicCostEntry], - seed: u64, - allow_undeployable_families: bool, - mut accuracy: impl FnMut(&Raqe, &AtomicCostEntry) -> Option, -) -> Result> { - // One resolution per distinct (k, top-k?), not per RAQE. - let keys: Vec<(u64, bool)> = raqes - .iter() - .map(|r| (r.topk_k(), crate::candidates::is_topk(r.capability))) - .collect(); - let mut resolutions: BTreeMap<(u64, bool), Resolved> = BTreeMap::new(); - for (raqe, &key) in raqes.iter().zip(&keys) { - resolutions - .entry(key) - .or_insert_with(|| resolve_for(raqe, costs)); - } - let searches: Vec<_> = raqes - .iter() - .zip(&keys) - .map(|(raqe, key)| { - search_resolved( - raqe, - &resolutions[key], - seed, - allow_undeployable_families, - &mut accuracy, - ) - }) - .collect(); - let unservable: Vec<_> = searches - .iter() - .filter(|s| s.selected.is_none()) - .map(|s| s.raqe_id.clone()) - .collect(); - if !unservable.is_empty() { - return Err(unservable); - } - let deployments = raqes - .iter() - .zip(&searches) - .zip(&keys) - .map(|((raqe, s), key)| { - let (window_ms, slide_ms) = window_adapter(raqe); - Deployment { - capability: raqe.capability, - metric: raqe.metric.clone(), - spatial_filter: raqe.spatial_filter.clone(), - grouping_labels: raqe.grouping_labels.clone(), - config: resolutions[key].costs - [s.selected.expect("unservable RAQEs returned above")] - .clone(), - window_ms, - slide_ms, - key_tracker: None, - } - }) - .collect(); - Ok(AutoSketchPlan { - deployments, - mapping: (0..raqes.len()).collect(), - searches, - }) -} - -/// `costs` as a top-`k` RAQE sees them, index for index: each heap top-k -/// config's smallest-heap row priced at the heap a one-window answer needs -/// (`k`, floored at the smallest measured heap) by the MILP's own -/// [`crate::candidates::heap_row`], and its other heap rows masked out of -/// the search. AutoSketch never merges (x = S), so heap is no search axis. -/// For a top-k RAQE (`topk`), rows that can't answer `k` are masked too. -fn resolve_heaps(costs: &[AtomicCostEntry], k: u64, topk: bool) -> Resolved { - let mut resolved = costs.to_vec(); - let mut usable = vec![true; costs.len()]; - // Heap rows by family and counter shape, then by heap, with their index. - let mut groups: BTreeMap<(String, String), BTreeMap> = BTreeMap::new(); - for (i, row) in costs.iter().enumerate() { - if let Some(heap) = crate::heap_capacity(row) { - let mut shape = row.sketch_config["params"].clone(); - if let Some(params) = shape.as_object_mut() { - params.remove("heap"); - } - groups - .entry((row.sketch.clone(), shape.to_string())) - .or_default() - .insert(heap, i); - } - } - for by_heap in groups.values() { - let rows: BTreeMap = by_heap - .iter() - .map(|(&heap, &i)| (heap, &costs[i])) - .collect(); - by_heap.values().for_each(|&i| usable[i] = false); - let (Some((_, &smallest)), Some(entry)) = ( - by_heap.first_key_value(), - crate::candidates::heap_row(&rows, k, k), - ) else { - continue; - }; - resolved[smallest] = entry; - usable[smallest] = true; - } - // A fixed-k top-k family answers TOPK_K only. - if topk { - for (i, row) in resolved.iter().enumerate() { - usable[i] &= crate::serves_topk_k(row, k); - } - } - Resolved { - costs: resolved, - usable, - } -} - -/// `costs` as a RAQE sees them ([`resolve_heaps`]) and which rows the -/// search may use. -struct Resolved { - costs: Vec, - usable: Vec, -} - -/// Algorithm 4 for one RAQE: LHS seeds per sketch variant, then -/// feasibility-directed neighbor search with the paper's pruning and -/// stopping rules. Minimizes memory per instance, then insert CPU. -pub fn search( - raqe: &Raqe, - costs: &[AtomicCostEntry], - seed: u64, - allow_undeployable_families: bool, - accuracy: impl FnMut(&Raqe, &AtomicCostEntry) -> Option, -) -> RaqeSearch { - let resolved = resolve_for(raqe, costs); - search_resolved(raqe, &resolved, seed, allow_undeployable_families, accuracy) -} - -fn resolve_for(raqe: &Raqe, costs: &[AtomicCostEntry]) -> Resolved { - resolve_heaps( - costs, - raqe.topk_k(), - crate::candidates::is_topk(raqe.capability), - ) -} - -/// [`search`] over costs already resolved for `raqe`. -fn search_resolved( - raqe: &Raqe, - resolved: &Resolved, - seed: u64, - allow_undeployable_families: bool, - mut accuracy: impl FnMut(&Raqe, &AtomicCostEntry) -> Option, -) -> RaqeSearch { - let started = Instant::now(); - let (costs, usable) = (&resolved.costs[..], &resolved.usable); - let grids: Vec = raqe - .capability - .candidate_families(allow_undeployable_families) - // ponytail: ranking compares per-instance memory, which a sketch - // shared by all groups isn't comparable on; sketch-bench#159. - .filter(|variant| !crate::family_properties(variant).one_fixed_size_sketch_for_all_groups) - .filter_map(|variant| Grid::new(variant, costs, usable)) - .collect(); - - let mut pending = VecDeque::new(); - for grid in &grids { - let seeds = grid.lhs(seed); - if seeds.is_empty() { - // A sparse table can leave no LHS point measured. Seed the - // cheapest configuration so the variant is still searched. - if let Some(&cheapest) = grid - .points - .values() - .min_by(|&&a, &&b| compare_resources(&costs[a], &costs[b])) - { - pending.push_back((cheapest, None)); - } - } - pending.extend(seeds.into_iter().map(|index| (index, None))); - } - - let mut evaluated: BTreeMap = BTreeMap::new(); - let mut probes = Vec::new(); - let mut best: Option = None; - let cheaper = |a: usize, b: usize| compare_resources(&costs[a], &costs[b]).is_lt(); - - while let Some((index, initial_feasible)) = pending.pop_front() { - // EXAMINE rule (1): a configuration is evaluated, and expanded, once. - if evaluated.contains_key(&index) { - continue; - } - probes.push(index); - let feasible = raqe.meets_sla(&costs[index].sketch, accuracy(raqe, &costs[index])); - evaluated.insert(index, feasible); - if feasible && best.is_none_or(|b| cheaper(index, b)) { - best = Some(index); - } - // Stopping rule: a path that started feasible stops shrinking at the - // first failure; one that started infeasible stops at the first success. - if initial_feasible.unwrap_or(feasible) != feasible { - continue; - } - let grid = grids - .iter() - .find(|g| g.variant == costs[index].sketch) - .expect("every pending index comes from a grid"); - for neighbor in grid.neighbors(index) { - // Shrink when feasible, grow when not. - let toward = if feasible { - cheaper(neighbor, index) - } else { - cheaper(index, neighbor) - }; - // Prune anything no cheaper than a configuration already known to - // satisfy the intent. - let useful = best.is_none_or(|b| cheaper(neighbor, b)); - if toward && useful && !evaluated.contains_key(&neighbor) { - pending.push_back((neighbor, Some(feasible))); - } - } - } - - RaqeSearch { - raqe_id: raqe.id.clone(), - selected: best, - probes, - search_wall_secs: started.elapsed().as_secs_f64(), - } -} - -/// AutoSketch's resource score with no ALUs: memory, with insert CPU only -/// breaking ties. -fn compare_resources(a: &AtomicCostEntry, b: &AtomicCostEntry) -> std::cmp::Ordering { - a.mem_bytes_per_instance - .total_cmp(&b.mem_bytes_per_instance) - .then(a.insert_cpu_secs.total_cmp(&b.insert_cpu_secs)) -} - -/// One sketch variant's measured configurations, indexed by their numeric -/// parameter values (`sketch_config.params`). -struct Grid { - variant: String, - /// Sorted distinct measured values per parameter name. - axes: BTreeMap>, - /// Parameter vector (in `axes` order) -> first matching cost-table row. - points: BTreeMap, usize>, -} - -impl Grid { - /// `k`: a heap top-k variant's grid holds the smallest measured heap - /// that answers a top-`k` query. - /// `usable` masks the rows [`resolve_heaps`] set aside. - fn new(variant: &str, costs: &[AtomicCostEntry], usable: &[bool]) -> Option { - let rows: Vec<(usize, BTreeMap)> = costs - .iter() - .enumerate() - .filter(|&(i, c)| c.sketch == variant && usable[i]) - .map(|(i, c)| (i, numeric_params(&c.sketch_config))) - .collect(); - if rows.is_empty() { - return None; - } - let mut axes: BTreeMap> = BTreeMap::new(); - for (_, params) in &rows { - for (name, &value) in params { - axes.entry(name.clone()).or_default().push(value); - } - } - for values in axes.values_mut() { - values.sort_by(f64::total_cmp); - values.dedup(); - } - let mut points = BTreeMap::new(); - for (index, params) in rows { - let key: Option> = axes - .keys() - .map(|name| params.get(name).map(|v| v.to_bits())) - .collect(); - if let Some(key) = key { - points.entry(key).or_insert(index); - } - } - Some(Grid { - variant: variant.to_string(), - axes, - points, - }) - } - - fn key_of(&self, index: usize) -> &Vec { - self.points - .iter() - .find(|(_, &i)| i == index) - .map(|(key, _)| key) - .expect("index belongs to this grid") - } - - /// Latin hypercube seeds: each sample takes a distinct value on every - /// axis. With several axes, the sample count is the shortest axis length - /// (the paper's Figure 4); a single axis gets one random seed, since one - /// sample per value would make the search exhaustive. - fn lhs(&self, seed: u64) -> Vec { - let mut rng = Rng(seed); - let count = if self.axes.len() > 1 { - self.axes.values().map(Vec::len).min().unwrap_or(0) - } else { - 1 - }; - let columns: Vec> = self - .axes - .values() - .map(|values| { - let mut shuffled: Vec = values.iter().map(|v| v.to_bits()).collect(); - rng.shuffle(&mut shuffled); - shuffled - }) - .collect(); - (0..count) - .filter_map(|sample| { - let key: Vec = columns.iter().map(|column| column[sample]).collect(); - self.points.get(&key).copied() - }) - .collect() - } - - /// Measured configurations that move one parameter to its adjacent - /// measured value. - fn neighbors(&self, index: usize) -> Vec { - let key = self.key_of(index); - let mut out = Vec::new(); - for (axis, values) in self.axes.values().enumerate() { - let position = values - .iter() - .position(|v| v.to_bits() == key[axis]) - .expect("key values come from the axes"); - let adjacent = [position.checked_sub(1), Some(position + 1)]; - for next in adjacent.into_iter().flatten().filter(|&p| p < values.len()) { - let mut moved = key.clone(); - moved[axis] = values[next].to_bits(); - if let Some(&neighbor) = self.points.get(&moved) { - out.push(neighbor); - } - } - } - out - } -} - -/// Search axes: numeric params but the heap and k, which -/// [`resolve_heaps`] fixes per RAQE. -fn numeric_params(sketch_config: &serde_json::Value) -> BTreeMap { - sketch_config - .get("params") - .and_then(serde_json::Value::as_object) - .map(|params| { - params - .iter() - .filter(|(name, _)| !matches!(name.as_str(), "heap" | "topk_k")) - .filter_map(|(name, value)| value.as_f64().map(|v| (name.clone(), v))) - .collect() - }) - .unwrap_or_default() -} - -/// SplitMix64, as in the backend port: deterministic and dependency-free. -struct Rng(u64); - -impl Rng { - fn next(&mut self) -> u64 { - self.0 = self.0.wrapping_add(0x9e37_79b9_7f4a_7c15); - let mut z = self.0; - z = (z ^ (z >> 30)).wrapping_mul(0xbf58_476d_1ce4_e5b9); - z = (z ^ (z >> 27)).wrapping_mul(0x94d0_49bb_1331_11eb); - z ^ (z >> 31) - } - - fn shuffle(&mut self, values: &mut [T]) { - for i in (1..values.len()).rev() { - values.swap(i, (self.next() % (i as u64 + 1)) as usize); - } - } -} - -#[cfg(test)] -mod tests { - use super::*; - use crate::analytical_cost_model::{score, PhaseCost}; - use crate::candidates::is_eligible; - use crate::test_support::{facts, measured_at, METRIC}; - use crate::{row_accuracy, Capability, LabelSet}; - use std::collections::BTreeSet; - - fn cms(rows: u64, cols: u64) -> AtomicCostEntry { - AtomicCostEntry { - sketch: "cms-heap-topk-fastpath-vector2d".into(), - sketch_config: serde_json::json!({ - "algorithm": "cms-heap-topk-fastpath-vector2d", - "params": {"rows": rows, "cols": cols}, - }), - mem_bytes_per_instance: (rows * cols * 4) as f64, - insert_cpu_secs: 1e-8 * rows as f64, - merge_cpu_secs: 1e-6, - query_cpu_secs: 1e-6, - // Precision rises with width and depth, so feasibility is monotone. - query_accuracy: BTreeMap::from([( - "precision_at_k".into(), - 1.0 - 1.0 / (rows * cols) as f64, - )]), - accuracy_metric: "precision_at_k".into(), - measured_at: measured_at(), - } - } - - fn grid(rows: &[u64], cols: &[u64]) -> Vec { - rows.iter() - .flat_map(|&r| cols.iter().map(move |&c| cms(r, c))) - .collect() - } - - /// A top-k RAQE whose precision floor is `1 - error_budget`. - fn raqe(id: &str, lookback: Millis, interval: Millis, error_budget: f64) -> Raqe { - Raqe { - id: id.into(), - capability: Capability::TopKByValue, - lookback_ms: lookback, - interval_ms: interval, - metric: METRIC.into(), - spatial_filter: String::new(), - grouping_labels: LabelSet::new(), - accuracy_sla: 1.0 - error_budget, - latency_sla_ms: None, - topk_k: None, - accuracy_covers_share: None, - } - } - - fn table_accuracy(_: &Raqe, config: &AtomicCostEntry) -> Option { - Some(row_accuracy(config)) - } - - fn cheapest_feasible(raqe: &Raqe, costs: &[AtomicCostEntry]) -> usize { - (0..costs.len()) - .filter(|&i| raqe.meets_sla(&costs[i].sketch, table_accuracy(raqe, &costs[i]))) - .min_by(|&a, &b| compare_resources(&costs[a], &costs[b])) - .unwrap() - } - - #[test] - fn picks_smallest_feasible_config() { - let costs = grid(&[1, 2, 3], &[100, 200, 400, 800]); - // Needs rows * cols >= 1000: rows=3 cols=400 (1200 counters) is the - // smallest; rows=2 cols=800 and rows=3 cols=800 also pass. - let r = raqe("r", 3_600_000, 60_000, 1.0 / 1_000.0); - let found = search(&r, &costs, 7, false, table_accuracy); - assert_eq!(found.selected, Some(cheapest_feasible(&r, &costs))); - } - - #[test] - fn evaluates_fewer_configs_than_exhaustive_search() { - let rows: Vec = (1..=8).collect(); - let cols: Vec = (0..8).map(|i| 64 << i).collect(); - let costs = grid(&rows, &cols); - let r = raqe("r", 3_600_000, 60_000, 1.0 / 2_000.0); - let found = search(&r, &costs, 42, false, table_accuracy); - assert!(found.selected.is_some()); - assert!( - found.probes.len() < costs.len(), - "{} probes", - found.probes.len() - ); - let unique: BTreeSet<_> = found.probes.iter().collect(); - assert_eq!(unique.len(), found.probes.len()); - } - - #[test] - fn never_shares_identical_choices() { - let costs = grid(&[2, 3], &[256, 512]); - let raqes = vec![ - raqe("a", 3_600_000, 60_000, 0.01), - raqe("b", 3_600_000, 60_000, 0.01), - ]; - let plan = plan(&raqes, &costs, 1, false, table_accuracy).unwrap(); - assert_eq!(plan.deployments.len(), 2); - assert_eq!(plan.deployments[0], plan.deployments[1]); - assert_eq!(plan.mapping, vec![0, 1]); - - let facts = facts(1, 10); - let both = score(&raqes, &plan.deployments, &plan.mapping, &facts); - let one = score(&raqes[..1], &plan.deployments[..1], &vec![0], &facts); - assert_eq!( - both.ingest, - PhaseCost { - cpu_secs_per_sec: 2.0 * one.ingest.cpu_secs_per_sec, - memory_bytes: 2.0 * one.ingest.memory_bytes, - } - ); - } - - #[test] - fn adapted_window_is_eligible() { - let costs = grid(&[2, 3], &[256, 512]); - // T divides S, and T does not divide S (gcd slide). - let raqes = vec![ - raqe("tiled", 3_600_000, 60_000, 0.01), - raqe("gcd", 3_600_000, 280_000, 0.01), - ]; - let plan = plan(&raqes, &costs, 1, false, table_accuracy).unwrap(); - assert_eq!(window_adapter(&raqes[0]), (3_600_000, 60_000)); - assert_eq!(window_adapter(&raqes[1]), (3_600_000, 40_000)); - for (r, d) in raqes.iter().zip(&plan.deployments) { - assert!( - is_eligible(r, d, &facts(1, 1), &crate::table_accuracy), - "{} not eligible", - r.id - ); - } - } - - #[test] - fn reports_unservable_raqe() { - let costs = grid(&[2, 3], &[256, 512]); - let raqes = vec![ - raqe("ok", 3_600_000, 60_000, 0.01), - raqe("too_strict", 3_600_000, 60_000, 1e-9), - ]; - assert_eq!( - plan(&raqes, &costs, 1, false, table_accuracy).unwrap_err(), - vec!["too_strict".to_string()] - ); - } - - #[test] - fn missing_measurement_is_infeasible() { - let costs = grid(&[2, 3], &[256, 512]); - let r = raqe("r", 3_600_000, 60_000, 0.01); - let found = search(&r, &costs, 1, false, |_, _| None); - assert_eq!(found.selected, None); - assert_eq!(found.probes.len(), costs.len()); - } - - fn kll(k: u64) -> AtomicCostEntry { - AtomicCostEntry { - sketch: "kll-percall".into(), - sketch_config: serde_json::json!({"algorithm": "kll-percall", "params": {"k": k}}), - mem_bytes_per_instance: (k * 8) as f64, - insert_cpu_secs: 1e-8, - merge_cpu_secs: 1e-6, - query_cpu_secs: 1e-6, - query_accuracy: BTreeMap::from([("mean_rank_err".into(), 1.0 / k as f64)]), - accuracy_metric: "mean_rank_err".into(), - measured_at: measured_at(), - } - } - - fn quantile_raqe(max_rank_err: f64) -> Raqe { - Raqe { - capability: Capability::Quantile, - accuracy_sla: max_rank_err, - ..raqe("q", 3_600_000, 60_000, 0.0) - } - } - - #[test] - fn single_axis_path_stops_at_the_feasibility_boundary() { - // Feasible iff k >= 6. One LHS seed s, and no extra cheapest seed - // because the seed is measured. A feasible seed shrinks to k = 5 and - // stops; an infeasible one grows to k = 6 and stops. - let costs: Vec<_> = (1..=10).map(kll).collect(); - let r = quantile_raqe(1.0 / 6.0); - for seed in 0..20 { - let found = search(&r, &costs, seed, false, table_accuracy); - let ks: Vec = found.probes.iter().map(|&i| i as u64 + 1).collect(); - assert_eq!(found.selected, Some(5), "seed {seed}: {ks:?}"); - let s = ks[0]; - let (lo, hi) = (s.min(5), s.max(6)); - assert_eq!(ks.len() as u64, hi - lo + 1, "seed {seed}: {ks:?}"); - assert!( - ks.iter().all(|&k| (lo..=hi).contains(&k)), - "seed {seed}: {ks:?}" - ); - } - } - - #[test] - fn chooses_the_cheaper_variant() { - // countsketch-heap isn't deployable, so this needs the study flag. - let mut costs = grid(&[2, 3], &[256, 512]); - let mut cs = cms(2, 256); - cs.sketch = "countsketch-heap-topk-fastpath-vector2d".into(); - cs.mem_bytes_per_instance = 100.0; - costs.push(cs); - let r = raqe("r", 3_600_000, 60_000, 0.01); - let found = search(&r, &costs, 3, true, table_accuracy); - assert_eq!(found.selected, Some(costs.len() - 1)); - } - - #[test] - fn higher_is_better_metric_is_a_floor() { - // Precision rises with k; the target is a floor of 0.95. - let costs: Vec<_> = (1..=10) - .map(|k| { - let mut c = cms(1, k); - c.query_accuracy = - BTreeMap::from([("precision_at_k".into(), 0.9 + 0.01 * k as f64)]); - c - }) - .collect(); - let r = Raqe { - accuracy_sla: 0.95, - ..raqe("r", 3_600_000, 60_000, 0.0) - }; - let found = search(&r, &costs, 11, false, table_accuracy); - assert_eq!(found.selected, Some(4)); // k = 5: 0.95 - } - - #[test] - fn lhs_samples_take_distinct_values_on_every_axis() { - let costs = grid(&[1, 2, 3, 4], &[64, 128, 256, 512, 1024]); - let resolved = resolve_heaps(&costs, crate::TOPK_K, true); - let g = Grid::new( - "cms-heap-topk-fastpath-vector2d", - &resolved.costs, - &resolved.usable, - ) - .unwrap(); - for seed in 0..10 { - let samples = g.lhs(seed); - assert_eq!(samples.len(), 4); - for axis in 0..2 { - let values: BTreeSet = samples.iter().map(|&i| g.key_of(i)[axis]).collect(); - assert_eq!(values.len(), samples.len(), "seed {seed} axis {axis}"); - } - } - } - - /// AutoSketch prices a top-k heap as the MILP does: the smallest heap - /// row resolved at k (floored at the smallest measured heap, extended - /// past the largest), the other heap rows masked out. - #[test] - fn heaps_resolve_at_k_like_the_milp() { - let at = |heap: u64| { - let mut row = cms(3, 256); - row.sketch_config["params"]["heap"] = heap.into(); - row.mem_bytes_per_instance = 1000.0 + heap as f64; - row - }; - let costs = [at(32), at(128)]; - for (k, heap, mem) in [(10, 32, 1032.0), (100, 100, 1100.0), (512, 512, 1512.0)] { - let Resolved { - costs: resolved, - usable, - } = resolve_heaps(&costs, k, true); - assert_eq!(usable, vec![true, false], "k = {k}"); - assert_eq!(crate::heap_capacity(&resolved[0]), Some(heap), "k = {k}"); - assert_eq!(crate::config_topk_k(&resolved[0]), k, "k = {k}"); - assert!( - (resolved[0].mem_bytes_per_instance - mem).abs() < 1e-9, - "k = {k}" - ); - } - } - - /// UnivMon answers TOPK_K only, so a top-10 search can't pick it. - #[test] - fn a_fixed_k_family_is_masked_off_its_k() { - let costs = [AtomicCostEntry { - sketch: "univmon-topk".into(), - ..cms(3, 256) - }]; - assert_eq!(resolve_heaps(&costs, crate::TOPK_K, true).usable, [true]); - assert_eq!(resolve_heaps(&costs, 10, true).usable, [false]); - // A non-top-k RAQE never asks for a k. - assert_eq!(resolve_heaps(&costs, 10, false).usable, [true]); - let ten = Raqe { - topk_k: Some(10), - ..raqe("r", 60_000, 60_000, 0.1) - }; - assert_eq!(search(&ten, &costs, 0, true, table_accuracy).selected, None); - } -} diff --git a/rqe-optimizer/src/candidates.rs b/rqe-optimizer/src/candidates.rs deleted file mode 100644 index ee8623d8..00000000 --- a/rqe-optimizer/src/candidates.rs +++ /dev/null @@ -1,1383 +0,0 @@ -//! Candidate generation and eligibility (`docs/rqe_optimizer_candidates.md`). - -use crate::analytical_cost_model; -use crate::{ - config_topk_k, has_heap, heap_capacity, heap_needed, Accuracy, AtomicCostEntry, Capability, - Deployment, LabelSet, MetricFacts, Millis, Raqe, WorkloadFacts, KEY_TRACKER_FAMILY, -}; -use std::collections::{BTreeMap, BTreeSet}; - -pub fn gcd(a: Millis, b: Millis) -> Millis { - if b == 0 { - a - } else { - gcd(b, a % b) - } -} - -fn divisors(value: Millis) -> Vec { - let mut out = Vec::new(); - let mut factor = 1; - while factor <= value / factor { - if value.is_multiple_of(factor) { - out.push(factor); - if factor != value / factor { - out.push(value / factor); - } - } - factor += 1; - } - out.sort_unstable(); - out -} - -/// The gcd of every non-empty subset of `values`, in one pass without listing -/// subsets: `gcd(A ∪ {v}) = gcd(gcd(A), v)`, so each new value extends every -/// gcd found so far. -fn subset_gcds(values: impl IntoIterator) -> BTreeSet { - let mut gcds = BTreeSet::new(); - for value in values { - let extended: Vec<_> = gcds.iter().map(|&found| gcd(found, value)).collect(); - gcds.insert(value); - gcds.extend(extended); - } - gcds -} - -/// Windows and slides are multiples of the metric's scrape interval: anything -/// finer only splits one scrape's samples. -/// -/// Deployments are grouped by `grouping` and built from the cost rows that -/// pass `row`. Their windows, slides and heaps come from -/// `group`, the RAQEs they may serve. -fn candidate_deployments( - group: &[&Raqe], - grouping: &LabelSet, - row: impl Fn(&AtomicCostEntry) -> bool, - costs: &[AtomicCostEntry], - metric_facts: &MetricFacts, - allow_undeployable_families: bool, -) -> Vec { - if group.is_empty() { - return Vec::new(); - } - let capability = group[0].capability; - let scrape_interval_ms = metric_facts.scrape_interval_ms; - // The tracker is deployable whenever the family needing it is: ASAPQuery - // runs it as DeltaSetAggregator. - let tracker_rows: Vec<_> = costs - .iter() - .filter(|c| c.sketch == KEY_TRACKER_FAMILY) - .collect(); - assert!( - tracker_rows.len() <= 1, - "{} {KEY_TRACKER_FAMILY} rows in the cost table; the tracker's price would depend on row order", - tracker_rows.len() - ); - let tracker_row = tracker_rows.first().copied(); - let family_rows: Vec<&AtomicCostEntry> = costs - .iter() - .filter(|c| { - capability - .candidate_families(allow_undeployable_families) - .any(|family| family == c.sketch) - && row(c) - }) - .collect(); - let heap_rows = heap_rows_by_shape(&family_rows); - let windows = group - .iter() - .flat_map(|r| divisors(r.lookback_ms / scrape_interval_ms)) - .map(|scrapes| scrapes * scrape_interval_ms) - .collect::>(); - let mut deployments = Vec::new(); - for window_ms in windows { - // gcd(x, T) is the largest slide that serves an RAQE. For RAQEs sharing a - // deployment, the gcd of theirs is strictly better than any finer - // slide that serves them all. Any subset might share. - let slides = subset_gcds( - group - .iter() - .filter(|r| r.lookback_ms % window_ms == 0) - .map(|r| gcd(window_ms, r.interval_ms)), - ); - // A heap family keeps one candidate per (heap, k) a RAQE on this - // window needs, m · k for its m = L / x merged windows, priced by - // interpolating the measured heap sizes. Other rows are used as they are. - let heaps: BTreeSet<(u64, u64)> = group - .iter() - .filter_map(|r| { - Some(( - heap_needed(r.lookback_ms, window_ms, r.topk_k())?, - r.topk_k(), - )) - }) - .collect(); - let configs: Vec = family_rows - .iter() - .filter(|c| !has_heap(&c.sketch)) - .map(|c| (*c).clone()) - .chain(heap_rows.values().flat_map(|rows| { - // Needs that one measured row serves collapse onto it. - let by_heap: BTreeMap<(u64, u64), AtomicCostEntry> = heaps - .iter() - .filter_map(|&(heap, k)| heap_row(rows, heap, k)) - .filter_map(|entry| { - Some(((heap_capacity(&entry)?, config_topk_k(&entry)), entry)) - }) - .collect(); - by_heap.into_values() - })) - .collect(); - for config in &configs { - let key_tracker = - if crate::family_properties(&config.sketch).needs_delta_set_key_tracker { - match tracker_row { - Some(tracker) => Some(tracker.clone()), - None => continue, - } - } else { - None - }; - for &slide_ms in &slides { - if slide_ms.is_multiple_of(scrape_interval_ms) { - deployments.push(Deployment { - capability, - metric: group[0].metric.clone(), - spatial_filter: group[0].spatial_filter.clone(), - grouping_labels: grouping.clone(), - config: config.clone(), - window_ms, - slide_ms, - key_tracker: key_tracker.clone(), - }); - } - } - } - } - deployments -} - -/// Whether `capability` asks for a top-k list. -pub(crate) fn is_topk(capability: Capability) -> bool { - matches!( - capability, - Capability::TopKByValue | Capability::TopKByCount - ) -} - -/// A heap family's rows, by family and counter shape (every param but -/// `heap`), then by heap capacity. -pub(crate) fn heap_rows_by_shape<'a>( - rows: &[&'a AtomicCostEntry], -) -> BTreeMap<(String, String), BTreeMap> { - let mut out: BTreeMap<_, BTreeMap> = BTreeMap::new(); - for &row in rows { - let Some(heap) = heap_capacity(row) else { - continue; - }; - let mut shape = row.sketch_config["params"].clone(); - if let Some(params) = shape.as_object_mut() { - params.remove("heap"); - } - let shape = shape.to_string(); - if out - .entry((row.sketch.clone(), shape.clone())) - .or_default() - .insert(heap, row) - .is_some() - { - panic!( - "two {} {shape} cost rows at heap {heap} (e.g. no heap and heap={heap}); \ - keep one", - row.sketch - ); - } - } - out -} - -/// `rows` (one family and counter shape, by heap) priced for a top-`k` -/// answer needing `heap`: at `heap`, but never below the smallest measured -/// heap (nothing is extrapolated below it). The config names its heap -/// explicitly and its `topk_k`, so the deployed sketch answers `k` with the -/// heap that was priced. The MILP's candidates and AutoSketch both price -/// heaps here. -pub(crate) fn heap_row( - rows: &BTreeMap, - heap: u64, - k: u64, -) -> Option { - let (&smallest, _) = rows.first_key_value()?; - let heap = heap.max(smallest); - let mut entry = at_heap(rows, heap)?; - // The priced heap, explicit: with no `heap`, a `topk_k` below it would - // read (and build) as a heap of `k`. - let priced = heap_capacity(&entry)?; - let params = entry.sketch_config["params"].as_object_mut()?; - params.insert("heap".into(), priced.into()); - params.remove("topk_k"); - if let Some(k) = crate::topk_k_param(k) { - params.insert("topk_k".into(), k.into()); - } - Some(entry) -} - -/// `rows` (one family and counter shape, by heap) priced at `heap`: each -/// per-op cost linear in the heap between the measured sizes around it, and -/// extended from the two nearest past them. With one measured size, the -/// smallest row holding at least `heap` serves at its own size and cost. -/// The accuracy fields are the smallest heap's: curves are measured at -/// `TOPK_K`. Panics when one measured heap is smaller than `heap`: the -/// table needs the heap sizes measured. -fn at_heap(rows: &BTreeMap, heap: u64) -> Option { - let (_, &base) = rows.first_key_value()?; - // A measured heap's costs, with the base's accuracy like every other. - let measured = |row: &AtomicCostEntry| AtomicCostEntry { - query_accuracy: base.query_accuracy.clone(), - ..row.clone() - }; - if let Some(row) = rows.get(&heap) { - return Some(measured(row)); - } - if rows.len() < 2 { - let Some((_, row)) = rows.range(heap..).next() else { - panic!( - "{} {} is priced at one heap only, smaller than the heap {heap} a \ - candidate needs; rerun study_saturation.py --phase optimizer-cost to \ - measure the top-k heap sizes", - base.sketch, base.sketch_config["params"] - ); - }; - return Some(measured(row)); - } - let mut below = rows.range(..heap).rev(); - let mut above = rows.range(heap..); - let ((h0, a), (h1, b)) = match (below.next(), above.next()) { - (Some(lo), Some(hi)) => (lo, hi), - (Some(hi), None) => (below.next()?, hi), - (None, Some(lo)) => (lo, above.next()?), - (None, None) => return None, - }; - let t = (heap as f64 - *h0 as f64) / (*h1 as f64 - *h0 as f64); - let line = |x: f64, y: f64| (x + t * (y - x)).max(0.0); - let mut entry = AtomicCostEntry { - mem_bytes_per_instance: line(a.mem_bytes_per_instance, b.mem_bytes_per_instance), - insert_cpu_secs: line(a.insert_cpu_secs, b.insert_cpu_secs), - merge_cpu_secs: line(a.merge_cpu_secs, b.merge_cpu_secs), - query_cpu_secs: line(a.query_cpu_secs, b.query_cpu_secs), - ..(*base).clone() - }; - entry.sketch_config["params"]["heap"] = heap.into(); - Some(entry) -} - -/// Configurations come from [`Capability::candidate_families`]. -pub fn build_all_candidates( - raqes: &[Raqe], - costs: &[AtomicCostEntry], - facts: &WorkloadFacts, - allow_undeployable_families: bool, - accuracy: &Accuracy, -) -> Vec { - prune_dominated_candidates( - raqes, - facts, - build_all_candidates_unpruned(raqes, costs, facts, allow_undeployable_families), - accuracy, - ) -} - -/// Generate the complete v1 candidate set before dominance pruning. -/// -/// The public optimizer entry point is [`build_all_candidates`]. This helper -/// exists so diagnostics can report exactly how much safe pruning removed. -pub fn build_all_candidates_unpruned( - raqes: &[Raqe], - costs: &[AtomicCostEntry], - facts: &WorkloadFacts, - allow_undeployable_families: bool, -) -> Vec { - let mut groups: BTreeMap<(Capability, &str, &str, &LabelSet), Vec<&Raqe>> = BTreeMap::new(); - for raqe in raqes { - groups - .entry(( - raqe.capability, - &raqe.metric, - &raqe.spatial_filter, - &raqe.grouping_labels, - )) - .or_default() - .push(raqe); - } - // RAQEs of `(capability, metric, filter)` grouped by a subset of - // `grouping`, non-empty ones only for Hydra. - let subset_group = - |(capability, metric, filter, grouping): (Capability, &str, &str, &LabelSet), - non_empty: bool| { - raqes - .iter() - .filter(|r| { - r.capability == capability - && r.metric == metric - && r.spatial_filter == filter - && r.grouping_labels.is_subset(grouping) - && !(non_empty && r.grouping_labels.is_empty()) - }) - .collect::>() - }; - let mut candidates: Vec = groups - .iter() - .flat_map(|(&key, exact_group)| { - let (_, metric, _, grouping) = key; - // A family that rolls up may also serve RAQEs grouped by a subset, - // so its windows must suit theirs too. - let subset_group = subset_group(key, false); - let properties = |c: &AtomicCostEntry| crate::family_properties(&c.sketch); - [ - candidate_deployments( - exact_group, - grouping, - |c| { - !properties(c).answers_any_subgrouping - && !properties(c).mergeable_across_groups - }, - costs, - &facts[metric], - allow_undeployable_families, - ), - candidate_deployments( - &subset_group, - grouping, - |c| { - !properties(c).answers_any_subgrouping - && properties(c).mergeable_across_groups - }, - costs, - &facts[metric], - allow_undeployable_families, - ), - ] - .concat() - }) - .collect(); - // Hydra, on a metric with a measured dataset: one grid over its full - // schema `Λ`, the width the study measures cost and accuracy at, from - // the rows measured on that dataset. `Λ` is a label set, not an ordered - // list: the library encodes subkeys in schema order, and every metric - // keeps one order (the eval datasets fix it), so the set names the grid. - let streams: BTreeSet<(Capability, &str, &str)> = groups - .keys() - .map(|&(capability, metric, filter, _)| (capability, metric, filter)) - .collect(); - for (capability, metric, filter) in streams { - let Some((dataset, schema)) = &facts[metric].hydra_dataset else { - continue; - }; - candidates.extend(candidate_deployments( - &subset_group((capability, metric, filter, schema), true), - schema, - |c| { - crate::family_properties(&c.sketch).answers_any_subgrouping - && c.measured_at.dataset.as_ref() == Some(dataset) - }, - costs, - &facts[metric], - allow_undeployable_families, - )); - } - candidates -} - -/// Remove a candidate only when another candidate can replace it in every -/// mapping without making any modeled objective worse. -/// -/// This comparison is local to a (capability, metric, spatial_filter, -/// grouping) group. It compares ingest CPU and memory; compaction per window -/// (it starts every chain) and per second (CPU-seconds and byte-seconds per -/// slide, as cost by use bills it); then, for each RAQE the dominated -/// candidate can serve, the query job's CPU (merge and query, its latency), -/// its memory (merge accumulators and output) and stored memory. All are as -/// the analytical cost model prices them: a sketch shared by all groups and a -/// sketch per group don't scale alike with `card(G)`. -pub fn prune_dominated_candidates( - raqes: &[Raqe], - facts: &WorkloadFacts, - candidates: Vec, - accuracy: &Accuracy, -) -> Vec { - let costs: Vec = candidates - .iter() - .map(|candidate| CandidateCosts::new(candidate, raqes, facts, accuracy)) - .collect(); - - candidates - .iter() - .enumerate() - .filter_map(|(candidate_index, candidate)| { - let dominated = (0..candidates.len()).any(|other_index| { - if other_index == candidate_index { - return false; - } - match dominance( - &candidates[other_index], - &costs[other_index], - candidate, - &costs[candidate_index], - ) { - Dominance::StrictlyBetter => true, - // The earlier of two identical candidates wins, so a tie - // never removes both. - Dominance::Equal => other_index < candidate_index, - Dominance::NotDominating => false, - } - }); - (!dominated).then(|| candidate.clone()) - }) - .collect() -} - -/// One candidate's costs, computed once for every pairwise comparison. -struct CandidateCosts { - /// Per deployment: ingest CPU and memory; compaction CPU-seconds per - /// window (it starts every chain); and compaction CPU and byte-seconds - /// per second, as cost by use bills them every slide. - ingest: [f64; 5], - /// Per RAQE: latency, query memory (merge and output) and stored memory - /// if the candidate serves it, else `None`. - per_raqe: Vec>, -} - -impl CandidateCosts { - fn new( - candidate: &Deployment, - raqes: &[Raqe], - facts: &WorkloadFacts, - accuracy: &Accuracy, - ) -> Self { - let ingest = analytical_cost_model::ingest(candidate, facts); - let compaction_secs = analytical_cost_model::compaction_secs(candidate, facts); - let slide_secs = crate::secs(candidate.slide_ms); - let per_raqe = raqes - .iter() - .map(|raqe| { - is_eligible(raqe, candidate, facts, accuracy).then(|| { - [ - analytical_cost_model::query_latency_ms(raqe, candidate, facts), - analytical_cost_model::merge(raqe, candidate, facts).memory_bytes - + analytical_cost_model::query(raqe, candidate, facts).memory_bytes, - analytical_cost_model::storage_bytes(raqe, candidate, facts), - ] - }) - }) - .collect(); - Self { - ingest: [ - ingest.cpu_secs_per_sec, - ingest.memory_bytes, - compaction_secs, - compaction_secs / slide_secs, - analytical_cost_model::compaction_bytes(candidate, facts) * compaction_secs - / slide_secs, - ], - per_raqe, - } - } -} - -enum Dominance { - StrictlyBetter, - Equal, - NotDominating, -} - -/// Whether `replacement` can stand in for `original`: it serves every RAQE -/// `original` serves and is no worse on any cost. -fn dominance( - replacement: &Deployment, - replacement_costs: &CandidateCosts, - original: &Deployment, - original_costs: &CandidateCosts, -) -> Dominance { - let same_group = replacement.capability == original.capability - && replacement.metric == original.metric - && replacement.spatial_filter == original.spatial_filter - && replacement.grouping_labels == original.grouping_labels; - let covers_original = original_costs - .per_raqe - .iter() - .zip(&replacement_costs.per_raqe) - .all(|(original_serves, replacement_serves)| { - original_serves.is_none() || replacement_serves.is_some() - }); - if !same_group || !covers_original { - return Dominance::NotDominating; - } - - // Each pair is (replacement's cost, original's cost), over the RAQEs - // `original` serves. - let mut costs: Vec<(f64, f64)> = replacement_costs - .ingest - .into_iter() - .zip(original_costs.ingest) - .collect(); - for (replacement_raqe, original_raqe) in replacement_costs - .per_raqe - .iter() - .zip(&original_costs.per_raqe) - { - if let (Some(replacement_raqe), Some(original_raqe)) = (replacement_raqe, original_raqe) { - costs.extend( - replacement_raqe - .iter() - .copied() - .zip(original_raqe.iter().copied()), - ); - } - } - - let serves_more = original_costs - .per_raqe - .iter() - .zip(&replacement_costs.per_raqe) - .any(|(original_serves, replacement_serves)| { - original_serves.is_none() && replacement_serves.is_some() - }); - if costs - .iter() - .any(|(replacement_cost, original_cost)| replacement_cost > original_cost) - { - Dominance::NotDominating - } else if serves_more - || costs - .iter() - .any(|(replacement_cost, original_cost)| replacement_cost < original_cost) - { - Dominance::StrictlyBetter - } else { - Dominance::Equal - } -} - -pub fn eligible_deployments_for( - r: &Raqe, - deployments: &[Deployment], - facts: &WorkloadFacts, - accuracy: &Accuracy, -) -> Vec { - deployments - .iter() - .enumerate() - .filter(|(_, d)| is_eligible(r, d, facts, accuracy)) - .map(|(i, _)| i) - .collect() -} - -/// Whether `d` can serve `r`. Every rule lives here, so the MILP, enumeration -/// and candidate pruning agree on what is valid. -pub fn is_eligible(r: &Raqe, d: &Deployment, _facts: &WorkloadFacts, accuracy: &Accuracy) -> bool { - let properties = d.properties(); - r.capability == d.capability - && r.metric == d.metric - && r.spatial_filter == d.spatial_filter - && (if properties.answers_any_subgrouping { - // Hydra: any non-empty subset of its schema, read directly. - !r.grouping_labels.is_empty() && r.grouping_labels.is_subset(&d.grouping_labels) - } else { - r.grouping_labels == d.grouping_labels - || (properties.mergeable_across_groups - && r.grouping_labels.is_subset(&d.grouping_labels)) - }) - && d.window_ms != 0 - && d.slide_ms != 0 - && d.window_ms.is_multiple_of(d.slide_ms) - && r.lookback_ms.is_multiple_of(d.window_ms) - && r.interval_ms.is_multiple_of(d.slide_ms) - && (properties.mergeable_across_windows || r.lookback_ms == d.window_ms) - && properties.needs_delta_set_key_tracker == d.key_tracker.is_some() - && heap_capacity(&d.config).is_none_or(|heap| { - d.heap_needed(r.lookback_ms, r.topk_k()) - .is_some_and(|need| heap >= need) - }) - && (!is_topk(r.capability) || crate::serves_topk_k(&d.config, r.topk_k())) - && r.meets_sla(&d.config.sketch, accuracy(r, d)) -} - -#[cfg(test)] -mod tests { - use super::*; - use crate::test_support::{facts, measured_at, metric_of, perfect_accuracy, METRIC}; - use crate::{table_accuracy, LabelSet, TOPK_K}; - use std::collections::BTreeMap; - fn raqe(id: &str, lookback: Millis, interval: Millis) -> Raqe { - Raqe { - id: id.into(), - capability: Capability::TopKByValue, - lookback_ms: lookback, - interval_ms: interval, - metric: METRIC.into(), - spatial_filter: String::new(), - grouping_labels: LabelSet::new(), - accuracy_sla: 0.5, - latency_sla_ms: None, - topk_k: None, - accuracy_covers_share: None, - } - } - fn cost() -> AtomicCostEntry { - AtomicCostEntry { - sketch: "cms-heap-topk-fastpath-vector2d".into(), - // A heap that holds any merge, so heap top-k fixtures merge freely. - sketch_config: serde_json::json!({"params": {"heap": 1u64 << 40}}), - mem_bytes_per_instance: 1.0, - insert_cpu_secs: 1.0, - merge_cpu_secs: 1.0, - query_cpu_secs: 1.0, - query_accuracy: perfect_accuracy(), - accuracy_metric: "precision_at_k".into(), - measured_at: measured_at(), - } - } - /// A heap top-k row measured at `heap`, its costs `heap` per op. - fn heap_row(heap: u64) -> AtomicCostEntry { - let h = heap as f64; - AtomicCostEntry { - sketch_config: serde_json::json!({"params": {"rows": 3, "cols": 256, "heap": heap}}), - mem_bytes_per_instance: 1000.0 + h, - insert_cpu_secs: h, - merge_cpu_secs: 2.0 * h, - query_cpu_secs: 3.0 * h, - ..cost() - } - } - - #[test] - fn heap_costs_are_linear_between_and_past_the_measured_sizes() { - let (a, b, c) = (heap_row(32), heap_row(128), heap_row(512)); - let rows = BTreeMap::from([(32, &a), (128, &b), (512, &c)]); - let at = |heap| at_heap(&rows, heap).unwrap(); - assert_eq!(at(128), b); - let between = at(64); - assert_eq!(between.mem_bytes_per_instance, 1064.0); - assert_eq!(between.merge_cpu_secs, 128.0); - assert_eq!(heap_capacity(&between), Some(64)); - // Past the largest: the line through the two largest. - assert_eq!(at(2048).query_cpu_secs, 3.0 * 2048.0); - // One measured size: the smallest row holding the heap serves as is. - let single = BTreeMap::from([(128, &b)]); - assert_eq!(at_heap(&single, 64), Some(b.clone())); - } - - /// A table priced at one heap can't price a larger one: planning stops - /// and names the rerun, rather than dropping the candidate. - #[test] - #[should_panic(expected = "rerun study_saturation.py --phase optimizer-cost")] - fn one_measured_heap_cannot_price_a_larger_one() { - let b = heap_row(128); - at_heap(&BTreeMap::from([(128, &b)]), 256); - } - - /// No heap and heap = k are one heap: two such rows would overwrite each - /// other. - #[test] - #[should_panic(expected = "cost rows at heap 32")] - fn two_rows_at_one_heap_are_refused() { - let explicit = heap_row(32); - let mut implicit = heap_row(32); - implicit.sketch_config["params"] - .as_object_mut() - .unwrap() - .remove("heap"); - heap_rows_by_shape(&[&explicit, &implicit]); - } - - /// Each window gets the heaps its RAQEs need, m · k for m = L / x, and a - /// RAQE is eligible only where the heap holds its m · k. - #[test] - fn a_heap_candidate_holds_m_times_k_for_its_merges() { - let raqes = vec![raqe("a", 60_000, 60_000), raqe("b", 240_000, 60_000)]; - let rows = [heap_row(32), heap_row(128), heap_row(512)]; - let candidates = build_all_candidates_unpruned(&raqes, &rows, &facts(1, 1), false); - let heaps_at = |window_ms: Millis| { - candidates - .iter() - .filter(|d| d.window_ms == window_ms) - .filter_map(|d| heap_capacity(&d.config)) - .collect::>() - }; - // x = 60 s: a merges 1 (32), b merges 4 (128). - assert_eq!(heaps_at(60_000), BTreeSet::from([32, 128])); - // x = 30 s: a merges 2 (64, interpolated), b merges 8 (256). - assert_eq!(heaps_at(30_000), BTreeSet::from([64, 256])); - let d = candidates - .iter() - .find(|d| d.window_ms == 60_000 && heap_capacity(&d.config) == Some(32)) - .unwrap(); - assert!(is_eligible(&raqes[0], d, &facts(1, 1), &table_accuracy)); - assert!(!is_eligible(&raqes[1], d, &facts(1, 1), &table_accuracy)); - } - - /// A top-10 RAQE needs m · 10: its heaps follow its own k, and a heap - /// built for k = 10 doesn't serve a k = 32 RAQE at the same merges. - #[test] - fn a_heap_candidate_follows_each_raqes_k() { - let ten = Raqe { - topk_k: Some(10), - ..raqe("ten", 240_000, 60_000) - }; - let raqes = vec![ten.clone(), raqe("b", 240_000, 60_000)]; - let rows = [heap_row(32), heap_row(128), heap_row(512)]; - let candidates = build_all_candidates_unpruned(&raqes, &rows, &facts(1, 1), false); - let at = |heap| { - candidates - .iter() - .find(|d| d.window_ms == 60_000 && heap_capacity(&d.config) == Some(heap)) - .unwrap() - }; - // x = 60 s merges 4: k = 10 needs 40, k = 32 needs 128. - assert_eq!(config_topk_k(&at(40).config), 10); - assert_eq!(config_topk_k(&at(128).config), TOPK_K); - assert!(is_eligible(&ten, at(40), &facts(1, 1), &table_accuracy)); - assert!(!is_eligible( - &raqes[1], - at(40), - &facts(1, 1), - &table_accuracy - )); - assert!(is_eligible( - &raqes[1], - at(128), - &facts(1, 1), - &table_accuracy - )); - } - - /// A k below the smallest measured heap prices at that heap, never - /// extrapolated below it, and the config carries its `topk_k`. - #[test] - fn a_small_k_is_floored_at_the_smallest_measured_heap() { - let rows = [heap_row(32), heap_row(128)]; - let refs: Vec<&AtomicCostEntry> = rows.iter().collect(); - let by_shape = heap_rows_by_shape(&refs); - let shape = by_shape.values().next().unwrap(); - let entry = super::heap_row(shape, 10, 10).unwrap(); - assert_eq!(heap_capacity(&entry), Some(32)); - assert_eq!(config_topk_k(&entry), 10); - assert_eq!( - entry.mem_bytes_per_instance, - heap_row(32).mem_bytes_per_instance - ); - } - - /// A floor row measured with its heap implicit at k = 32 keeps that heap - /// explicit once it serves a smaller k, so its capacity is the priced 32. - #[test] - fn the_floored_heap_is_explicit() { - let mut implicit = heap_row(32); - implicit.sketch_config = serde_json::json!({"params": {"rows": 3, "cols": 256}}); - let larger = heap_row(128); - let rows = BTreeMap::from([(32, &implicit), (128, &larger)]); - let entry = super::heap_row(&rows, 10, 10).unwrap(); - assert_eq!(entry.sketch_config["params"]["heap"], 32); - assert_eq!(heap_capacity(&entry), Some(32)); - assert_eq!(config_topk_k(&entry), 10); - } - - /// UnivMon's top-k answers TOPK_K only: a top-10 RAQE can't use it. - #[test] - fn univmon_topk_serves_only_topk_k() { - let univmon = Deployment { - config: AtomicCostEntry { - sketch: "univmon-topk".into(), - ..cost() - }, - capability: Capability::TopKByValue, - metric: METRIC.into(), - spatial_filter: String::new(), - grouping_labels: LabelSet::new(), - window_ms: 60_000, - slide_ms: 60_000, - key_tracker: None, - }; - let r = raqe("r", 60_000, 60_000); - let ten = Raqe { - topk_k: Some(10), - ..r.clone() - }; - assert!(is_eligible(&r, &univmon, &facts(1, 1), &table_accuracy)); - assert!(!is_eligible(&ten, &univmon, &facts(1, 1), &table_accuracy)); - } - - #[test] - fn shared_slide_comes_from_subset_gcd_not_all_divisors() { - let raqes = vec![raqe("a", 60_000, 20_000), raqe("b", 60_000, 30_000)]; - let candidates = - build_all_candidates(&raqes, &[cost()], &facts(1, 1), false, &table_accuracy); - let slides: BTreeSet<_> = candidates - .iter() - .filter(|d| d.window_ms == 60_000) - .map(|d| d.slide_ms) - .collect(); - assert_eq!(slides, BTreeSet::from([10_000, 20_000, 30_000])); - } - #[test] - fn windows_and_slides_are_multiples_of_the_scrape_interval() { - let raqes = vec![raqe("a", 60_000, 20_000), raqe("b", 60_000, 30_000)]; - let mut facts = facts(1, 1); - facts.get_mut(METRIC).unwrap().scrape_interval_ms = 15_000; - let candidates = build_all_candidates_unpruned(&raqes, &[cost()], &facts, false); - assert!(!candidates.is_empty()); - // Without the filter, windows 1..60 and slides 10/20 would appear. - assert!(candidates - .iter() - .all(|d| d.window_ms % 15_000 == 0 && d.slide_ms % 15_000 == 0)); - } - #[test] - fn different_spatial_filters_never_share_a_deployment() { - let unfiltered = raqe("a", 60_000, 60_000); - let filtered = Raqe { - spatial_filter: r#"{job="api"}"#.into(), - ..raqe("b", 60_000, 60_000) - }; - let candidates = build_all_candidates( - &[unfiltered.clone(), filtered.clone()], - &[cost()], - &facts(1, 1), - false, - &table_accuracy, - ); - assert!(candidates.iter().all(|d| is_eligible( - &unfiltered, - d, - &facts(1, 1), - &table_accuracy - ) != is_eligible( - &filtered, - d, - &facts(1, 1), - &table_accuracy - ))); - } - - #[test] - fn a_min_query_is_only_offered_the_min_accumulator() { - let r = Raqe { - capability: Capability::Min, - ..raqe("r", 60_000, 60_000) - }; - let named = |sketch: &str| AtomicCostEntry { - sketch: sketch.into(), - accuracy_metric: metric_of(sketch), - ..cost() - }; - let candidates = build_all_candidates( - &[r], - &[named("exact-min"), named("exact-max")], - &facts(1, 1), - false, - &table_accuracy, - ); - assert!(!candidates.is_empty()); - assert!(candidates.iter().all(|d| d.config.sketch == "exact-min")); - } - - #[test] - fn undeployable_families_are_candidates_only_when_allowed() { - let r = raqe("r", 60_000, 60_000); - let named = |sketch: &str| AtomicCostEntry { - sketch: sketch.into(), - accuracy_metric: metric_of(sketch), - ..cost() - }; - let costs = [ - named("cms-heap-topk-fastpath-vector2d"), - named("countsketch-heap-topk-fastpath-vector2d"), - ]; - let candidates = build_all_candidates( - std::slice::from_ref(&r), - &costs, - &facts(1, 1), - false, - &table_accuracy, - ); - assert!(!candidates.is_empty()); - assert!(candidates - .iter() - .all(|d| d.config.sketch == "cms-heap-topk-fastpath-vector2d")); - let all = build_all_candidates_unpruned(&[r], &costs, &facts(1, 1), true); - assert!(all - .iter() - .any(|d| d.config.sketch == "countsketch-heap-topk-fastpath-vector2d")); - } - - #[test] - fn eligibility_requires_exact_non_overlapping_tiling() { - let r = raqe("r", 600_000, 180_000); - let d = Deployment { - capability: Capability::TopKByValue, - metric: METRIC.into(), - spatial_filter: String::new(), - grouping_labels: LabelSet::new(), - config: cost(), - window_ms: 120_000, - slide_ms: 60_000, - key_tracker: None, - }; - assert!(is_eligible(&r, &d, &facts(1, 1), &table_accuracy)); - assert!(!is_eligible( - &r, - &Deployment { - window_ms: 128_000, - ..d.clone() - }, - &facts(1, 1), - &table_accuracy - )); - assert!(!is_eligible( - &r, - &Deployment { - slide_ms: 70_000, - ..d - }, - &facts(1, 1), - &table_accuracy - )); - } - - #[test] - fn prunes_finer_slide_when_the_coarser_slide_serves_the_same_raqe() { - let r = raqe("r", 60_000, 60_000); - let coarse = Deployment { - capability: Capability::TopKByValue, - metric: METRIC.into(), - spatial_filter: String::new(), - grouping_labels: LabelSet::new(), - config: cost(), - window_ms: 60_000, - slide_ms: 60_000, - key_tracker: None, - }; - let fine = Deployment { - slide_ms: 30_000, - ..coarse.clone() - }; - - let retained = prune_dominated_candidates( - &[r], - &facts(1, 1), - vec![fine, coarse.clone()], - &table_accuracy, - ); - - assert_eq!(retained, vec![coarse]); - } - - #[test] - fn prunes_the_candidate_with_larger_query_output() { - // Two heaps that differ only in the `k` they answer: the larger one - // outputs 100 entries per group, not 32, at the same CPU. - let r = raqe("r", 60_000, 60_000); - let answering = |k: u64| { - let mut d = crate::test_support::deployment(1.0, 1.0, 1.0, 0.0, 60_000, 60_000); - d.config.sketch_config = - serde_json::json!({"params": {"heap": 1u64 << 40, "topk_k": k}}); - d - }; - let retained = prune_dominated_candidates( - &[r], - &facts(1, 1), - vec![answering(100), answering(32)], - &table_accuracy, - ); - assert_eq!(retained, vec![answering(32)]); - } - - #[test] - fn retains_candidate_with_cheaper_compaction() { - // A direct RAQE never merges at query time, so merge CPU shows up only - // in compaction. `cheap_ingest` ingests on 2 workers (ρ = 2) and - // compacts for 100 s; `cheap_compaction` ingests on 3 (ρ = 3) and - // compacts for 2 s. Neither is no worse on every cost. - let r = raqe("r", 60_000, 60_000); - let cheap_ingest = crate::test_support::deployment(1.0, 1.0, 100.0, 1.0, 60_000, 60_000); - let cheap_compaction = crate::test_support::deployment(1.0, 1.5, 1.0, 1.0, 60_000, 60_000); - - let retained = prune_dominated_candidates( - &[r], - &facts(1, 2), - vec![cheap_ingest.clone(), cheap_compaction.clone()], - &table_accuracy, - ); - - assert_eq!(retained, vec![cheap_ingest, cheap_compaction]); - } - - #[test] - fn retains_candidate_with_lower_query_latency() { - let r = raqe("r", 60_000, 60_000); - let cost = cost(); - let large_window = Deployment { - capability: Capability::TopKByValue, - metric: METRIC.into(), - spatial_filter: String::new(), - grouping_labels: LabelSet::new(), - config: cost.clone(), - window_ms: 60_000, - slide_ms: 60_000, - key_tracker: None, - }; - let small_window = Deployment { - window_ms: 30_000, - slide_ms: 30_000, - ..large_window.clone() - }; - - let retained = prune_dominated_candidates( - &[r], - &facts(1, 1), - vec![small_window.clone(), large_window.clone()], - &table_accuracy, - ); - - assert_eq!(retained, vec![large_window]); - } - - #[test] - fn retains_candidate_with_lower_stored_memory() { - let r = raqe("r", 60_000, 60_000); - // Stores (60 − 60) / 60 + 1 = 1 closed instance of 10 bytes. - let whole_window = Deployment { - capability: Capability::TopKByValue, - metric: METRIC.into(), - spatial_filter: String::new(), - grouping_labels: LabelSet::new(), - config: AtomicCostEntry { - mem_bytes_per_instance: 10.0, - ..cost() - }, - window_ms: 60_000, - slide_ms: 60_000, - key_tracker: None, - }; - // Smaller, faster sketches, but (60 − 20) / 20 + 1 = 3 of them: 18 bytes. - let panes = Deployment { - config: AtomicCostEntry { - mem_bytes_per_instance: 6.0, - query_cpu_secs: 0.5, - merge_cpu_secs: 0.1, - ..cost() - }, - window_ms: 20_000, - slide_ms: 20_000, - ..whole_window.clone() - }; - - let retained = prune_dominated_candidates( - &[r], - &facts(1, 1), - vec![whole_window.clone(), panes.clone()], - &table_accuracy, - ); - - assert_eq!(retained, vec![whole_window, panes]); - } - - #[test] - fn retains_direct_query_candidate_that_never_merges() { - let r = raqe("r", 60_000, 60_000); - // x == L: no merge, so no merge memory. - let direct = Deployment { - capability: Capability::TopKByValue, - metric: METRIC.into(), - spatial_filter: String::new(), - grouping_labels: LabelSet::new(), - config: AtomicCostEntry { - mem_bytes_per_instance: 10.0, - ..cost() - }, - window_ms: 60_000, - slide_ms: 60_000, - key_tracker: None, - }; - // Smaller and cheaper on every other cost, but merges two windows, - // holding a 4-byte accumulator per group. - let halves = Deployment { - config: AtomicCostEntry { - mem_bytes_per_instance: 4.0, - query_cpu_secs: 0.5, - merge_cpu_secs: 0.1, - ..cost() - }, - window_ms: 30_000, - slide_ms: 30_000, - ..direct.clone() - }; - - let retained = prune_dominated_candidates( - &[r], - &facts(1, 1), - vec![direct.clone(), halves.clone()], - &table_accuracy, - ); - - assert_eq!(retained, vec![direct, halves]); - } - /// A whole-grid hydra-kll row measured on `hydra_test`, and a per-key - /// DeltaSet row. - fn hydra_and_tracker() -> [AtomicCostEntry; 2] { - [ - AtomicCostEntry { - sketch: "hydra-kll".into(), - mem_bytes_per_instance: 1000.0, - query_accuracy: BTreeMap::from([("mean_rank_err".into(), 0.1)]), - accuracy_metric: metric_of("hydra-kll"), - measured_at: aqpbm_core::MeasuredAt { - dataset: Some("hydra_test".into()), - ..crate::test_support::measured_at() - }, - ..cost() - }, - AtomicCostEntry { - sketch: KEY_TRACKER_FAMILY.into(), - ..cost() - }, - ] - } - - fn quantile_raqe() -> Raqe { - Raqe { - capability: Capability::Quantile, - ..raqe("r", 60_000, 60_000) - } - } - - #[test] - #[should_panic(expected = "exact-delta-set rows in the cost table")] - fn two_tracker_rows_are_refused() { - let [hydra, tracker] = hydra_and_tracker(); - build_all_candidates( - &[quantile_raqe()], - &[hydra, tracker.clone(), tracker], - &facts(10, 10), - true, - &table_accuracy, - ); - } - - #[test] - fn a_mergeable_deployment_serves_a_coarser_grouping_only() { - use crate::test_support::{kll_by, quantile_by, service_endpoint_facts}; - let facts = service_endpoint_facts(); - let serves = |d: &Deployment, grouping: &[&str]| { - is_eligible( - &quantile_by(grouping, 60_000, 60_000), - d, - &facts, - &table_accuracy, - ) - }; - let by_endpoint = kll_by(&["service", "endpoint"], 60_000); - assert!(serves(&by_endpoint, &["service", "endpoint"])); - assert!(serves(&by_endpoint, &["service"])); - assert!(serves(&by_endpoint, &[])); - assert!(!serves(&by_endpoint, &["pod"])); - assert!(!serves( - &kll_by(&["service"], 60_000), - &["service", "endpoint"] - )); - } - - #[test] - fn a_rate_serves_only_its_own_grouping() { - use crate::test_support::{kll_by, quantile_by, service_endpoint_facts}; - let facts = service_endpoint_facts(); - let by_endpoint = kll_by(&["service", "endpoint"], 60_000); - let increase_by_endpoint = Deployment { - capability: Capability::RateOrIncrease, - config: AtomicCostEntry { - sketch: "exact-increase".into(), - accuracy_metric: metric_of("exact-increase"), - ..by_endpoint.config.clone() - }, - ..by_endpoint - }; - let rate_by = |grouping: &[&str]| Raqe { - capability: Capability::RateOrIncrease, - ..quantile_by(grouping, 60_000, 60_000) - }; - let serves = |r: &Raqe| is_eligible(r, &increase_by_endpoint, &facts, &table_accuracy); - assert!(serves(&rate_by(&["service", "endpoint"]))); - assert!(!serves(&rate_by(&["service"]))); - } - - #[test] - fn top_k_serves_only_its_own_grouping() { - use crate::test_support::{deployment, label_set, service_endpoint_facts}; - let facts = service_endpoint_facts(); - let by_endpoint = Deployment { - grouping_labels: label_set(&["service", "endpoint"]), - ..deployment(1.0, 1.0, 1.0, 1.0, 60_000, 60_000) - }; - let serves = |grouping: &[&str]| { - let r = Raqe { - grouping_labels: label_set(grouping), - ..raqe("r", 60_000, 60_000) - }; - is_eligible(&r, &by_endpoint, &facts, &table_accuracy) - }; - assert!(serves(&["service", "endpoint"])); - assert!(!serves(&["service"])); - } - - /// A 1 h fine RAQE alone never yields a 90 min window. Its 90 min coarse - /// sibling does, but only for the family that can serve both. - #[test] - fn fine_candidates_take_windows_from_coarser_raqes() { - use crate::test_support::{label_set, quantile_by, service_endpoint_facts}; - const HOUR: Millis = 3_600_000; - const NINETY_MINUTES: Millis = 5_400_000; - let fine = ["service", "endpoint"]; - let topk_by = |grouping: &[&str], lookback_ms| Raqe { - grouping_labels: label_set(grouping), - ..raqe("r", lookback_ms, lookback_ms) - }; - let raqes = [ - quantile_by(&fine, HOUR, HOUR), - quantile_by(&["service"], NINETY_MINUTES, NINETY_MINUTES), - topk_by(&fine, HOUR), - topk_by(&["service"], NINETY_MINUTES), - ]; - let kll = AtomicCostEntry { - sketch: "kll-percall".into(), - sketch_config: serde_json::json!({"params": {"k": 200}}), - accuracy_metric: metric_of("kll-percall"), - ..cost() - }; - let candidates = - build_all_candidates_unpruned(&raqes, &[kll, cost()], &service_endpoint_facts(), false); - let fine_windows = |sketch: &str| { - candidates - .iter() - .filter(|d| d.config.sketch == sketch && d.grouping_labels == label_set(&fine)) - .map(|d| d.window_ms) - .collect::>() - }; - assert!(fine_windows("kll-percall").contains(&NINETY_MINUTES)); - let topk_windows = fine_windows("cms-heap-topk-fastpath-vector2d"); - assert!(topk_windows.contains(&HOUR)); - assert!(!topk_windows.contains(&NINETY_MINUTES)); - } - - /// Schema region (2) × service (5) × endpoint (10 per service), measured - /// as the Hydra dataset `hydra_test`, plus a per-series label `x`; every - /// RAQE grouping on it has a cardinality. - fn region_service_endpoint_facts() -> WorkloadFacts { - use crate::test_support::label_set; - let schema = label_set(&["region", "service", "endpoint"]); - WorkloadFacts::from([( - METRIC.to_string(), - MetricFacts { - labels: label_set(&["region", "service", "endpoint", "x"]), - scrape_interval_ms: 1_000, - cardinality: BTreeMap::from([ - (label_set(&["region"]), 2), - (label_set(&["service"]), 5), - (label_set(&["region", "service"]), 10), - (label_set(&["service", "endpoint"]), 50), - (schema.clone(), 100), - (label_set(&["region", "service", "endpoint", "x"]), 1_000), - ]), - value_range: None, - data_shape: BTreeMap::new(), - hydra_dataset: Some(("hydra_test".into(), schema)), - }, - )]) - } - - fn quantile_by(grouping: &[&str]) -> Raqe { - crate::test_support::quantile_by(grouping, 60_000, 60_000) - } - - /// A Hydra grid over its schema answers every non-empty subset of it, - /// coarse or not a prefix, directly; nothing outside it, nor the - /// ungrouped query. A per-group sketch serves only subsets of its own - /// grouping that roll up. - #[test] - fn hydra_serves_any_non_empty_subset_of_its_schema() { - use crate::test_support::{kll_by, label_set}; - let facts = region_service_endpoint_facts(); - let [hydra, tracker] = hydra_and_tracker(); - let grid = Deployment { - grouping_labels: label_set(&["region", "service"]), - config: hydra, - key_tracker: Some(tracker), - ..kll_by(&[], 60_000) - }; - let serves = |grouping: &[&str]| { - is_eligible(&quantile_by(grouping), &grid, &facts, &|_, _| Some(0.0)) - }; - assert!(serves(&["region", "service"])); - assert!(serves(&["service"])); - assert!(serves(&["region"])); - assert!(!serves(&[])); - assert!(!serves(&["service", "endpoint"])); - assert!(!serves(&["region", "service", "endpoint"])); - // Its own measured accuracy still decides. - let r = quantile_by(&["service"]); - assert!(!is_eligible(&r, &grid, &facts, &|_, _| Some(0.9))); - } - - /// One Hydra grid per stream, over its full schema (without `x`), from - /// the rows measured on its dataset only, with the windows of the RAQEs - /// it can serve; none on a metric without a dataset. - #[test] - fn hydra_grids_are_built_at_the_full_schema_from_its_dataset_rows() { - use crate::test_support::label_set; - let mut facts = region_service_endpoint_facts(); - // Minute scrapes: windows of whole minutes only. - facts.get_mut(METRIC).unwrap().scrape_interval_ms = 60_000; - let raqes = [ - quantile_by(&["region"]), - crate::test_support::quantile_by(&["service", "endpoint"], 120_000, 120_000), - ]; - let [hydra, tracker] = hydra_and_tracker(); - let elsewhere = |dataset: Option<&str>| AtomicCostEntry { - mem_bytes_per_instance: 2000.0, - measured_at: aqpbm_core::MeasuredAt { - dataset: dataset.map(str::to_string), - ..crate::test_support::measured_at() - }, - ..hydra.clone() - }; - let costs = [ - hydra.clone(), - elsewhere(Some("hydra_other")), - elsewhere(None), - tracker, - ]; - let grids = |facts: &WorkloadFacts| { - build_all_candidates_unpruned(&raqes, &costs, facts, true) - .into_iter() - .filter(|d| d.config.sketch == "hydra-kll") - .map(|d| { - ( - d.grouping_labels, - d.window_ms, - d.config.mem_bytes_per_instance as u64, - ) - }) - .collect::>() - }; - let schema = label_set(&["region", "service", "endpoint"]); - assert_eq!( - grids(&facts), - BTreeSet::from([ - (schema.clone(), 60_000, 1000), - (schema.clone(), 120_000, 1000), - ]) - ); - // No Hydra dataset: no grid at all, and not as a plain candidate. - facts.get_mut(METRIC).unwrap().hydra_dataset = None; - assert!(grids(&facts).is_empty()); - } -} diff --git a/rqe-optimizer/src/enumerate.rs b/rqe-optimizer/src/enumerate.rs deleted file mode 100644 index 83cd3d64..00000000 --- a/rqe-optimizer/src/enumerate.rs +++ /dev/null @@ -1,232 +0,0 @@ -//! Brute-force search over full mappings. -//! -//! A mapping is one deployment choice per RAQE (the MILP's only constraint is -//! `Σ_D z_{i,D} = 1`; `u_D` is derived). Choices are independent, so the -//! search space is exactly the cartesian product of the per-RAQE eligible -//! lists -- nothing to prune against. Sharing falls out when two choices land -//! on the same index, including a fine deployment chosen by a RAQE that rolls -//! up to a coarser grouping. -//! -//! An ILP would replace this module alone: `Mapping` already encodes -//! `z_{i,D}` and `u_D`. - -use crate::{ - candidates::eligible_deployments_for, Accuracy, Deployment, Mapping, Raqe, WorkloadFacts, -}; - -/// RAQEs nothing in `deployments` can serve. Surfaced separately from -/// `brute_force`: an unservable RAQE is a modeling gap (tolerance too tight, -/// no such config), not a normal empty result. -pub fn unservable( - raqes: &[Raqe], - deployments: &[Deployment], - facts: &WorkloadFacts, - accuracy: &Accuracy, -) -> Vec { - raqes - .iter() - .filter(|r| eligible_deployments_for(r, deployments, facts, accuracy).is_empty()) - .map(|r| r.id.clone()) - .collect() -} - -/// Every feasible full mapping. Empty if any RAQE is unservable -- check -/// [`unservable`] to tell the two cases apart. -pub fn brute_force( - raqes: &[Raqe], - deployments: &[Deployment], - facts: &WorkloadFacts, - accuracy: &Accuracy, -) -> Vec { - let mut mappings = Vec::new(); - for_each_mapping(raqes, deployments, facts, accuracy, |mapping| { - mappings.push(mapping.to_vec()) - }); - mappings -} - -/// Visit each feasible mapping without retaining the complete Cartesian -/// product. The visitor receives a borrowed, index-aligned mapping that is -/// valid only for the duration of the call. Returns the number visited. -pub fn for_each_mapping( - raqes: &[Raqe], - deployments: &[Deployment], - facts: &WorkloadFacts, - accuracy: &Accuracy, - mut visit: impl FnMut(&Mapping), -) -> u64 { - for_each_mapping_while(raqes, deployments, facts, accuracy, |mapping| { - visit(mapping); - true - }) - .visited -} - -/// Result of a streaming enumeration. `completed` is false when the visitor -/// stopped enumeration early. -#[derive(Debug, Clone, Copy, PartialEq, Eq)] -pub struct EnumerationResult { - pub visited: u64, - pub completed: bool, -} - -/// Visit feasible mappings until the visitor returns false. This makes sample -/// and bounded inspection modes safe: they never need to enumerate the rest -/// of the Cartesian product after collecting enough examples. -pub fn for_each_mapping_while( - raqes: &[Raqe], - deployments: &[Deployment], - facts: &WorkloadFacts, - accuracy: &Accuracy, - mut visit: impl FnMut(&Mapping) -> bool, -) -> EnumerationResult { - let choices: Vec> = raqes - .iter() - .map(|r| eligible_deployments_for(r, deployments, facts, accuracy)) - .collect(); - - if choices.iter().any(|c| c.is_empty()) { - return EnumerationResult { - visited: 0, - completed: true, - }; - } - - fn visit_depth_first( - choices: &[Vec], - mapping: &mut Mapping, - visit: &mut impl FnMut(&Mapping) -> bool, - visited: &mut u64, - ) -> bool { - if mapping.len() == choices.len() { - *visited += 1; - return visit(mapping); - } - for &deployment in &choices[mapping.len()] { - mapping.push(deployment); - let should_continue = visit_depth_first(choices, mapping, visit, visited); - mapping.pop(); - if !should_continue { - return false; - } - } - true - } - - let mut visited = 0; - let completed = visit_depth_first( - &choices, - &mut Vec::with_capacity(raqes.len()), - &mut visit, - &mut visited, - ); - EnumerationResult { visited, completed } -} - -#[cfg(test)] -mod tests { - use super::*; - use crate::candidates; - use crate::test_support::{facts, measured_at, perfect_accuracy, METRIC}; - use crate::{table_accuracy, AtomicCostEntry, Capability, Deployment, LabelSet}; - - fn cost(sketch: &str) -> AtomicCostEntry { - AtomicCostEntry { - sketch: sketch.to_string(), - // A heap that holds any merge, so heap top-k fixtures merge freely. - sketch_config: serde_json::json!({"params": {"heap": 1u64 << 40}}), - mem_bytes_per_instance: 1.0, - insert_cpu_secs: 1.0, - merge_cpu_secs: 1.0, - query_cpu_secs: 1.0, - query_accuracy: perfect_accuracy(), - accuracy_metric: "precision_at_k".into(), - measured_at: measured_at(), - } - } - - fn build_all_candidates(raqes: &[Raqe], costs: &[AtomicCostEntry]) -> Vec { - candidates::build_all_candidates(raqes, costs, &facts(1, 1), false, &table_accuracy) - } - - fn raqe(id: &str, interval: u64, lookback: u64) -> Raqe { - Raqe { - id: id.to_string(), - capability: Capability::TopKByValue, - lookback_ms: lookback, - interval_ms: interval, - metric: METRIC.into(), - spatial_filter: String::new(), - grouping_labels: LabelSet::new(), - accuracy_sla: 0.5, - latency_sla_ms: None, - topk_k: None, - accuracy_covers_share: None, - } - } - - #[test] - fn two_raqes_can_share_one_deployment() { - let raqes = vec![raqe("a", 60_000, 3_600_000), raqe("b", 60_000, 3_600_000)]; - let table = vec![cost("cms-heap-topk-fastpath-vector2d")]; - let deployments = build_all_candidates(&raqes, &table); - - assert!(unservable(&raqes, &deployments, &facts(1, 1), &table_accuracy).is_empty()); - let mappings = brute_force(&raqes, &deployments, &facts(1, 1), &table_accuracy); - assert!( - mappings.iter().any(|m| m[0] == m[1]), - "expected at least one mapping where both identical RAQEs land on the same deployment" - ); - } - - #[test] - fn unservable_raqe_yields_no_mappings() { - let raqes = vec![raqe("a", 60_000, 3_600_000)]; - let table: Vec = Vec::new(); // no configs at all - let deployments = build_all_candidates(&raqes, &table); - - assert_eq!( - unservable(&raqes, &deployments, &facts(1, 1), &table_accuracy), - vec!["a".to_string()] - ); - assert!(brute_force(&raqes, &deployments, &facts(1, 1), &table_accuracy).is_empty()); - } - - #[test] - fn streaming_visits_the_same_mappings_as_eager_enumeration() { - let raqes = vec![raqe("a", 60_000, 3_600_000), raqe("b", 60_000, 3_600_000)]; - let deployments = build_all_candidates(&raqes, &[cost("cms-heap-topk-fastpath-vector2d")]); - let eager = brute_force(&raqes, &deployments, &facts(1, 1), &table_accuracy); - let mut streamed = Vec::new(); - let count = for_each_mapping( - &raqes, - &deployments, - &facts(1, 1), - &table_accuracy, - |mapping| streamed.push(mapping.clone()), - ); - assert_eq!(count as usize, eager.len()); - assert_eq!(streamed, eager); - } - - #[test] - fn streaming_can_stop_after_a_fixed_number_of_mappings() { - let raqes = vec![raqe("a", 60_000, 3_600_000), raqe("b", 60_000, 3_600_000)]; - let deployments = build_all_candidates(&raqes, &[cost("cms-heap-topk-fastpath-vector2d")]); - let mut sampled = Vec::new(); - let result = for_each_mapping_while( - &raqes, - &deployments, - &facts(1, 1), - &table_accuracy, - |mapping| { - sampled.push(mapping.clone()); - sampled.len() < 2 - }, - ); - - assert_eq!(result.visited, 2); - assert!(!result.completed); - assert_eq!(sampled.len(), 2); - } -} diff --git a/rqe-optimizer/src/lib.rs b/rqe-optimizer/src/lib.rs deleted file mode 100644 index 3c6a70e1..00000000 --- a/rqe-optimizer/src/lib.rs +++ /dev/null @@ -1,1101 +0,0 @@ -//! v1 brute-force solver for the RAQE -> sketch-deployment mapping problem. -//! `docs/rqe_sketch_deployment_v1.md` is the problem statement; -//! `rqe_optimizer_candidates.md` and `rqe_optimizer_cost_model.md` hold the -//! detailed planning design. -//! -//! Three independent stages, so an ILP can replace [`enumerate`] alone: -//! [`candidates`] builds 𝒟 and per-RAQE eligibility, [`enumerate`] searches, -//! and [`analytical_cost_model`] scores. -//! -//! Every number the algorithm uses is caller-supplied, except the query -//! output size estimates in [`analytical_cost_model`]. - -pub mod analytical_cost_model; -pub mod autosketch; -pub mod candidates; -pub mod enumerate; -pub mod milp; -pub mod pareto; -pub mod saturation; -pub mod theory; -pub mod usage; - -use std::collections::BTreeMap; -use std::collections::BTreeSet; - -pub use aqpbm_core::{AtomicCostEntry, AtomicCostTable}; - -/// Whole milliseconds. Window alignment is expressed with exact `%` checks. -pub type Millis = u64; - -/// Rates (CPU-sec/sec, samples/sec) stay per second. -pub(crate) fn secs(ms: Millis) -> f64 { - ms as f64 / 1000.0 -} - -/// A group-by key, compared as a set: a deployment serves a RAQE with the -/// same one, or a subset of it when the family is -/// [`mergeable_across_groups`](FamilyProperties::mergeable_across_groups). -pub type LabelSet = BTreeSet; - -/// What an RAQE's statistic needs. A variant becomes a candidate only -/// if it is in both `families()` and [`DEPLOYABLE_FAMILIES`]. -#[derive(Debug, Clone, Copy, PartialEq, Eq, PartialOrd, Ord, Hash)] -pub enum Capability { - Sum, - Count, - Min, - Max, - RateOrIncrease, - Quantile, - Cardinality, - /// `topk(k, sum_over_time(x[w]))`: ranked by summed value. - TopKByValue, - /// `topk(k, count_over_time(x[w]))`: ranked by sample count. - TopKByCount, -} - -impl Capability { - pub const ALL: [Capability; 9] = [ - Capability::Sum, - Capability::Count, - Capability::Min, - Capability::Max, - Capability::RateOrIncrease, - Capability::Quantile, - Capability::Cardinality, - Capability::TopKByValue, - Capability::TopKByCount, - ]; - - /// `AtomicCostEntry.sketch` values serving this capability. These are - /// sketch-bench *variant* strings, not algorithm names: fastpath and - /// regularpath have different cost profiles, so `"cms"` would be - /// ambiguous. Lists what `study_saturation.py --phase optimizer-cost` - /// measures, plus families it doesn't run yet; - /// an unmeasured name here is harmless, it just matches no row. - pub fn families(self) -> &'static [&'static str] { - match self { - // Exact multi-subpopulation accumulators, not sketches. Count is - // a sum of 1s, so both use `exact-sum`, but as separate - // capabilities they never share one accumulator. Likewise min and - // max, so a min query never lands on, or shares, a max one. - Capability::Sum | Capability::Count => &["exact-sum"], - Capability::Min => &["exact-min"], - Capability::Max => &["exact-max"], - Capability::RateOrIncrease => &["exact-increase"], - // Hydra (one grid for every subgrouping of a schema) is planned only - // with undeployable families allowed: ASAPQuery doesn't run it yet. - Capability::Quantile => &["kll-percall", "dd", "hydra-kll"], - // univmon-cardinality is registered under KeyedCardinality in - // sketch-bench, but its ground truth (`KeyedCardinalityGT`) is - // "count of keys with a nonzero total" -- plain distinct-key - // count, same target as HLL. Second candidate, not a second - // capability. - Capability::Cardinality => &[ - "hll", - "univmon-cardinality", - "hydra-hll", - "hydra-univmon-cardinality", - ], - // The sketch is the same; ASAPQuery deploys the two kinds with - // different `count_events`, so they never share one. - Capability::TopKByValue | Capability::TopKByCount => &[ - "cms-heap-topk-fastpath-vector2d", - "countsketch-heap-topk-fastpath-vector2d", - "univmon-topk", - ], - } - } - - /// The variants the MILP and AutoSketch plan with: the subset of - /// [`Capability::families`] in [`DEPLOYABLE_FAMILIES`], or all of them - /// when `allow_undeployable_families` (for studies; the plan can't be - /// deployed). - pub fn candidate_families( - self, - allow_undeployable_families: bool, - ) -> impl Iterator { - self.families().iter().copied().filter(move |family| { - allow_undeployable_families || DEPLOYABLE_FAMILIES.contains(family) - }) - } -} - -/// Variants ASAPQuery can deploy, each with the `AggregationType` its query -/// engine runs. Both sides use `asap_sketchlib` for the sketches. -pub const DEPLOYABLE_FAMILIES: &[&str] = &[ - "exact-sum", // MultipleSum - "exact-min", // MultipleMinMax, sub_type "min" - "exact-max", // MultipleMinMax, sub_type "max" - "exact-increase", // MultipleIncrease - "kll-percall", // DatasketchesKLL - "dd", // DDSketch - "hll", // HLL - "cms-heap-topk-fastpath-vector2d", // CountMinSketchWithHeap -]; - -/// The variant every [`FamilyProperties::needs_delta_set_key_tracker`] -/// deployment pays for alongside its sketch. Never a candidate on its own. -pub const KEY_TRACKER_FAMILY: &str = "exact-delta-set"; - -/// The `k` a top-k RAQE asks for when it sets no [`Raqe::topk_k`], and the -/// heap capacity of a row that sets no `heap` (sketch-bench's `TOPK_K`). -pub const TOPK_K: u64 = 32; - -/// Top-k families whose heap capacity is a config knob (sketch-bench's -/// `heap=`). A deployment answering from `m` merged windows keeps a heap of -/// `m · k`, so the merged heaps still hold the true top `k` -/// (approximately: a working assumption, not a guarantee). -pub fn has_heap(sketch: &str) -> bool { - matches!( - sketch, - "cms-heap-topk-fastpath-vector2d" | "countsketch-heap-topk-fastpath-vector2d" - ) -} - -/// The heap a heap top-k deployment with `window_ms` windows needs to serve -/// a top-`k` RAQE over `lookback_ms`: `m · k` for its `m` merged windows, or -/// `None` when the lookback isn't whole windows. [`Deployment::heap_needed`] -/// for a built one. -pub fn heap_needed(lookback_ms: Millis, window_ms: Millis, k: u64) -> Option { - if window_ms == 0 || !lookback_ms.is_multiple_of(window_ms) { - return None; - } - (lookback_ms / window_ms).checked_mul(k) -} - -/// The `k` a top-k `topk_k` param names, in any JSON form (10 or 10.0): -/// absent means [`TOPK_K`]. The one decoding of the knob, as sketch-bench's -/// `answered_k` reads it. -pub fn params_topk_k(topk_k: Option) -> u64 { - topk_k.map_or(TOPK_K, |k| k.round() as u64) -} - -/// The `topk_k` param that encodes `k`: none at [`TOPK_K`], which rows and -/// curves leave implicit, else `k`. The one encoding of the knob. -pub fn topk_k_param(k: u64) -> Option { - (k != TOPK_K).then_some(k) -} - -/// The `k` a top-k cost row or deployment's `topk_k` param names. -pub fn config_topk_k(config: &AtomicCostEntry) -> u64 { - params_topk_k(config.sketch_config["params"]["topk_k"].as_f64()) -} - -/// The `k` a top-k config answers: a heap family's `topk_k`; a fixed-k -/// family (UnivMon's top-k) answers [`TOPK_K`] only. -pub fn answered_k(config: &AtomicCostEntry) -> u64 { - if has_heap(&config.sketch) { - config_topk_k(config) - } else { - TOPK_K - } -} - -/// Whether `config` can answer a top-`k` query: a heap family answering at -/// least `k` (cut to `k`), or a fixed-k family at exactly its `k`. -pub fn serves_topk_k(config: &AtomicCostEntry, k: u64) -> bool { - if has_heap(&config.sketch) { - config_topk_k(config) >= k - } else { - k == TOPK_K - } -} - -/// A heap family's capacity: its `heap` param, else its `k` (sketch-bench's -/// default). `None` for other families. -pub fn heap_capacity(config: &AtomicCostEntry) -> Option { - has_heap(&config.sketch).then(|| { - let heap = &config.sketch_config["params"]["heap"]; - // A number in any JSON form (128, 128.0); absent is k. - heap.as_f64() - .map_or_else(|| config_topk_k(config), |h| h.round() as u64) - }) -} - -/// What a variant's algorithm can do, as opposed to what it measured. -#[derive(Debug, Clone, Copy, PartialEq, Eq)] -pub struct FamilyProperties { - /// Windows fold into one. A family without it only serves `L == x`. - pub mergeable_across_windows: bool, - /// Groups' states fold into their parent group's: a deployment grouped by - /// `G` serves a RAQE grouped by any subset of `G` (a roll-up). Not top-k, - /// whose heaps drop items that are in the parent's top k but no child's. - pub mergeable_across_groups: bool, - /// One sketch of fixed size holds every group: the design doc's Shared + - /// Fixed cell. Memory, merge and storage don't scale with `card(G)`. - /// Exact accumulators are Shared + PerKey and `false` here, because the - /// export prices them per group. - pub one_fixed_size_sketch_for_all_groups: bool, - /// The sketch can't list its groups, so a DeltaSet records which keys - /// each window saw, for the query to probe. - pub needs_delta_set_key_tracker: bool, - /// Hydra: one grid over a label schema `Λ` (the deployment's - /// `grouping_labels`) answers any non-empty subgrouping of it directly, - /// from the subkeys every insert fans out to. Not a roll-up: nothing is - /// merged across groups. Its accuracy is measured per grouping - /// (`hydra_saturation.csv`). - pub answers_any_subgrouping: bool, - /// Answers without error, so its accuracy comes from the cost table - /// rather than a saturation curve. - pub exact: bool, - /// The accuracy key this variant's comparator reports, and which way it - /// runs. `None` only for [`KEY_TRACKER_FAMILY`], which is never checked. - pub accuracy: Option<(&'static str, AccuracyDirection)>, -} - -/// Panics on a variant with no entry: guessing would misprice it. -/// Accuracy keys are the metrics `study_saturation.py` records per variant, -/// which every cost-table row names as its `accuracy_metric`. -pub fn family_properties(variant: &str) -> FamilyProperties { - use AccuracyDirection::{HigherIsBetter, LowerIsBetter}; - let one_sketch_per_group = |metric, direction| FamilyProperties { - mergeable_across_windows: true, - mergeable_across_groups: true, - one_fixed_size_sketch_for_all_groups: false, - needs_delta_set_key_tracker: false, - answers_any_subgrouping: false, - exact: false, - accuracy: Some((metric, direction)), - }; - // One grid holds every group; a DeltaSet lists the keys to probe. - let hydra = |metric| FamilyProperties { - mergeable_across_windows: true, - mergeable_across_groups: false, - one_fixed_size_sketch_for_all_groups: true, - needs_delta_set_key_tracker: true, - answers_any_subgrouping: true, - exact: false, - accuracy: Some((metric, LowerIsBetter)), - }; - // `relative_error` is the worst group's, not `relative_error_mean`. - let exact = FamilyProperties { - exact: true, - ..one_sketch_per_group("relative_error", LowerIsBetter) - }; - match variant { - "exact-sum" | "exact-min" | "exact-max" => exact, - // Not rolled up: its merge joins two pieces of one counter in time - // (the later start after a drop is a reset), so merging two groups' - // counters is wrong. A coarse increase is the sum of per-series - // increases, which needs per-series state and a sum, not this merge. - "exact-increase" => FamilyProperties { - mergeable_across_groups: false, - ..exact - }, - KEY_TRACKER_FAMILY => FamilyProperties { - accuracy: None, - ..exact - }, - "hll" | "univmon-cardinality" => one_sketch_per_group("relative_error", LowerIsBetter), - "kll-percall" => one_sketch_per_group("mean_rank_err", LowerIsBetter), - "dd" => one_sketch_per_group("mean_relative_value_error", LowerIsBetter), - "cms-heap-topk-fastpath-vector2d" - | "countsketch-heap-topk-fastpath-vector2d" - | "univmon-topk" => FamilyProperties { - mergeable_across_groups: false, - ..one_sketch_per_group("precision_at_k", HigherIsBetter) - }, - "hydra-hll" | "hydra-univmon-cardinality" => hydra("relative_error"), - "hydra-kll" => hydra("mean_rank_err"), - _ => panic!("{variant} has no FamilyProperties; add it to family_properties"), - } -} - -/// Facts about one metric, given as input. RAQEs and deployments on the same -/// metric read the same samples. -#[derive(Debug, Clone)] -pub struct MetricFacts { - /// Every label the metric's series carry. - pub labels: LabelSet, - /// Each series yields one sample per scrape. - pub scrape_interval_ms: Millis, - /// `card(X)`: distinct value combinations of each label set `X` in use. - /// Must include `labels` itself, whose cardinality is the series count. - /// A label set never has more groups than any superset of it. - pub cardinality: BTreeMap, - /// `(lo, hi)`: smallest and largest positive sample value, if known. - /// Sizes DDSketch; without it, DDSketch keeps the measured memory. - pub value_range: Option<(f64, f64)>, - /// Fitted data parameters per grouping `G`, for reading sketch accuracy - /// off the saturation curves. Without one, no sketch serves `G`. - pub data_shape: BTreeMap, - /// The `hydra_saturation.csv` dataset whose schema and shares match this - /// metric's, and that schema `Λ` (its labels; `cardinality` must hold - /// it). Hydra grids are built only over `Λ`, the width the study - /// measures, from cost rows measured on the dataset. Without one, no - /// Hydra candidate is built on the metric. - pub hydra_dataset: Option<(String, LabelSet)>, -} - -impl MetricFacts { - /// `λ`: samples/sec across all of the metric's series. - pub fn arrival_rate_per_sec(&self) -> f64 { - self.cardinality[&self.labels] as f64 / secs(self.scrape_interval_ms) - } -} - -/// [`MetricFacts`] keyed by metric name. -pub type WorkloadFacts = BTreeMap; - -/// Every problem with `raqes` and the `facts` serving them. The rest of the -/// crate indexes `facts` without checks, so call this first on caller input. -pub fn validate_facts(raqes: &[Raqe], facts: &WorkloadFacts) -> Result<(), Vec> { - let mut problems = BTreeSet::new(); - for raqe in raqes { - let id = &raqe.id; - if raqe.lookback_ms == 0 || raqe.interval_ms == 0 { - problems.insert(format!("{id}: lookback and interval must be nonzero")); - } - if !raqe.spatial_filter.is_empty() { - problems.insert(format!("{id}: spatial filters are not supported yet")); - } - // An SLA no candidate's metric can meet would read as "unservable" - // rather than bad input. Every metric is >= 0, and every - // higher-is-better one is a fraction (precision), so its floor is <= 1. - let sla = raqe.accuracy_sla; - let floors_a_fraction = raqe.capability.families().iter().any(|family| { - family_properties(family) - .accuracy - .is_some_and(|(_, direction)| direction == AccuracyDirection::HigherIsBetter) - }); - let max = if floors_a_fraction { - 1.0 - } else { - f64::INFINITY - }; - if !(sla.is_finite() && (0.0..=max).contains(&sla)) { - problems.insert(format!("{id}: accuracy SLA {sla} is outside [0, {max}]")); - } - if let Some(limit) = raqe.latency_sla_ms { - if !(limit.is_finite() && limit > 0.0) { - problems.insert(format!("{id}: latency SLA {limit} ms is not positive")); - } - } - let (metric, grouping) = (&raqe.metric, &raqe.grouping_labels); - let Some(metric_facts) = facts.get(metric) else { - problems.insert(format!("{metric}: no facts for this metric")); - continue; - }; - let all_labels = &metric_facts.labels; - let cardinality = &metric_facts.cardinality; - let scrape_ms = metric_facts.scrape_interval_ms; - if scrape_ms == 0 { - problems.insert(format!("{metric}: scrape interval is 0")); - } else if !(raqe.lookback_ms.is_multiple_of(scrape_ms) - && raqe.interval_ms.is_multiple_of(scrape_ms)) - { - problems.insert(format!( - "{id}: lookback {} ms and interval {} ms must be multiples of the {scrape_ms} ms scrape interval", - raqe.lookback_ms, raqe.interval_ms - )); - } - if let Some((lo, hi)) = metric_facts.value_range { - if !(lo > 0.0 && hi >= lo && hi.is_finite()) { - problems.insert(format!( - "{metric}: value range ({lo}, {hi}) needs 0 < lo <= hi < inf" - )); - } - } - if !grouping.is_subset(all_labels) { - problems.insert(format!( - "{metric}: grouping {grouping:?} is not a subset of its labels {all_labels:?}" - )); - } - for labels in [grouping, all_labels] { - match cardinality.get(labels) { - None => problems.insert(format!("{metric}: no cardinality for {labels:?}")), - Some(0) => problems.insert(format!("{metric}: cardinality of {labels:?} is 0")), - Some(_) => false, - }; - } - if let Some((_, schema)) = &metric_facts.hydra_dataset { - if !schema.is_subset(all_labels) { - problems.insert(format!( - "{metric}: Hydra schema {schema:?} is not a subset of its labels {all_labels:?}" - )); - } - if cardinality.get(schema).is_none_or(|&groups| groups == 0) { - problems.insert(format!( - "{metric}: Hydra schema {schema:?} needs a nonzero cardinality" - )); - } - } - } - // Dropping labels never adds groups. A roll-up's merge count, - // `card(G_d) · L/x − card(G_r)`, relies on it, so it is checked between - // the label sets a plan can use: every RAQE grouping on the metric, and - // its full label set. A label set no RAQE groups by is never read. - let mut used: BTreeMap<&str, BTreeSet<&LabelSet>> = BTreeMap::new(); - for raqe in raqes { - if let Some(metric_facts) = facts.get(&raqe.metric) { - used.entry(&raqe.metric) - .or_insert_with(|| BTreeSet::from([&metric_facts.labels])) - .insert(&raqe.grouping_labels); - } - } - for (metric, label_sets) in &used { - let cardinality = &facts[*metric].cardinality; - for coarse in label_sets { - for fine in label_sets { - if let (Some(&coarse_groups), Some(&fine_groups)) = - (cardinality.get(*coarse), cardinality.get(*fine)) - { - if coarse.is_subset(fine) && coarse_groups > fine_groups { - problems.insert(format!( - "{metric}: {coarse:?} has {coarse_groups} groups but its superset \ - {fine:?} only {fine_groups}" - )); - } - } - } - } - } - if problems.is_empty() { - Ok(()) - } else { - Err(problems.into_iter().collect()) - } -} - -/// Which way an accuracy metric runs. Most comparators report an error -/// (lower better); top-k reports `precision_at_k`/`recall_at_k` (higher -/// better), so its tolerance is a floor. -/// -/// Named per family in [`family_properties`], never inferred from the metric -/// name: an unanticipated name would get a silent wrong default, and the -/// failure mode is a hard constraint accepting exactly what it should reject. -#[derive(Debug, Clone, Copy, PartialEq, Eq)] -pub enum AccuracyDirection { - /// `measured <= tolerance` passes -- the metric is an error. - LowerIsBetter, - /// `measured >= tolerance` passes -- the metric is a score. - HigherIsBetter, -} - -/// One repeating query expression. -#[derive(Debug, Clone)] -pub struct Raqe { - pub id: String, - pub capability: Capability, - /// `L_i`. - pub lookback_ms: Millis, - /// `T_i`: how often this RAQE is queried. - pub interval_ms: Millis, - pub metric: String, - /// Series selector beyond the metric name, as canonical PromQL matcher - /// text; empty for none. Part of stream identity, compared as an opaque - /// string. - // ponytail: validate_facts rejects non-empty filters until facts carry - // per-filter cardinality; without it a filtered stream is costed as the - // whole metric. - pub spatial_filter: String, - /// `G`: the query's group-by labels. - pub grouping_labels: LabelSet, - /// `tol_i`: hard constraint on each candidate's own - /// [accuracy metric](FamilyProperties::accuracy), a ceiling or a floor by - /// its direction. One number means different guarantees across metrics - /// (sketch-bench#172). - pub accuracy_sla: f64, - /// Hard ceiling on modeled query latency; `None` for no limit. Only the - /// MILP enforces it. - pub latency_sla_ms: Option, - /// A top-k RAQE's `k`; `None` means [`TOPK_K`]. Ignored for other - /// capabilities. - pub topk_k: Option, - /// The groups the accuracy SLA covers: those holding at least this - /// share of the metric's records, plus always the largest; `None` for - /// every group. Read only by Hydra's accuracy, measured per coverage. - pub accuracy_covers_share: Option, -} - -impl Raqe { - /// The `k` this RAQE asks for: its [`Raqe::topk_k`], else [`TOPK_K`]. - pub fn topk_k(&self) -> u64 { - self.topk_k.unwrap_or(TOPK_K) - } - - /// Whether `accuracy`, in `sketch`'s [`accuracy_key`], clears the SLA. - /// Unknown or non-finite never passes. The MILP's eligibility and the - /// AutoSketch baseline both decide here. - pub fn meets_sla(&self, sketch: &str, accuracy: Option) -> bool { - let (_, direction) = accuracy_key(sketch); - accuracy.is_some_and(|value| { - value.is_finite() - && match direction { - AccuracyDirection::LowerIsBetter => value <= self.accuracy_sla, - AccuracyDirection::HigherIsBetter => value >= self.accuracy_sla, - } - }) - } -} - -/// `sketch`'s [accuracy metric](FamilyProperties::accuracy) and direction. -/// Panics for [`KEY_TRACKER_FAMILY`], which is never a candidate. -pub fn accuracy_key(sketch: &str) -> (&'static str, AccuracyDirection) { - family_properties(sketch) - .accuracy - .unwrap_or_else(|| panic!("{sketch} has no accuracy metric, so it can't be a candidate")) -} - -/// The accuracy `raqe` gets from a deployment, or `None` when unknown -- -/// unknown never passes. Production uses -/// [`saturation::SaturationCurves::accuracy`]. -pub type Accuracy<'a> = dyn Fn(&Raqe, &Deployment) -> Option + 'a; - -/// The cost table's measured value, which is the exact accumulators' -/// accuracy: they merge without loss, so the window size doesn't matter. -pub fn table_accuracy(_raqe: &Raqe, deployment: &Deployment) -> Option { - Some(row_accuracy(&deployment.config)) -} - -/// `config`'s accuracy: its `query_accuracy[accuracy_metric]`. Panics when -/// `accuracy_metric` isn't its family's [`accuracy_key`] or the score is -/// missing: either means the cost export is broken. -pub fn row_accuracy(config: &AtomicCostEntry) -> f64 { - let (metric, _) = accuracy_key(&config.sketch); - assert_eq!( - config.accuracy_metric, metric, - "{} {}: the row's accuracy_metric isn't its family's; the cost export is broken", - config.sketch, config.sketch_config - ); - config.accuracy().unwrap_or_else(|| { - panic!( - "{} {} has no {metric}; the cost export is broken", - config.sketch, config.sketch_config - ) - }) -} - -/// A candidate deployment (`docs/rqe_optimizer_candidates.md`): one configuration, one grouped stream, and a -/// sliding sketch window. Retention is an execution/storage concern, not an -/// optimizer candidate dimension. -#[derive(Debug, Clone, PartialEq)] -pub struct Deployment { - pub capability: Capability, - pub metric: String, - pub spatial_filter: String, - /// `G`: one instance per group, per window, unless the family keeps one - /// for all groups. - pub grouping_labels: LabelSet, - pub config: AtomicCostEntry, - /// `x`: materialized sketch-window size. - pub window_ms: Millis, - /// `y`: materialized sketch slide. - pub slide_ms: Millis, - /// The [`KEY_TRACKER_FAMILY`] row, for a family that - /// [needs one](FamilyProperties::needs_delta_set_key_tracker). - pub key_tracker: Option, -} - -impl Deployment { - pub fn properties(&self) -> FamilyProperties { - family_properties(&self.config.sketch) - } - - /// Number of active overlapping instances receiving each item. - pub fn active_instance_count(&self) -> Option { - if self.slide_ms == 0 || !self.window_ms.is_multiple_of(self.slide_ms) { - return None; - } - Some(self.window_ms / self.slide_ms) - } - - /// Number of non-overlapping instances merged for one query window. - pub fn query_instance_count(&self, lookback_ms: Millis) -> Option { - if self.window_ms == 0 || !lookback_ms.is_multiple_of(self.window_ms) { - return None; - } - Some(lookback_ms / self.window_ms) - } - - /// The heap a heap top-k deployment needs to serve `lookback_ms`: `m · k` - /// for its `m` merged windows. `None` when the lookback isn't whole - /// windows. - pub fn heap_needed(&self, lookback_ms: Millis, k: u64) -> Option { - heap_needed(lookback_ms, self.window_ms, k) - } - - /// Closed instances kept to serve a lookback: those wholly inside it, - /// which start in `[now − L, now − x]`, so `(L − x) / y + 1`. - pub fn closed_instance_count(&self, lookback_ms: Millis) -> Option { - self.query_instance_count(lookback_ms)?; - self.active_instance_count()?; - Some((lookback_ms - self.window_ms) / self.slide_ms + 1) - } -} - -/// A full mapping: one deployment index (into the `deployments` slice passed -/// to `enumerate::brute_force`) per RAQE, aligned with the `raqes` slice's -/// order. `z_{i,D} = 1 <=> mapping[i] == D`'s index; `u_D = 1 <=>` `D`'s -/// index appears anywhere in `mapping` -- `y` is never stored -/// separately, it's always derived from `mapping`. -pub type Mapping = Vec; - -/// Fixtures shared by the unit tests: one metric `m`, grouped by nothing, -/// served by top-k. -#[cfg(test)] -pub(crate) mod test_support { - use super::*; - - pub const METRIC: &str = "m"; - - /// Clears an SLA of 0.5 on every family's metric: errors 0, precision 1, - /// so a row renamed to any family stays feasible. - pub fn perfect_accuracy() -> BTreeMap { - [ - "relative_error", - "mean_rank_err", - "mean_relative_value_error", - ] - .map(|metric| (metric.to_string(), 0.0)) - .into_iter() - .chain([("precision_at_k".to_string(), 1.0)]) - .collect() - } - - /// The accuracy metric a row of `sketch` names: its family's. - pub fn metric_of(sketch: &str) -> String { - accuracy_key(sketch).0.to_string() - } - - /// Where a fixture row was "measured": the cost table's canonical point. - pub fn measured_at() -> aqpbm_core::MeasuredAt { - aqpbm_core::MeasuredAt { - items_per_instance: 1_000_000, - keys_per_instance: Some(10_000), - value_range: None, - merge_operand_items: None, - distribution: None, - dataset: None, - schema_width: None, - } - } - - /// `groups` groups and `series` raw series scraped every second, so - /// `λ = series`. Not validated: unit tests set `groups` freely. - pub fn facts(groups: u64, series: u64) -> WorkloadFacts { - let labels = LabelSet::from(["series".to_string()]); - WorkloadFacts::from([( - METRIC.to_string(), - MetricFacts { - labels: labels.clone(), - scrape_interval_ms: 1_000, - cardinality: BTreeMap::from([(LabelSet::new(), groups), (labels, series)]), - value_range: None, - data_shape: BTreeMap::new(), - hydra_dataset: None, - }, - )]) - } - - pub fn raqe(lookback_ms: Millis, interval_ms: Millis) -> Raqe { - Raqe { - id: "r".into(), - capability: Capability::TopKByValue, - lookback_ms, - interval_ms, - metric: METRIC.into(), - spatial_filter: String::new(), - grouping_labels: LabelSet::new(), - accuracy_sla: 0.5, - latency_sla_ms: None, - topk_k: None, - accuracy_covers_share: None, - } - } - - /// Per-instance memory and insert/merge/query CPU, then window and slide. - pub fn deployment( - memory: f64, - insert: f64, - merge: f64, - query: f64, - window_ms: Millis, - slide_ms: Millis, - ) -> Deployment { - Deployment { - capability: Capability::TopKByValue, - metric: METRIC.into(), - spatial_filter: String::new(), - grouping_labels: LabelSet::new(), - config: AtomicCostEntry { - sketch: "cms-heap-topk-fastpath-vector2d".into(), - // A heap that holds any merge, so heap top-k fixtures merge freely. - sketch_config: serde_json::json!({"params": {"heap": 1u64 << 40}}), - mem_bytes_per_instance: memory, - insert_cpu_secs: insert, - merge_cpu_secs: merge, - query_cpu_secs: query, - query_accuracy: perfect_accuracy(), - accuracy_metric: "precision_at_k".into(), - measured_at: measured_at(), - }, - window_ms, - slide_ms, - key_tracker: None, - } - } - - pub fn label_set(names: &[&str]) -> LabelSet { - names.iter().map(|name| name.to_string()).collect() - } - - /// 5 services × 10 endpoints, one series per endpoint, scraped every - /// second: the coarse and fine groupings of a roll-up. - pub fn service_endpoint_facts() -> WorkloadFacts { - let service = label_set(&["service"]); - let service_endpoint = label_set(&["service", "endpoint"]); - WorkloadFacts::from([( - METRIC.to_string(), - MetricFacts { - labels: service_endpoint.clone(), - scrape_interval_ms: 1_000, - cardinality: BTreeMap::from([(service, 5), (service_endpoint, 50)]), - value_range: None, - data_shape: BTreeMap::new(), - hydra_dataset: None, - }, - )]) - } - - /// A per-group KLL deployment by `grouping`, every per-instance cost 1. - pub fn kll_by(grouping: &[&str], window_ms: Millis) -> Deployment { - let base = deployment(1.0, 1.0, 1.0, 1.0, window_ms, window_ms); - Deployment { - capability: Capability::Quantile, - grouping_labels: label_set(grouping), - config: AtomicCostEntry { - sketch: "kll-percall".into(), - sketch_config: serde_json::json!({"params": {"k": 200}}), - accuracy_metric: metric_of("kll-percall"), - ..base.config - }, - ..base - } - } - - pub fn quantile_by(grouping: &[&str], lookback_ms: Millis, interval_ms: Millis) -> Raqe { - Raqe { - capability: Capability::Quantile, - grouping_labels: label_set(grouping), - ..raqe(lookback_ms, interval_ms) - } - } -} - -#[cfg(test)] -mod tests { - use super::*; - use test_support::{deployment, raqe, METRIC}; - - fn labels(names: &[&str]) -> LabelSet { - names.iter().map(|s| s.to_string()).collect() - } - - /// One quantile SLA: KLL is checked by rank error, DD by relative value - /// error, each against the same number. - #[test] - fn one_quantile_raqe_checks_each_family_by_its_own_metric() { - let r = Raqe { - capability: Capability::Quantile, - accuracy_sla: 0.05, - ..raqe(60_000, 60_000) - }; - let row = |sketch: &str, metric: &str, value: f64| AtomicCostEntry { - sketch: sketch.into(), - query_accuracy: BTreeMap::from([(metric.to_string(), value)]), - accuracy_metric: metric.to_string(), - ..deployment(0.0, 0.0, 0.0, 0.0, 60_000, 60_000).config - }; - let ok = |c: AtomicCostEntry| r.meets_sla(&c.sketch, Some(row_accuracy(&c))); - assert!(ok(row("kll-percall", "mean_rank_err", 0.01))); - assert!(!ok(row("kll-percall", "mean_rank_err", 0.1))); - assert!(ok(row("dd", "mean_relative_value_error", 0.01))); - assert!(!ok(row("dd", "mean_relative_value_error", 0.1))); - } - - /// DD omits its metric when every true quantile is 0; a row without it - /// must not pass, nor be skipped silently. - #[test] - #[should_panic(expected = "has no mean_relative_value_error")] - fn a_row_missing_its_familys_metric_panics() { - let config = AtomicCostEntry { - sketch: "dd".into(), - query_accuracy: BTreeMap::from([("mean_rank_err".to_string(), 0.0)]), - accuracy_metric: "mean_relative_value_error".into(), - ..deployment(0.0, 0.0, 0.0, 0.0, 60_000, 60_000).config - }; - row_accuracy(&config); - } - - /// The row's declared metric is the one read, and it must be the - /// family's: a row naming another is a broken export, not a guess. - #[test] - #[should_panic(expected = "the row's accuracy_metric isn't its family's")] - fn a_row_naming_another_metric_panics() { - let config = AtomicCostEntry { - sketch: "kll-percall".into(), - query_accuracy: BTreeMap::from([("max_rank_err".to_string(), 0.0)]), - accuracy_metric: "max_rank_err".into(), - ..deployment(0.0, 0.0, 0.0, 0.0, 60_000, 60_000).config - }; - row_accuracy(&config); - } - - #[test] - fn every_deployable_family_serves_a_capability() { - for family in DEPLOYABLE_FAMILIES { - assert!( - Capability::ALL - .iter() - .any(|c| c.families().contains(family)), - "{family} serves no capability" - ); - } - } - - #[test] - fn every_family_has_properties() { - for capability in Capability::ALL { - for family in capability.families() { - family_properties(family); - } - } - family_properties(KEY_TRACKER_FAMILY); - } - - #[test] - fn closed_instances_lie_wholly_inside_the_lookback() { - // 10-minute windows every minute, over an hour: starts in minutes - // [now − 60, now − 10]. - let sliding = deployment(1.0, 1.0, 1.0, 1.0, 600_000, 60_000); - assert_eq!(sliding.closed_instance_count(3_600_000), Some(51)); - assert_eq!(sliding.closed_instance_count(600_000), Some(1)); - assert_eq!(sliding.closed_instance_count(900_000), None); // not tiled by x - } - - #[test] - fn arrival_rate_is_series_over_scrape_interval() { - let metric_facts = MetricFacts { - scrape_interval_ms: 15_000, - ..test_support::facts(4, 600)[METRIC].clone() - }; - assert_eq!(metric_facts.arrival_rate_per_sec(), 40.0); - } - - #[test] - fn valid_facts_pass() { - let facts = WorkloadFacts::from([( - METRIC.to_string(), - MetricFacts { - labels: labels(&["service", "endpoint"]), - scrape_interval_ms: 15_000, - cardinality: BTreeMap::from([ - (labels(&["service"]), 5), - (labels(&["service", "endpoint"]), 50), - ]), - value_range: None, - data_shape: BTreeMap::new(), - hydra_dataset: None, - }, - )]); - let r = Raqe { - grouping_labels: labels(&["service"]), - ..raqe(60_000, 60_000) - }; - assert_eq!(validate_facts(&[r], &facts), Ok(())); - } - - #[test] - fn reports_every_problem_at_once() { - let facts = WorkloadFacts::from([( - METRIC.to_string(), - MetricFacts { - labels: labels(&["service"]), - scrape_interval_ms: 0, - cardinality: BTreeMap::from([(labels(&["service"]), 0)]), - value_range: Some((0.0, 1.0)), - data_shape: BTreeMap::new(), - hydra_dataset: None, - }, - )]); - let raqes = [ - Raqe { - metric: "missing".into(), - ..raqe(60_000, 60_000) - }, - Raqe { - grouping_labels: labels(&["pod"]), - ..raqe(60_000, 60_000) - }, - Raqe { - id: "filtered".into(), - grouping_labels: labels(&["service"]), - spatial_filter: r#"{job="api"}"#.into(), - latency_sla_ms: Some(f64::NAN), - ..raqe(60_000, 60_000) - }, - ]; - let problems = validate_facts(&raqes, &facts).unwrap_err(); - let has = |needle: &str| problems.iter().any(|p| p.contains(needle)); - assert!(has("missing: no facts")); - assert!(has("scrape interval is 0")); - assert!(has("not a subset")); - assert!(has("no cardinality for {\"pod\"}")); - assert!(has("cardinality of {\"service\"} is 0")); - assert!(has("filtered: spatial filters are not supported")); - assert!(has("filtered: latency SLA NaN ms is not positive")); - assert!(has("value range (0, 1) needs 0 < lo <= hi < inf")); - assert_eq!(problems.len(), 8, "{problems:?}"); - } - - #[test] - fn rejects_zero_and_scrape_unaligned_times() { - // 1 s scrape. 3_600 looks like seconds left unconverted. - let raqes = [ - Raqe { - id: "zero".into(), - ..raqe(0, 60_000) - }, - Raqe { - id: "unaligned".into(), - ..raqe(3_600, 60_000) - }, - ]; - let problems = validate_facts(&raqes, &test_support::facts(1, 1)).unwrap_err(); - assert_eq!(problems.len(), 2, "{problems:?}"); - assert!(problems[0].starts_with("unaligned: lookback 3600 ms")); - assert!(problems[1].starts_with("zero: lookback and interval must be nonzero")); - } - - /// No metric can meet a negative SLA, nor a precision floor above 1; an - /// error ceiling above 1 is merely loose. - #[test] - fn rejects_an_accuracy_sla_no_metric_can_meet() { - let sla = |id: &str, capability, accuracy_sla| Raqe { - id: id.into(), - capability, - accuracy_sla, - ..raqe(60_000, 60_000) - }; - let raqes = [ - sla("negative", Capability::Quantile, -0.1), - sla("nan", Capability::Quantile, f64::NAN), - sla("precision_above_1", Capability::TopKByValue, 1.5), - sla("loose_error", Capability::Cardinality, 1.5), - sla("exact_precision", Capability::TopKByValue, 1.0), - ]; - let problems = validate_facts(&raqes, &test_support::facts(1, 1)).unwrap_err(); - assert_eq!( - problems, - [ - "nan: accuracy SLA NaN is outside [0, inf]", - "negative: accuracy SLA -0.1 is outside [0, inf]", - "precision_above_1: accuracy SLA 1.5 is outside [0, 1]", - ] - ); - } - - /// Facts where `(service)` has more groups than its superset - /// `(service, endpoint)`, and `(pod)` has none at all. - fn inconsistent_facts() -> WorkloadFacts { - WorkloadFacts::from([( - METRIC.to_string(), - MetricFacts { - labels: labels(&["service", "endpoint", "pod"]), - scrape_interval_ms: 15_000, - cardinality: BTreeMap::from([ - (labels(&["service"]), 60), - (labels(&["service", "endpoint"]), 50), - (labels(&["pod"]), 0), - (labels(&["service", "endpoint", "pod"]), 30_000), - ]), - value_range: None, - data_shape: BTreeMap::new(), - hydra_dataset: None, - }, - )]) - } - - /// Between two RAQE groupings, the subset may not have more groups. - #[test] - fn rejects_a_used_label_set_with_more_groups_than_a_used_superset() { - let grouped = |l: &[&str]| Raqe { - grouping_labels: labels(l), - ..raqe(60_000, 60_000) - }; - assert_eq!( - validate_facts( - &[grouped(&["service"]), grouped(&["service", "endpoint"])], - &inconsistent_facts() - ) - .unwrap_err(), - [format!( - "{METRIC}: {{\"service\"}} has 60 groups but its superset \ - {{\"endpoint\", \"service\"}} only 50" - )] - ); - } - - /// A RAQE grouping may not have more groups than the series. - #[test] - fn rejects_a_grouping_with_more_groups_than_series() { - let mut facts = inconsistent_facts(); - let cardinality = &mut facts.get_mut(METRIC).unwrap().cardinality; - cardinality.insert(labels(&["service", "endpoint", "pod"]), 40); - let r = Raqe { - grouping_labels: labels(&["service"]), - ..raqe(60_000, 60_000) - }; - assert_eq!( - validate_facts(&[r], &facts).unwrap_err(), - [format!( - "{METRIC}: {{\"service\"}} has 60 groups but its superset \ - {{\"endpoint\", \"pod\", \"service\"}} only 40" - )] - ); - } - - /// Label sets no RAQE groups by are never read, so inconsistencies there - /// don't reject the workload. - #[test] - fn ignores_label_sets_no_raqe_uses() { - let r = Raqe { - grouping_labels: labels(&["service"]), - ..raqe(60_000, 60_000) - }; - assert_eq!(validate_facts(&[r], &inconsistent_facts()), Ok(())); - } - - /// Hydra's schema must be the metric's labels with a known group count. - #[test] - fn rejects_a_hydra_schema_outside_the_labels_or_without_cardinality() { - let mut facts = test_support::facts(1, 1); - let metric_facts = facts.get_mut(METRIC).unwrap(); - let mut outside = metric_facts.labels.clone(); - outside.insert("pod".into()); - metric_facts.hydra_dataset = Some(("hydra_test".into(), outside.clone())); - let problems = validate_facts(&[raqe(60_000, 60_000)], &facts).unwrap_err(); - assert_eq!( - problems, - [ - format!( - "{METRIC}: Hydra schema {outside:?} is not a subset of its labels {:?}", - facts[METRIC].labels - ), - format!("{METRIC}: Hydra schema {outside:?} needs a nonzero cardinality"), - ] - ); - let metric_facts = facts.get_mut(METRIC).unwrap(); - let schema = metric_facts.labels.clone(); - metric_facts.cardinality.insert(schema.clone(), 0); - metric_facts.hydra_dataset = Some(("hydra_test".into(), schema)); - let problems = validate_facts(&[raqe(60_000, 60_000)], &facts).unwrap_err(); - assert!( - problems.iter().any(|p| p.contains("Hydra schema")), - "{problems:?}" - ); - } -} diff --git a/rqe-optimizer/src/milp.rs b/rqe-optimizer/src/milp.rs deleted file mode 100644 index 165d654b..00000000 --- a/rqe-optimizer/src/milp.rs +++ /dev/null @@ -1,824 +0,0 @@ -//! MILP solver for one scalarized RAQE deployment optimization. -//! -//! Candidate construction is documented in `docs/rqe_optimizer_candidates.md` -//! and the cost model in `docs/rqe_optimizer_cost_model.md`. It avoids -//! enumerating the Cartesian product of eligible deployment choices. - -use crate::analytical_cost_model::{self, score, PhaseCost, PlanCost, BYTES_PER_GIB}; -use crate::candidates::eligible_deployments_for; -use crate::{Accuracy, Deployment, Mapping, Raqe, WorkloadFacts}; -use good_lp::{ - default_solver, variable, Expression, ProblemVariables, ResolutionError, Solution, SolverModel, - Variable, -}; - -/// What a [`minimize`] solve minimizes. Eligibility, assignment and activation -/// rows, and latency SLAs are the same for every objective. -#[derive(Debug, Clone, Copy, PartialEq)] -pub enum Objective { - /// `w_cpu · CPU + w_mem · memory`. CPU is mean CPU-sec/sec, the area under - /// the load curve, so bursts are not priced. Memory is GiB summed over - /// every phase, as if every query evaluates at once. - AUCCost { w_cpu: f64, w_mem: f64 }, -} - -impl Default for Objective { - /// CPU only, until memory gets a price. - fn default() -> Self { - Objective::AUCCost { - w_cpu: 1.0, - w_mem: 0.0, - } - } -} - -impl Objective { - pub fn value(&self, plan_cost: &PlanCost) -> f64 { - self.weigh(PhaseCost { - cpu_secs_per_sec: plan_cost.cpu_secs_per_sec(), - memory_bytes: plan_cost.memory_bytes(), - }) - } - - fn weigh(&self, cost: PhaseCost) -> f64 { - let Objective::AUCCost { w_cpu, w_mem } = *self; - w_cpu * cost.cpu_secs_per_sec + w_mem * cost.memory_bytes / BYTES_PER_GIB - } -} - -/// The solved plan: the deployments to run and which one serves each RAQE. -#[derive(Debug, Clone)] -pub struct MilpSolution { - /// Active deployments only, in order of the first RAQE each serves. - pub deployments: Vec, - /// Index-aligned with the input RAQE slice. - pub raqes: Vec, - pub plan_cost: PlanCost, -} - -#[derive(Debug, Clone)] -pub struct PlannedDeployment { - pub deployment: Deployment, - /// Windows kept per group (per sketch, not × `card(G)`): `x/y` open plus - /// `(L − x)/y + 1` closed, for the longest lookback `L` it serves. - pub retained_instance_count: u64, -} - -#[derive(Debug, Clone)] -pub struct PlannedRaqe { - /// Index into [`MilpSolution::deployments`]. - pub deployment: usize, - /// `n = L/x` windows merged per query; 1 means Direct, more means Merge. - /// A roll-up (the RAQE's grouping differs from its deployment's) also - /// merges each coarse group's fine groups, even when `n = 1`. - pub merged_instance_count: u64, -} - -/// Minimize `objective` subject to each RAQE's latency SLA. `facts` must pass -/// [`crate::validate_facts`]. Missing eligible deployments are a caller input -/// error; use `enumerate::unservable` to report them before calling this -/// function. -pub fn minimize( - raqes: &[Raqe], - deployments: &[Deployment], - facts: &WorkloadFacts, - objective: Objective, - accuracy: &Accuracy, -) -> Result { - let eligible: Vec> = raqes - .iter() - .map(|raqe| eligible_deployments_for(raqe, deployments, facts, accuracy)) - .collect(); - assert!( - eligible.iter().all(|choices| !choices.is_empty()), - "cannot solve a workload with an unservable RAQE" - ); - - let mut variables = ProblemVariables::new(); - let active: Vec = deployments - .iter() - .map(|_| variables.add(variable().binary())) - .collect(); - let assignments: Vec> = eligible - .iter() - .map(|choices| { - choices - .iter() - .map(|&deployment_index| (deployment_index, variables.add(variable().binary()))) - .collect() - }) - .collect(); - // Weighted storage cost per deployment: the max over the RAQEs it serves. - let deployment_storage_cost: Vec = deployments - .iter() - .map(|_| variables.add(variable().min(0))) - .collect(); - - // Ingest is paid once per active deployment, merge and query once per - // assignment, and storage once per deployment for its longest lookback. - let ingest_cost: Vec = deployments - .iter() - .map(|deployment| objective.weigh(analytical_cost_model::ingest(deployment, facts))) - .collect(); - let merge_and_query_cost = |raqe: &Raqe, deployment: &Deployment| { - objective.weigh(analytical_cost_model::merge(raqe, deployment, facts)) - + objective.weigh(analytical_cost_model::query(raqe, deployment, facts)) - }; - let storage_cost_for_raqe_and_deployment = |raqe: &Raqe, deployment: &Deployment| { - objective.weigh(PhaseCost { - cpu_secs_per_sec: 0.0, - memory_bytes: analytical_cost_model::storage_bytes(raqe, deployment, facts), - }) - }; - - // Real plans cost ~1e-6 CPU-sec/sec and read in microseconds, below - // HiGHS's absolute gap (1e-6) and feasibility tolerance (1e-7). So costs - // are divided by `reference`, a plan's cost without sharing (each RAQE on - // its cheapest deployment), and latency SLAs become exclusions. The - // result is re-scored on real costs. - let reference: f64 = raqes - .iter() - .zip(&eligible) - .map(|(raqe, choices)| { - choices - .iter() - .map(|&deployment_index| { - let deployment = &deployments[deployment_index]; - ingest_cost[deployment_index] - + merge_and_query_cost(raqe, deployment) - + storage_cost_for_raqe_and_deployment(raqe, deployment) - }) - .fold(f64::INFINITY, f64::min) - }) - .sum(); - let reference = if reference.is_normal() { - reference - } else { - 1.0 - }; - - let mut goal: Expression = deployment_storage_cost.iter().sum(); - for (&cost, &active_variable) in ingest_cost.iter().zip(&active) { - goal.add_mul(cost / reference, active_variable); - } - for (raqe_index, choices) in assignments.iter().enumerate() { - for &(deployment_index, assignment) in choices { - goal.add_mul( - merge_and_query_cost(&raqes[raqe_index], &deployments[deployment_index]) - / reference, - assignment, - ); - } - } - - let mut model = variables.minimise(goal).using(default_solver); - for (raqe_index, choices) in assignments.iter().enumerate() { - let assignment_sum: Expression = choices.iter().map(|(_, variable)| *variable).sum(); - model.add_constraint(assignment_sum.eq(1)); - - for &(deployment_index, assignment) in choices { - let raqe = &raqes[raqe_index]; - let deployment = &deployments[deployment_index]; - model.add_constraint((assignment - active[deployment_index]).leq(0)); - // Zero when `w_mem` is 0; such a row can never bind. - let scaled_storage_cost = - storage_cost_for_raqe_and_deployment(raqe, deployment) / reference; - if scaled_storage_cost > 0.0 { - model.add_constraint( - (scaled_storage_cost * assignment - deployment_storage_cost[deployment_index]) - .leq(0), - ); - } - // Each RAQE takes exactly one deployment, so its latency SLA just - // forbids the choices over it. Comparing in f64 here, not in the - // solver, keeps µs latencies clear of its feasibility tolerance. - let latency_ms = analytical_cost_model::query_latency_ms(raqe, deployment, facts); - if raqe.latency_sla_ms.is_some_and(|limit| latency_ms > limit) { - model.add_constraint(Expression::from(assignment).leq(0)); - } - } - } - - for (deployment_index, active_variable) in active.iter().enumerate() { - let assignments_using_deployment: Expression = assignments - .iter() - .flat_map(|choices| choices.iter()) - .filter(|(index, _)| *index == deployment_index) - .map(|(_, assignment)| *assignment) - .sum(); - model.add_constraint((*active_variable - assignments_using_deployment).leq(0)); - } - - let solved = model.solve()?; - let mapping = assignments - .iter() - .map(|choices| { - choices - .iter() - .find_map(|&(deployment_index, assignment)| { - (solved.value(assignment) > 0.5).then_some(deployment_index) - }) - .expect("MILP assigns exactly one deployment to every RAQE") - }) - .collect(); - let plan_cost = score(raqes, deployments, &mapping, facts); - let (deployments, raqes) = plan(raqes, deployments, &mapping); - Ok(MilpSolution { - deployments, - raqes, - plan_cost, - }) -} - -/// Relative slack when comparing a chain with a latency bound, so float error -/// in summing µs-scale work can't rule out a chain equal to the bound. -const LATENCY_SLACK: f64 = 1e-9; - -/// A [`minimize_usage_cost`] solution, with its cost billed by use and its -/// latency. -#[derive(Debug, Clone)] -pub struct UsageSolution { - pub milp: MilpSolution, - /// One candidate index per RAQE. - pub mapping: Mapping, - pub cost: crate::usage::UsageCost, -} - -/// Minimize the cost billed by use, `w_cpu · AUC(CPU) + w_mem · AUC(memory)` -/// (per vCPU, per GiB; `docs/rqe_optimizer_cost_model.md`, "Cost by use and -/// batch latency"). The latency is reported. `latency_bound_ms`, when given, -/// rules out every pair whose chain (`analytical_cost_model::chain_ms`) -/// exceeds it, so sweeping it traces the cost–latency Pareto frontier; it is -/// not an SLA to meet or violate. Per-RAQE latency bounds -/// (`Raqe::latency_sla_ms`) are not used. Every term is linear, so this is -/// exact. -/// -/// `allowed`, when given, lists for each RAQE the candidate indices it may -/// use, e.g. only its own candidates for PerQuery (no sharing). Two RAQEs -/// share a deployment only by choosing the same candidate index, so giving -/// each RAQE its own copies of identical deployments keeps them apart, each -/// paying its own ingest. -#[allow(clippy::too_many_arguments)] -pub fn minimize_usage_cost( - raqes: &[Raqe], - deployments: &[Deployment], - facts: &WorkloadFacts, - w_cpu: f64, - w_mem: f64, - accuracy: &Accuracy, - allowed: Option<&[Vec]>, - latency_bound_ms: Option, -) -> Result { - let secs = |ms: u64| ms as f64 / 1000.0; - let gib = |bytes: f64| bytes / BYTES_PER_GIB; - // Per candidate: ingest vCPUs and bytes (every worker's open windows), - // compaction CPU-seconds per window and the bytes it holds. - let ingest_cpu: Vec = deployments - .iter() - .map(|d| analytical_cost_model::ingest(d, facts).cpu_secs_per_sec) - .collect(); - let ingest_bytes: Vec = deployments - .iter() - .map(|d| { - analytical_cost_model::ingest(d, facts).memory_bytes - * analytical_cost_model::ingest_workers(d, facts) - }) - .collect(); - let compaction: Vec = deployments - .iter() - .map(|d| analytical_cost_model::compaction_secs(d, facts)) - .collect(); - let compaction_bytes: Vec = deployments - .iter() - .map(|d| analytical_cost_model::compaction_bytes(d, facts)) - .collect(); - let eligible: Vec> = raqes - .iter() - .enumerate() - .map(|(i, raqe)| { - eligible_deployments_for(raqe, deployments, facts, accuracy) - .into_iter() - .filter(|d| allowed.is_none_or(|allowed| allowed[i].contains(d))) - .filter(|&d| { - latency_bound_ms.is_none_or(|bound| { - analytical_cost_model::chain_ms(raqe, &deployments[d], facts) - <= bound * (1.0 + LATENCY_SLACK) - }) - }) - .collect() - }) - .collect(); - if eligible.iter().any(Vec::is_empty) { - return Err(ResolutionError::Infeasible); - } - // Per pair: query work (CPU-sec), its memory and the storage it needs. - let work = |i: usize, d: usize| { - analytical_cost_model::query_latency_ms(&raqes[i], &deployments[d], facts) / 1000.0 - }; - let query_bytes = |i: usize, d: usize| { - analytical_cost_model::merge(&raqes[i], &deployments[d], facts).memory_bytes - + analytical_cost_model::query(&raqes[i], &deployments[d], facts).memory_bytes - }; - let storage = |i: usize, d: usize| { - analytical_cost_model::storage_bytes(&raqes[i], &deployments[d], facts) - }; - // Per-deployment and per-pair costs, priced. - let deployment_cost = |d: usize| { - let slide = secs(deployments[d].slide_ms); - w_cpu * (ingest_cpu[d] + compaction[d] / slide) - + w_mem * gib(ingest_bytes[d] + compaction_bytes[d] * compaction[d] / slide) - }; - let pair_cost = |i: usize, d: usize| { - let interval = secs(raqes[i].interval_ms); - w_cpu * work(i, d) / interval + w_mem * gib(query_bytes(i, d) * work(i, d) / interval) - }; - - // Scale: each RAQE alone on its cheapest pair, so HiGHS sees magnitudes - // near 1. - let reference: f64 = (0..raqes.len()) - .map(|i| { - eligible[i] - .iter() - .map(|&d| deployment_cost(d) + pair_cost(i, d) + w_mem * gib(storage(i, d))) - .fold(f64::INFINITY, f64::min) - }) - .sum(); - let reference = if reference.is_normal() { - reference - } else { - 1.0 - }; - - let mut variables = ProblemVariables::new(); - let active: Vec = deployments - .iter() - .map(|_| variables.add(variable().binary())) - .collect(); - let assignments: Vec> = eligible - .iter() - .map(|choices| { - choices - .iter() - .map(|&d| (d, variables.add(variable().binary()))) - .collect() - }) - .collect(); - // Priced storage per deployment, scaled: the max over the RAQEs it serves. - let stored: Vec = deployments - .iter() - .map(|_| variables.add(variable().min(0))) - .collect(); - - let mut goal: Expression = stored.iter().sum(); - for (d, &u) in active.iter().enumerate() { - goal.add_mul(deployment_cost(d) / reference, u); - } - for (i, choices) in assignments.iter().enumerate() { - for &(d, z) in choices { - goal.add_mul(pair_cost(i, d) / reference, z); - } - } - - let mut model = variables.minimise(goal).using(default_solver); - for (i, choices) in assignments.iter().enumerate() { - let sum: Expression = choices.iter().map(|&(_, z)| z).sum(); - model.add_constraint(sum.eq(1)); - for &(d, z) in choices { - model.add_constraint((z - active[d]).leq(0)); - let needed = w_mem * gib(storage(i, d)) / reference; - if needed > 0.0 { - model.add_constraint((needed * z - stored[d]).leq(0)); - } - } - } - for (d, &u) in active.iter().enumerate() { - let used: Expression = assignments - .iter() - .flat_map(|choices| choices.iter()) - .filter(|(index, _)| *index == d) - .map(|&(_, z)| z) - .sum(); - model.add_constraint((u - used).leq(0)); - } - - let solved = model.solve()?; - let mapping: Mapping = assignments - .iter() - .map(|choices| { - choices - .iter() - .find_map(|&(d, z)| (solved.value(z) > 0.5).then_some(d)) - .expect("the MILP assigns every RAQE one deployment") - }) - .collect(); - let cost = crate::usage::usage_cost( - &crate::usage::PlanLoad::new(raqes, deployments, &mapping, facts), - w_cpu, - w_mem, - ); - let plan_cost = score(raqes, deployments, &mapping, facts); - let (planned, planned_raqes) = plan(raqes, deployments, &mapping); - Ok(UsageSolution { - milp: MilpSolution { - deployments: planned, - raqes: planned_raqes, - plan_cost, - }, - mapping, - cost, - }) -} - -/// Keeps the deployments `mapping` uses, renumbered, with their instance -/// counts. Every pair in `mapping` is eligible, so the counts exist. -fn plan( - raqes: &[Raqe], - candidates: &[Deployment], - mapping: &Mapping, -) -> (Vec, Vec) { - let mut planned_index: Vec> = vec![None; candidates.len()]; - let mut planned_deployments: Vec = Vec::new(); - let mut planned_raqes = Vec::with_capacity(raqes.len()); - // `retained_instance_count` holds the closed windows until the open ones - // are added below, once per deployment. - for (raqe, &candidate_index) in raqes.iter().zip(mapping) { - let deployment = &candidates[candidate_index]; - let index = *planned_index[candidate_index].get_or_insert_with(|| { - planned_deployments.push(PlannedDeployment { - deployment: deployment.clone(), - retained_instance_count: 0, - }); - planned_deployments.len() - 1 - }); - let closed = deployment - .closed_instance_count(raqe.lookback_ms) - .expect("eligible deployments have whole window counts"); - let planned = &mut planned_deployments[index]; - planned.retained_instance_count = planned.retained_instance_count.max(closed); - planned_raqes.push(PlannedRaqe { - deployment: index, - merged_instance_count: deployment - .query_instance_count(raqe.lookback_ms) - .expect("eligible deployments divide the lookback"), - }); - } - for planned in &mut planned_deployments { - planned.retained_instance_count += planned - .deployment - .active_instance_count() - .expect("eligible deployments have whole window counts"); - } - (planned_deployments, planned_raqes) -} - -#[cfg(test)] -mod tests { - use super::*; - use crate::candidates::build_all_candidates; - use crate::enumerate::brute_force; - use crate::test_support::{self, facts}; - use crate::{table_accuracy, AtomicCostEntry, Capability}; - - /// Window and slide 60 s, merge 1 CPU-sec. - fn deployment(insert_cpu_secs: f64, memory: f64, query_cpu_secs: f64) -> Deployment { - test_support::deployment(memory, insert_cpu_secs, 1.0, query_cpu_secs, 60_000, 60_000) - } - - fn raqe(id: &str, lookback_ms: u64) -> Raqe { - Raqe { - id: id.into(), - ..test_support::raqe(lookback_ms, 60_000) - } - } - - /// The candidate index serving each RAQE. - fn chosen(milp: &MilpSolution, candidates: &[Deployment]) -> Mapping { - milp.raqes - .iter() - .map(|planned| { - let deployment = &milp.deployments[planned.deployment].deployment; - candidates - .iter() - .position(|candidate| candidate == deployment) - .expect("planned deployments come from the candidates") - }) - .collect() - } - - fn weights(w_cpu: f64, w_mem: f64) -> Objective { - Objective::AUCCost { w_cpu, w_mem } - } - - /// The MILP's plan scores as well as the best of every mapping. - fn assert_matches_brute_force( - raqes: &[Raqe], - deployments: &[Deployment], - objective: Objective, - ) { - let facts = facts(1, 1); - let best = brute_force(raqes, deployments, &facts, &table_accuracy) - .iter() - .map(|mapping| objective.value(&score(raqes, deployments, mapping, &facts))) - .min_by(f64::total_cmp) - .expect("test workload is servable"); - let milp = minimize(raqes, deployments, &facts, objective, &table_accuracy) - .expect("feasible MILP"); - let got = objective.value(&milp.plan_cost); - assert!( - (got - best).abs() <= 1e-9 * best, - "{objective:?}: {got} vs {best}" - ); - } - - /// Capabilities the plan serves from one deployment, solving `a` and `b` - /// queries at the same cadence from one `sketch` cost row. - fn shared_deployments(a: Capability, b: Capability, sketch: &str) -> usize { - let raqes = [a, b].map(|capability| Raqe { - capability, - ..raqe("r", 60_000) - }); - let costs = [AtomicCostEntry { - sketch: sketch.into(), - accuracy_metric: crate::test_support::metric_of(sketch), - ..deployment(1.0, 1.0, 1.0).config - }]; - let candidates = build_all_candidates(&raqes, &costs, &facts(1, 1), false, &table_accuracy); - let milp = minimize( - &raqes, - &candidates, - &facts(1, 1), - Objective::default(), - &table_accuracy, - ) - .expect("feasible MILP"); - raqes.len() - milp.deployments.len() - } - - #[test] - fn plan_never_serves_paired_capabilities_from_one_deployment() { - let pairs = [ - (Capability::Sum, Capability::Count, "exact-sum"), - ( - Capability::TopKByValue, - Capability::TopKByCount, - "cms-heap-topk-fastpath-vector2d", - ), - ]; - for (a, b, sketch) in pairs { - // Sharing halves ingest, so the MILP shares whenever it may. - assert_eq!(shared_deployments(a, a, sketch), 1, "{a:?} twice"); - assert_eq!(shared_deployments(a, b, sketch), 0, "{a:?} and {b:?}"); - } - } - - #[test] - fn minimizes_cpu_and_respects_a_latency_sla() { - let raqes = vec![raqe("r", 60_000)]; - // CPU 1 + 3/60 with latency 3, or 2 + 1/60 with latency 1. - let deployments = vec![deployment(1.0, 20.0, 3.0), deployment(2.0, 10.0, 1.0)]; - let solve = |raqes: &[Raqe]| { - let milp = minimize( - raqes, - &deployments, - &facts(1, 1), - Objective::default(), - &table_accuracy, - ) - .expect("feasible MILP"); - chosen(&milp, &deployments) - }; - - assert_eq!(solve(&raqes), vec![0]); - let bounded = Raqe { - latency_sla_ms: Some(2_000.0), - ..raqes[0].clone() - }; - assert_eq!(solve(&[bounded]), vec![1]); - } - - /// Two RAQEs, 100 groups, and candidates that trade ingest for queries. - fn usage_workload() -> (Vec, Vec, WorkloadFacts) { - let raqes = vec![raqe("frequent", 60_000), raqe("long", 600_000)]; - // (memory, insert, merge, query, window, slide): cheap ingest with - // slow queries; costlier ingest with fast ones; a 10-minute window - // that answers `long` without merging but keeps 10 windows open. - let deployments = vec![ - test_support::deployment(0.5 * BYTES_PER_GIB, 1e-3, 1e-4, 4e-3, 60_000, 60_000), - test_support::deployment(0.1 * BYTES_PER_GIB, 3e-3, 1e-5, 1e-5, 60_000, 60_000), - test_support::deployment(2.0 * BYTES_PER_GIB, 2e-4, 1e-5, 1e-5, 600_000, 60_000), - ]; - (raqes, deployments, facts(100, 100)) - } - - #[test] - fn usage_cost_matches_brute_force() { - use crate::usage::{usage_cost, PlanLoad}; - let (raqes, deployments, facts) = usage_workload(); - for (w_cpu, w_mem) in [(1.0, 0.0), (0.0, 1.0), (1.0, 4.0)] { - let best = brute_force(&raqes, &deployments, &facts, &table_accuracy) - .iter() - .map(|mapping| { - usage_cost( - &PlanLoad::new(&raqes, &deployments, mapping, &facts), - w_cpu, - w_mem, - ) - .value - }) - .min_by(f64::total_cmp) - .expect("servable"); - let got = minimize_usage_cost( - &raqes, - &deployments, - &facts, - w_cpu, - w_mem, - &table_accuracy, - None, - None, - ) - .expect("feasible"); - assert!( - (got.cost.value - best).abs() <= 1e-9 * best, - "({w_cpu}, {w_mem}): {} vs {best}", - got.cost.value - ); - } - } - - #[test] - fn a_latency_bound_gives_the_cheapest_plan_that_fast_by_brute_force() { - use crate::usage::{usage_cost, PlanLoad}; - let (raqes, deployments, facts) = usage_workload(); - let price = |mapping: &Mapping| { - usage_cost( - &PlanLoad::new(&raqes, &deployments, mapping, &facts), - 1.0, - 0.0, - ) - }; - let mut previous = (0.0, f64::INFINITY); - // Looser bounds never cost more and never give a faster plan. - for bound in [300.0, 1e3, 1e4, 1e6] { - let best = brute_force(&raqes, &deployments, &facts, &table_accuracy) - .iter() - .map(price) - .filter(|cost| cost.latency_ms <= bound) - .map(|cost| cost.value) - .min_by(f64::total_cmp) - .expect("servable within the bound"); - let got = minimize_usage_cost( - &raqes, - &deployments, - &facts, - 1.0, - 0.0, - &table_accuracy, - None, - Some(bound), - ) - .expect("feasible"); - assert!((got.cost.value - best).abs() <= 1e-9 * best, "{bound}"); - assert!(got.cost.latency_ms <= bound); - assert!(got.cost.value <= previous.1 * (1.0 + 1e-12)); - previous = (got.cost.latency_ms, got.cost.value); - } - } - - #[test] - fn allowed_candidates_keep_identical_deployments_apart() { - // Two RAQEs at one cadence, each with its own copy of one deployment. - let raqes = vec![raqe("a", 60_000), raqe("b", 60_000)]; - let one = test_support::deployment(BYTES_PER_GIB, 1e-3, 1e-5, 1e-5, 60_000, 60_000); - let deployments = vec![one.clone(), one]; - let facts = facts(100, 100); - let solve = |allowed: Option<&[Vec]>| { - minimize_usage_cost( - &raqes, - &deployments, - &facts, - 1.0, - 0.0, - &table_accuracy, - allowed, - None, - ) - .expect("feasible") - }; - // Free to choose: both use one copy and ingest once. - let shared = solve(None); - assert_eq!(shared.mapping[0], shared.mapping[1]); - // Each on its own copy: two deployments, ingest paid twice. - let own = solve(Some(&[vec![0], vec![1]])); - assert_eq!(own.mapping, vec![0, 1]); - let ingest = analytical_cost_model::ingest(&deployments[0], &facts).cpu_secs_per_sec; - assert!((own.cost.cpu - shared.cost.cpu - ingest).abs() < 1e-12 * own.cost.cpu); - } - - #[test] - fn matches_brute_force_with_shared_deployment_costs() { - let raqes = vec![raqe("frequent", 60_000), raqe("long", 600_000)]; - let deployments = vec![ - deployment(1.0, 0.5 * BYTES_PER_GIB, 400.0), - deployment(3.0, 0.1 * BYTES_PER_GIB, 1.0), - deployment(0.2, 2.0 * BYTES_PER_GIB, 1.0), - ]; - for objective in [weights(1.0, 0.0), weights(0.0, 1.0), weights(1.0, 4.0)] { - assert_matches_brute_force(&raqes, &deployments, objective); - } - } - - #[test] - fn memory_weight_trades_cpu_for_memory() { - let raqes = vec![raqe("r", 60_000)]; - // CPU-heavy and small, or CPU-light and twice the size. - let deployments = vec![ - deployment(1.0, 1.0 * BYTES_PER_GIB, 0.0), - deployment(0.05, 2.0 * BYTES_PER_GIB, 0.0), - ]; - let solve = |objective| { - let milp = minimize( - &raqes, - &deployments, - &facts(1, 1), - objective, - &table_accuracy, - ) - .expect("feasible MILP"); - chosen(&milp, &deployments) - }; - - assert_eq!(solve(weights(1.0, 0.0)), vec![1]); - assert_eq!(solve(weights(0.0, 1.0)), vec![0]); - } - - #[test] - fn tiny_magnitudes_match_brute_force_and_respect_latency_slas() { - // Real plans cost ~1e-6 CPU-sec/sec with µs latencies: below HiGHS's - // absolute gap and feasibility tolerances unless the model is scaled. - let raqes = vec![raqe("frequent", 60_000), raqe("long", 600_000)]; - let tiny = 1e-9; - let deployments = vec![ - deployment(1.0 * tiny, 0.5 * tiny * BYTES_PER_GIB, 400.0 * tiny), - deployment(3.0 * tiny, 0.1 * tiny * BYTES_PER_GIB, 1.0 * tiny), - deployment(0.2 * tiny, 2.0 * tiny * BYTES_PER_GIB, 1.0 * tiny), - ] - .into_iter() - .map(|mut candidate| { - candidate.config.merge_cpu_secs = tiny; - candidate - }) - .collect::>(); - for objective in [weights(1.0, 0.0), weights(1.0, 4.0)] { - assert_matches_brute_force(&raqes, &deployments, objective); - } - - // The cheap deployment's latency is 1.5e-7 s, over a 1e-7 s SLA by - // less than HiGHS's absolute feasibility tolerance. - let raqes = vec![Raqe { - latency_sla_ms: Some(1e-4), - ..raqe("r", 60_000) - }]; - let deployments = vec![ - deployment(tiny, tiny, 1.5e-7), - deployment(10.0 * tiny, tiny, 0.5e-7), - ]; - let milp = minimize( - &raqes, - &deployments, - &facts(1, 1), - Objective::default(), - &table_accuracy, - ) - .expect("feasible MILP"); - assert_eq!(chosen(&milp, &deployments), vec![1]); - assert!(milp.plan_cost.query_latency_ms[0] <= 1e-4); - } - - #[test] - fn plan_lists_active_deployments_with_their_instance_counts() { - // Window 60 s, slide 20 s; the unused candidate 0 is CPU-heavy. - let shared = test_support::deployment(1.0, 1.0, 1.0, 1.0, 60_000, 20_000); - let unused = test_support::deployment(1.0, 1e6, 1.0, 1.0, 60_000, 20_000); - let raqes = vec![raqe("short", 60_000), raqe("long", 600_000)]; - let milp = minimize( - &raqes, - &[unused, shared.clone()], - &facts(1, 1), - Objective::default(), - &table_accuracy, - ) - .expect("feasible MILP"); - - assert_eq!(milp.deployments.len(), 1); - assert_eq!(milp.deployments[0].deployment, shared); - // 60/20 open + (600 − 60)/20 + 1 closed, for the longer lookback. - assert_eq!(milp.deployments[0].retained_instance_count, 3 + 28); - let merged: Vec<_> = milp - .raqes - .iter() - .map(|planned| (planned.deployment, planned.merged_instance_count)) - .collect(); - assert_eq!(merged, vec![(0, 1), (0, 10)]); - } -} diff --git a/rqe-optimizer/src/pareto.rs b/rqe-optimizer/src/pareto.rs deleted file mode 100644 index a3694c7d..00000000 --- a/rqe-optimizer/src/pareto.rs +++ /dev/null @@ -1,103 +0,0 @@ -//! Non-dominated filtering for `(CPU, memory, per-RAQE latency)`. - -use crate::{analytical_cost_model::PlanCost, Mapping}; - -fn dominates(a: &[f64], b: &[f64]) -> bool { - let mut strict = false; - for (left, right) in a.iter().zip(b) { - if left > right { - return false; - } - if left < right { - strict = true; - } - } - strict -} - -fn pareto_vector(plan_cost: &PlanCost) -> Vec { - let mut vector = vec![plan_cost.cpu_secs_per_sec(), plan_cost.memory_bytes()]; - vector.extend(&plan_cost.query_latency_ms); - vector -} - -/// A mapping retained on the current incremental Pareto frontier. -#[derive(Debug, Clone)] -pub struct ParetoEntry { - pub mapping: Mapping, - pub plan_cost: PlanCost, -} - -/// Retains only non-dominated mappings while a streaming enumerator visits -/// them. Peak memory is proportional to the frontier, not all mappings. -#[derive(Debug, Default)] -pub struct ParetoFront { - entries: Vec, -} - -impl ParetoFront { - pub fn new() -> Self { - Self::default() - } - - /// Consider one scored mapping. Returns true when it remains on the - /// frontier. Equal objective vectors are both retained. - pub fn consider(&mut self, mapping: &Mapping, plan_cost: PlanCost) -> bool { - let candidate = pareto_vector(&plan_cost); - if self - .entries - .iter() - .any(|entry| dominates(&pareto_vector(&entry.plan_cost), &candidate)) - { - return false; - } - self.entries - .retain(|entry| !dominates(&candidate, &pareto_vector(&entry.plan_cost))); - self.entries.push(ParetoEntry { - mapping: mapping.clone(), - plan_cost, - }); - true - } - - pub fn entries(&self) -> &[ParetoEntry] { - &self.entries - } -} - -pub fn pareto_front(plan_costs: &[PlanCost]) -> Vec { - let vectors: Vec> = plan_costs.iter().map(pareto_vector).collect(); - (0..plan_costs.len()) - .filter(|&i| !(0..plan_costs.len()).any(|j| j != i && dominates(&vectors[j], &vectors[i]))) - .collect() -} - -#[cfg(test)] -mod tests { - use super::*; - use crate::analytical_cost_model::PhaseCost; - - fn plan_cost(memory: f64, cpu: f64, latency: f64) -> PlanCost { - let phase = PhaseCost { - cpu_secs_per_sec: cpu, - memory_bytes: memory, - }; - PlanCost { - ingest: phase, - merge: PhaseCost::default(), - query: PhaseCost::default(), - storage: PhaseCost::default(), - query_latency_ms: vec![latency], - } - } - - #[test] - fn incremental_front_discards_dominated_mappings() { - let mut front = ParetoFront::new(); - assert!(front.consider(&vec![0], plan_cost(10.0, 10.0, 10.0))); - assert!(!front.consider(&vec![1], plan_cost(20.0, 20.0, 20.0))); - assert!(front.consider(&vec![2], plan_cost(5.0, 5.0, 5.0))); - assert_eq!(front.entries().len(), 1); - assert_eq!(front.entries()[0].mapping, vec![2]); - } -} diff --git a/rqe-optimizer/src/saturation.rs b/rqe-optimizer/src/saturation.rs deleted file mode 100644 index bacc7363..00000000 --- a/rqe-optimizer/src/saturation.rs +++ /dev/null @@ -1,2102 +0,0 @@ -//! Sketch accuracy read from the saturation study's error-vs-N curves -//! (`scripts/study_saturation.py`, #130, #140) at the number of items a -//! deployment's answer covers. Design decisions: #156. -//! -//! The curve is read at the number of items one group receives over the -//! RAQE's whole lookback: series per group times scrapes per lookback. For -//! sketches that merge exactly, that holds whether the answer is one sketch -//! or a merge of many smaller-window sketches. KLL and top-k merge lossily -//! (#131): an answer merged from `L / x` windows reads the curve of the sketch -//! merged from that many shards (#158). -//! -//! The cost table's sketch accuracies are not read: each is one point on a -//! curve, at the row's `measured_at`, and [`SaturationCurves::check_cost_table`] -//! checks that the two agree there (#171). Both come from one study run -//! (`study_saturation.py --phase optimizer-cost`, #174). -//! -//! Hydra reads none of these curves: its accuracy is measured per dataset -//! and grouping (`hydra_saturation.csv`, `docs/rqe_optimizer_hydra.md` -//! §2.4), see [`SaturationCurves::accuracy`]. - -use std::collections::{BTreeMap, BTreeSet}; -use std::io; -use std::path::Path; - -use crate::autosketch::window_adapter; -use crate::theory; -use crate::{ - accuracy_key, family_properties, has_heap, heap_capacity, params_topk_k, table_accuracy, - AccuracyDirection, AtomicCostEntry, Capability, Deployment, LabelSet, MetricFacts, Raqe, - WorkloadFacts, TOPK_K, -}; -use aqpbm_core::MeasuredShape; - -/// The data parameters a saturation curve is keyed by, fitted from the -/// dataset per (metric, grouping) as the worst case over windows and groups. -#[derive(Debug, Clone, Copy, PartialEq)] -pub struct DataShape { - /// Zipf skew θ of the keys inserted into one group's sketch (CMS, top-k, - /// HLL). Describes whichever insertion mode the engine uses: once per - /// sample, or weighted by sample value. - pub zipf_s: f64, - /// `K`: distinct keys per group per window. - pub distinct_keys: f64, - /// Pareto tail index `a` of the values (KLL, DDSketch). - pub tail_index: f64, -} - -/// The two runs' output directories under `--saturation-dir`. A point in -/// both is taken whole from the later one. -const RUN_DIRS: [&str; 2] = ["out_grid_1e7_cost", "out_1e9"]; - -/// The cost table under the same directory, written by -/// `study_saturation.py --phase optimizer-cost --out DIR/optimizer_cost`. -pub const COST_TABLE: &str = "optimizer_cost/rqe_atomic_costs.json"; - -/// Hydra's measurements under the same directory, written by -/// `study_saturation.py --phase hydra` on datasets shaped like the -/// workload's metrics. Optional: without it no Hydra deployment has an -/// accuracy. -pub const HYDRA_SATURATION: &str = "hydra_saturation.csv"; - -/// `hydra_saturation.csv`'s error columns, by the share of records a group -/// must hold to be covered (the largest group always is): every group, then -/// the two measured thresholds. -const HYDRA_COVERAGE: [(f64, &str); 3] = [ - (0.0, "err_max"), - (0.01, "err_max_cov_0.01"), - (0.05, "err_max_cov_0.05"), -]; - -/// Hydra measurements of one (variant, config, dataset, grouping), by merge -/// shards: each [`HYDRA_COVERAGE`] column's worst over the records and seeds -/// that filled it, `None` where every row left it empty. -type HydraErrors = BTreeMap; 3]>; - -/// One (sketch, config, data shape) point of the study. -#[derive(Debug, Clone, PartialEq)] -struct GridPoint { - params: BTreeMap, - shape: MeasuredShape, - error_metric: String, - /// `None`: still changing at the largest measured N. - n_sat: Option, - /// `(n, seed_mean_error, seed_se)`, ascending in `n`. - curve: Vec<(f64, f64, f64)>, - /// The same, for the sketch merged from `m` shards of the stream, keyed - /// by `m > 1` (`saturation_merge_curve.csv`, #131, #158). - merged: BTreeMap>, -} - -/// How far a cost-table value may sit outside the curve's range at its N: -/// this many of the curve's seed standard errors, plus [`AGREEMENT_RELATIVE`] -/// of the curve's value, because the table row is one seed of its own. -const AGREEMENT_SES: f64 = 3.0; -const AGREEMENT_RELATIVE: f64 = 0.05; - -impl GridPoint { - /// Zipf θ, or the Pareto tail index for quantile sketches. - fn shape_param(&self) -> f64 { - match self.shape { - MeasuredShape::Zipf { skew, .. } => skew, - MeasuredShape::Pareto { tail_index } => tail_index, - } - } - - /// `K`; `None` for quantile sketches. - fn distinct_keys(&self) -> Option { - match self.shape { - MeasuredShape::Zipf { keys, .. } => Some(keys), - MeasuredShape::Pareto { .. } => None, - } - } - - /// Whether a value measured at `n` agrees with the curve: within - /// tolerance of the checkpoint at `n`, or of the range between the two - /// either side. `None` where the curve can't say (as in - /// [`GridPoint::error_at`]). - fn agrees_at(&self, n: f64, value: f64) -> Option { - let &(first_n, ..) = self.curve.first()?; - let &last = self.curve.last()?; - let neighbours = if n < first_n { - return None; - } else if n >= last.0 { - // The plateau, as in `curve_error_at`. - self.n_sat.filter(|&sat| sat <= last.0)?; - [last, last] - } else { - let above = self - .curve - .partition_point(|&(checkpoint, ..)| checkpoint < n); - if self.curve[above].0 == n { - [self.curve[above]; 2] - } else { - [self.curve[above - 1], self.curve[above]] - } - }; - let [(_, a, a_se), (_, b, b_se)] = neighbours; - let (low, high) = (a.min(b), a.max(b)); - let tolerance = AGREEMENT_SES * a_se.max(b_se) + AGREEMENT_RELATIVE * high.abs(); - Some(value >= low - tolerance && value <= high + tolerance) - } - - /// Q6 of #156: between checkpoints, the worse neighbour; below the first, - /// unmeasured; past the last, the plateau only if the point saturated. - fn error_at(&self, n: f64, direction: AccuracyDirection) -> Option { - curve_error_at(&self.curve, self.n_sat, n, direction) - } - - /// The curves an answer merged from `merges` instances reads: the plain - /// curve alone, or the measured shard counts either side of `merges` - /// (one is the plain curve). Past the largest count, only the largest. - fn bracketing_curves(&self, merges: u64) -> Vec<&[(f64, f64, f64)]> { - if merges <= 1 { - return vec![&self.curve]; - } - let below = self - .merged - .range(..=merges) - .next_back() - .map_or(&self.curve[..], |(_, c)| &c[..]); - match self.merged.range(merges..).next() { - Some((_, above)) => vec![below, above], - None => self - .merged - .values() - .next_back() - .map_or(vec![below], |c| vec![&c[..]]), - } - } - - /// The smallest N the study measured the curves an answer merged from - /// `merges` instances reads. - fn first_measured_n(&self, merges: u64) -> Option { - self.bracketing_curves(merges) - .iter() - .map(|c| c.first().map(|p| p.0)) - .try_fold(0.0, |max: f64, first| first.map(|f| max.max(f))) - } - - /// The worst last error measured on the curves an answer merged from - /// `merges` instances reads: the floor a fallback extending them can't - /// beat. - fn last_measured_error(&self, merges: u64, direction: AccuracyDirection) -> Option { - self.bracketing_curves(merges) - .iter() - .filter_map(|c| c.last().map(|p| p.1)) - .reduce(|a, b| worse(a, b, direction)) - } - - /// The error at `n` items of the sketch merged from `merges` instances: - /// the worse of the measured shard counts either side of `merges` (one - /// is the plain sketch). `None` past the largest measured count, past the - /// merge curve's last N, or with no merge curve: what the study didn't - /// measure is unknown. - fn merged_error_at(&self, merges: u64, n: f64, direction: AccuracyDirection) -> Option { - if merges <= 1 { - return self.error_at(n, direction); - } - let below = self.merged.range(..=merges).next_back().map(|(&m, _)| m); - let (&above, _) = self.merged.range(merges..).next()?; - // Past a merge curve's last N it has no plateau: nothing shows the - // merged sketch stopped changing there. - let at = |m: u64| match m { - 1 => self.error_at(n, direction), - m => curve_error_at(&self.merged[&m], None, n, direction), - }; - let below = at(below.unwrap_or(1))?; - Some(worse(below, at(above)?, direction)) - } -} - -/// [`GridPoint::error_at`] on any of the point's curves. -fn curve_error_at( - curve: &[(f64, f64, f64)], - n_sat: Option, - n: f64, - direction: AccuracyDirection, -) -> Option { - let &(first_n, ..) = curve.first()?; - let &(last_n, last_error, _) = curve.last()?; - if n < first_n { - return None; - } - if n == last_n { - return Some(last_error); - } - // Past the last checkpoint, the plateau if the curve saturated by then. - if n > last_n { - return n_sat.filter(|&sat| sat <= last_n).map(|_| last_error); - } - let above = curve.partition_point(|&(checkpoint, ..)| checkpoint < n); - let (checkpoint, error, _) = curve[above]; - if checkpoint == n { - Some(error) - } else { - Some(worse(error, curve[above - 1].1, direction)) - } -} - -/// KLL, the heap top-k sketches and UnivMon lose accuracy when merged -/// (#131): a merged answer reads their merge curve. UnivMon's counters add -/// exactly, but each level's heavy-hitter heap is rebuilt from the union of -/// the two heaps, so a key heavy only in the union is lost. The other -/// sketches merge exactly. Hydra's cells merge as their sketch does. -fn merges_lossily(sketch: &str) -> bool { - matches!( - sketch, - "kll-percall" - | "univmon-cardinality" - | "hydra-kll" - | "hydra-univmon-cardinality" - | "cms-heap-topk-fastpath-vector2d" - | "countsketch-heap-topk-fastpath-vector2d" - | "univmon-topk" - ) -} - -/// Whether some capability plans `sketch`. -fn is_candidate(sketch: &str) -> bool { - Capability::ALL - .iter() - .any(|capability| capability.families().contains(&sketch)) -} - -fn worse(a: f64, b: f64, direction: AccuracyDirection) -> f64 { - match direction { - AccuracyDirection::LowerIsBetter => a.max(b), - AccuracyDirection::HigherIsBetter => a.min(b), - } -} - -/// Where an accuracy came from. -#[derive(Debug, Clone, Copy, PartialEq, Eq)] -pub enum AccuracySource { - /// The cost table (exact accumulators) or the study's curves. - Measured, - /// The algorithm's published guarantee, where the study measured nothing. - Theory, -} - -/// What [`SaturationCurves::check_cost_table`] found, one line per row. -#[derive(Debug, Default, PartialEq)] -pub struct CostTableCheck { - /// Rows whose accuracy disagrees with the curve at their `measured_at`, - /// whose config the grid lacks, or that lack the curve's metric. - pub mismatched: Vec, - /// Rows with no curve at their measured shape or N, such as KLL: the - /// table measures Zipf ranks, the study Pareto values. - pub unchecked: Vec, -} - -#[derive(Debug, Clone, Default)] -pub struct SaturationCurves { - points_by_sketch: BTreeMap>, - /// [`HYDRA_SATURATION`], keyed by variant, config params (as - /// [`config_params`] prints them), dataset and grouping. - hydra: BTreeMap<(String, String, String, LabelSet), HydraErrors>, -} - -impl SaturationCurves { - /// Reads `saturation.csv` (for the saturation point), `saturation_curve.csv` - /// and, where present, `saturation_merge_curve.csv` from each of - /// [`RUN_DIRS`] under `dir`. Fails on a candidate family's curve - /// for a metric other than its [`accuracy_key`]: that study is stale. - pub fn load(dir: &Path) -> io::Result { - type PointKey = (String, String, String, String, String); - let key = |row: &CsvRow| -> PointKey { - let field = |name: &str| row[name].clone(); - ( - field("sketch"), - field("config"), - field("dist"), - field("param"), - field("cardinality"), - ) - }; - let mut points: BTreeMap = BTreeMap::new(); - for run in RUN_DIRS { - let run_dir = dir.join(run); - let mut run_points = BTreeMap::new(); - let columns = [ - "sketch", - "config", - "dist", - "param", - "cardinality", - "n_sat", - "error_metric", - ]; - for row in read_csv(&run_dir.join("saturation.csv"), &columns)? { - let point = GridPoint { - params: parse_config(&row["config"])?, - shape: parse_shape(&row)?, - error_metric: row["error_metric"].clone(), - n_sat: match row["n_sat"].as_str() { - "not_saturated" => None, - n => Some(parse_number(n)?), - }, - curve: Vec::new(), - merged: BTreeMap::new(), - }; - let sketch = row["sketch"].as_str(); - if is_candidate(sketch) && point.error_metric != accuracy_key(sketch).0 { - return Err(invalid(format!( - "{run}: {sketch} curve measured {}, but the optimizer reads {}; \ - rerun the study", - point.error_metric, - accuracy_key(sketch).0 - ))); - } - run_points.insert(key(&row), (row["sketch"].clone(), point)); - } - let columns = [ - "sketch", - "config", - "dist", - "param", - "cardinality", - "n", - "seed_mean_error", - "seed_se", - ]; - for row in read_csv(&run_dir.join("saturation_curve.csv"), &columns)? { - let Some((_, point)) = run_points.get_mut(&key(&row)) else { - return Err(invalid(format!( - "{run}: curve row for a point not in saturation.csv: {row:?}" - ))); - }; - point.curve.push(( - parse_number(&row["n"])?, - parse_number(&row["seed_mean_error"])?, - parse_number(&row["seed_se"])?, - )); - } - let merge_path = run_dir.join("saturation_merge_curve.csv"); - if merge_path.exists() { - let columns = [ - "sketch", - "config", - "dist", - "param", - "cardinality", - "shards", - "n", - "seed_mean_error", - "seed_se", - ]; - for row in read_csv(&merge_path, &columns)? { - let Some((_, point)) = run_points.get_mut(&key(&row)) else { - return Err(invalid(format!( - "{run}: merge curve row for a point not in saturation.csv: {row:?}" - ))); - }; - let shards: u64 = row["shards"] - .parse() - .map_err(|_| invalid(format!("{run}: shards is not a count: {row:?}")))?; - if shards == 0 { - return Err(invalid(format!("{run}: zero shards: {row:?}"))); - } - if shards > 1 { - point.merged.entry(shards).or_default().push(( - parse_number(&row["n"])?, - parse_number(&row["seed_mean_error"])?, - parse_number(&row["seed_se"])?, - )); - } - } - } - // A later run's point wins, but keeps an earlier run's merge - // curves at shard counts it didn't measure (the 1e9 run measures - // none). - for (key, (sketch, mut point)) in run_points { - if let Some((_, earlier)) = points.remove(&key) { - for (shards, curve) in earlier.merged { - point.merged.entry(shards).or_insert(curve); - } - } - points.insert(key, (sketch, point)); - } - } - // A lossy candidate sketch with no merge curve at all (a study run - // without --merge-shards-list for it) would leave every merged - // deployment of it silently without accuracy; refuse it. A single - // point without one (only the 1e9 run has it) just has no merged - // accuracy. - let mut lossy: BTreeMap<&str, bool> = BTreeMap::new(); - for (sketch, point) in points.values() { - // Heap top-k merges as one sketch when sized m · k, so only the - // non-heap lossy sketches (KLL, univmon-cardinality, univmon-topk) - // need the curves. - if is_candidate(sketch) && merges_lossily(sketch) && !has_heap(sketch) { - *lossy.entry(sketch).or_default() |= !point.merged.is_empty(); - } - } - if let Some((sketch, _)) = lossy.iter().find(|(_, &merged)| !merged) { - return Err(invalid(format!( - "no merge curves for {sketch}; rerun the study with --merge-shards-list" - ))); - } - let mut points_by_sketch: BTreeMap> = BTreeMap::new(); - for (sketch, mut point) in points.into_values() { - point.curve.sort_by(|a, b| a.0.total_cmp(&b.0)); - for curve in point.merged.values_mut() { - curve.sort_by(|a, b| a.0.total_cmp(&b.0)); - } - points_by_sketch.entry(sketch).or_default().push(point); - } - // The optimizer brackets a shape between grid points, so each - // config's Zipf grid must be a full cross of its θ and K values: a - // hole would quietly leave every shape around it without accuracy. - for (sketch, points) in &points_by_sketch { - let mut by_config: BTreeMap> = BTreeMap::new(); - for point in points { - if let MeasuredShape::Zipf { skew, keys } = point.shape { - by_config - .entry(format!("{:?}", point.params)) - .or_default() - .insert((skew.to_bits(), keys.to_bits())); - } - } - for (config, shapes) in by_config { - let thetas: BTreeSet = shapes.iter().map(|&(t, _)| t).collect(); - let keys: BTreeSet = shapes.iter().map(|&(_, k)| k).collect(); - let missing: Vec<(f64, f64)> = thetas - .iter() - .flat_map(|&t| keys.iter().map(move |&k| (t, k))) - .filter(|shape| !shapes.contains(shape)) - .map(|(t, k)| (f64::from_bits(t), f64::from_bits(k))) - .collect(); - if !missing.is_empty() { - return Err(invalid(format!( - "{sketch} {config}: the grid isn't a full cross of θ and K; \ - missing (θ, K) {missing:?}. Rerun the study's accuracy grid for them" - ))); - } - } - } - let hydra_path = dir.join(HYDRA_SATURATION); - let hydra = if hydra_path.exists() { - load_hydra(&hydra_path)? - } else { - BTreeMap::new() - }; - Ok(Self { - points_by_sketch, - hydra, - }) - } - - /// Hydra's accuracy for `raqe` on `deployment`: the measurement at the - /// metric's [`MetricFacts::hydra_dataset`], the deployment's config and the - /// RAQE's grouping, in the column of the RAQE's - /// [`Raqe::accuracy_covers_share`] (a share between the measured ones - /// reads the next stricter column), worst over records and seeds. A - /// lossy merge of `L / x` windows reads the worse of the measured shard - /// counts either side; past the largest, or with no measurement, it is - /// unknown. - // Assumes error depends on group shares, not on N, at fixed shares; the - // study's N sweep backs it. - fn hydra_accuracy( - &self, - raqe: &Raqe, - deployment: &Deployment, - facts: &WorkloadFacts, - ) -> Option { - let sketch = &deployment.config.sketch; - let key = ( - sketch.clone(), - format!("{:?}", config_params(&deployment.config)?), - facts[&deployment.metric].hydra_dataset.as_ref()?.0.clone(), - raqe.grouping_labels.clone(), - ); - let by_shards = self.hydra.get(&key)?; - let merges = if merges_lossily(sketch) { - deployment.query_instance_count(raqe.lookback_ms)? - } else { - 1 - }; - let column = HYDRA_COVERAGE - .iter() - .rposition(|&(share, _)| raqe.accuracy_covers_share.is_some_and(|t| t >= share)) - .unwrap_or(0); - let (_, below) = by_shards.range(..=merges).next_back()?; - let (_, above) = by_shards.range(merges..).next()?; - Some(worse( - below[column]?, - above[column]?, - accuracy_key(sketch).1, - )) - } - - /// The accuracy `raqe` gets from `deployment`, or `None` when it can't be - /// read: no fitted [`DataShape`], a config or shape outside the grid, or - /// too few items. [`SaturationCurves::load`] guarantees the curve's metric - /// is the family's. - /// Exact accumulators keep the cost table's value. `facts` must pass - /// [`crate::validate_facts`]. - pub fn accuracy( - &self, - raqe: &Raqe, - deployment: &Deployment, - facts: &WorkloadFacts, - ) -> Option { - self.accuracy_with_source(raqe, deployment, facts) - .map(|(value, _)| value) - } - - /// [`SaturationCurves::accuracy`], and where the returned (worst) value - /// came from: measured, or the algorithm's guarantee - /// ([`crate::theory`]) for a bracketing point the study didn't measure - /// (past an unsaturated curve, past the measured merge counts or N, or - /// with no merge curve). Below the curves' first N, or outside the grid, - /// it stays unknown. - pub fn accuracy_with_source( - &self, - raqe: &Raqe, - deployment: &Deployment, - facts: &WorkloadFacts, - ) -> Option<(f64, AccuracySource)> { - let properties = family_properties(&deployment.config.sketch); - if properties.exact { - return table_accuracy(raqe, deployment).map(|v| (v, AccuracySource::Measured)); - } - if properties.answers_any_subgrouping { - return self - .hydra_accuracy(raqe, deployment, facts) - .map(|v| (v, AccuracySource::Measured)); - } - let metric_facts = &facts[&deployment.metric]; - // The answered groups: the RAQE's, coarser than the deployment's on a - // roll-up, whose answer is the merged coarse group's. - let grouping = &raqe.grouping_labels; - let shape = metric_facts.data_shape.get(grouping)?; - let k = raqe.topk_k(); - let base = with_topk_k(&config_params(&deployment.config)?, TOPK_K); - // A heap top-k reads the curves at the RAQE's k, else at the next - // larger measured k whose curves bracket the shape (harder); past every - // such k, the guarantee at k, no better than the largest bracketing k. - let (points, past_measured_k) = if has_heap(&deployment.config.sketch) { - self.curves_at_k(&deployment.config.sketch, &base, k, shape)? - } else { - ( - self.bracketing_points(&deployment.config.sketch, &base, shape)?, - false, - ) - }; - let covered = items_per_group(metric_facts, grouping, raqe.lookback_ms); - let (_, direction) = accuracy_key(&deployment.config.sketch); - let merges = if merges_lossily(&deployment.config.sketch) { - // A roll-up also merges each coarse group's children, the average - // `card(G_d) / card(G_r)` of them. - // ponytail: average fan-out; the largest group's is sketch-bench#189. - let fan_out = metric_facts.cardinality[&deployment.grouping_labels] - .div_ceil(metric_facts.cardinality[grouping]); - let merges = deployment.query_instance_count(raqe.lookback_ms)? * fan_out; - // A heap of m · k merged from m windows reads as one sketch: the - // merged heaps are taken to still hold the true top k. - match ( - heap_capacity(&deployment.config), - deployment.heap_needed(raqe.lookback_ms, k), - ) { - (Some(heap), Some(need)) if heap >= need => 1, - _ => merges, - } - } else { - 1 - }; - // The guarantee is at the RAQE's own k. - let params = with_topk_k(&base, k); - let mut worst: Option<(f64, AccuracySource)> = None; - for point in points { - let measured = (!past_measured_k) - .then(|| point.merged_error_at(merges, covered, direction)) - .flatten(); - let found = match measured { - Some(error) => (error, AccuracySource::Measured), - // Past every measured k as well as past the curve: the - // guarantee at k, within the curves' N range as everywhere - // else, and no better than what was measured. - None if past_measured_k || covered >= point.first_measured_n(merges)? => { - if covered < point.first_measured_n(merges)? { - return None; - } - let bound = - theory::bound(&deployment.config.sketch, ¶ms, point.shape, merges)?; - // No better than the measurement it extends: past every k, - // the largest k's at this N (else its last); past the - // curve, its last. - let floor = if past_measured_k { - point - .merged_error_at(merges, covered, direction) - .or_else(|| point.last_measured_error(merges, direction)) - } else { - point.last_measured_error(merges, direction) - }; - let error = match floor { - Some(last) => worse(bound, last, direction), - None => bound, - }; - (error, AccuracySource::Theory) - } - None => return None, - }; - if worst.is_none_or(|(w, _)| worse(w, found.0, direction) != w) { - worst = Some(found); - } - } - worst - } - - /// The oracle for [`crate::autosketch::plan`]: AutoSketch keeps one - /// unmerged sketch per query window. - pub fn autosketch_accuracy( - &self, - raqe: &Raqe, - config: &AtomicCostEntry, - facts: &WorkloadFacts, - ) -> Option { - let (window_ms, slide_ms) = window_adapter(raqe); - let deployment = Deployment { - capability: raqe.capability, - metric: raqe.metric.clone(), - spatial_filter: raqe.spatial_filter.clone(), - grouping_labels: raqe.grouping_labels.clone(), - config: config.clone(), - window_ms, - slide_ms, - key_tracker: None, - }; - self.accuracy(raqe, &deployment, facts) - } - - /// Checks the cost table against the family table and the curves. A - /// candidate family's row must name the family's metric as its - /// `accuracy_metric`. A sketch row's accuracy is the curve of its config - /// and `measured_at.data_shape()`, read at `measured_at.items_per_instance`. - /// Rows of sketches the study didn't run (exact accumulators among them) - /// skip the curve check. - pub fn check_cost_table(&self, costs: &[AtomicCostEntry]) -> CostTableCheck { - let mut check = CostTableCheck::default(); - for row in costs { - let name = format!("{} {}", row.sketch, row.sketch_config["params"]); - let metric = &row.accuracy_metric; - if is_candidate(&row.sketch) { - if let Some((family_metric, _)) = family_properties(&row.sketch).accuracy { - if metric != family_metric { - check.mismatched.push(format!( - "{name}: accuracy_metric is {metric}, but the optimizer reads \ - {family_metric}" - )); - continue; - } - } - } - let Some(points) = self.points_by_sketch.get(&row.sketch) else { - continue; - }; - // Curves are measured at a heap of k; larger heaps are cost-only. - if heap_capacity(row).is_some_and(|heap| heap != TOPK_K) { - continue; - } - let params = config_params(row); - let mut same_config = points - .iter() - .filter(|point| Some(&point.params) == params.as_ref()) - .peekable(); - if same_config.peek().is_none() { - check - .mismatched - .push(format!("{name}: config not in the saturation grid")); - continue; - } - let shape = row.measured_at.data_shape(); - let Some(point) = same_config.find(|point| Some(point.shape) == shape) else { - check - .unchecked - .push(format!("{name}: no grid point at {shape:?}")); - continue; - }; - if &point.error_metric != metric { - check.mismatched.push(format!( - "{name}: accuracy_metric is {metric}, the curve's {}", - point.error_metric - )); - continue; - } - let Some(value) = row.accuracy() else { - check - .mismatched - .push(format!("{name}: the table has no {metric}")); - continue; - }; - let n = row.measured_at.items_per_instance as f64; - match point.agrees_at(n, value) { - None => check - .unchecked - .push(format!("{name}: N = {n} is outside the curve")), - Some(false) => check.mismatched.push(format!( - "{name}: {metric} = {value} at N = {n}, curve {:?}", - point.curve - )), - Some(true) => {} - } - } - // A heap top-k config priced at one heap only can't serve any heap - // above it: every merged top-k candidate would quietly vanish. - let mut heaps: BTreeMap<(String, String), BTreeSet> = BTreeMap::new(); - for row in costs.iter().filter(|row| is_candidate(&row.sketch)) { - if let Some(heap) = heap_capacity(row) { - let key = (row.sketch.clone(), format!("{:?}", config_params(row))); - heaps.entry(key).or_default().insert(heap); - } - } - for ((sketch, params), measured) in heaps { - if measured.len() < 2 { - check.mismatched.push(format!( - "{sketch} {params}: priced at heap {measured:?} only; rerun \ - study_saturation.py --phase optimizer-cost for the heap sizes" - )); - } - } - check - } - - /// Every grid point of `config` bracketing `shape` (Q8 of #156), or - /// `None` when `config` isn't in the grid, `shape` lies outside it, or a - /// bracketing point is missing. - fn bracketing_points( - &self, - sketch: &str, - params: &BTreeMap, - shape: &DataShape, - ) -> Option> { - let points: Vec<&GridPoint> = self - .points_by_sketch - .get(sketch)? - .iter() - .filter(|point| &point.params == params) - .collect(); - let quantile = points.first()?.distinct_keys().is_none(); - let shape_params = if quantile { - bracket(points.iter().map(|p| p.shape_param()), shape.tail_index)? - } else { - bracket(points.iter().map(|p| p.shape_param()), shape.zipf_s)? - }; - let keys = if quantile { - vec![None] - } else { - let keys = points.iter().filter_map(|p| p.distinct_keys()); - bracket(keys, shape.distinct_keys)? - .into_iter() - .map(Some) - .collect() - }; - let bracketing: Vec<&GridPoint> = points - .into_iter() - .filter(|p| { - shape_params.contains(&p.shape_param()) && keys.contains(&p.distinct_keys()) - }) - .collect(); - (bracketing.len() == shape_params.len() * keys.len()).then_some(bracketing) - } -} - -impl SaturationCurves { - /// The grid points a top-`k` answer of `sketch` with `base` params reads - /// on `shape`: those of the smallest measured k at or above `k` whose - /// curves bracket `shape`, with `false`; else those of the largest k that - /// brackets it, with `true` (only the guarantee at `k` applies, floored - /// by them). `None` when no k's curves bracket `shape`. - fn curves_at_k( - &self, - sketch: &str, - base: &BTreeMap, - k: u64, - shape: &DataShape, - ) -> Option<(Vec<&GridPoint>, bool)> { - let measured: BTreeSet = self - .points_by_sketch - .get(sketch)? - .iter() - .filter(|point| same_but_topk_k(&point.params, base)) - .map(|point| params_topk_k(point.params.get("topk_k").copied())) - .collect(); - let bracket_at = |at: u64| self.bracketing_points(sketch, &with_topk_k(base, at), shape); - if let Some(points) = measured.range(k..).find_map(|&at| bracket_at(at)) { - return Some((points, false)); - } - measured - .iter() - .rev() - .find_map(|&at| bracket_at(at)) - .map(|points| (points, true)) - } -} - -/// Whether `a` and `b` name the same config, `topk_k` aside. -fn same_but_topk_k(a: &BTreeMap, b: &BTreeMap) -> bool { - let rest = |m: &BTreeMap| m.iter().filter(|(name, _)| *name != "topk_k").count(); - rest(a) == rest(b) - && a.iter() - .filter(|(name, _)| *name != "topk_k") - .all(|(name, value)| b.get(name) == Some(value)) -} - -/// `params` keyed at top-k `k`: a `topk_k` entry unless `k` is [`TOPK_K`], -/// which the grid and cost rows leave implicit. -fn with_topk_k(params: &BTreeMap, k: u64) -> BTreeMap { - let mut params = params.clone(); - params.remove("topk_k"); - if let Some(k) = crate::topk_k_param(k) { - params.insert("topk_k".to_string(), k as f64); - } - params -} - -/// Items one group receives in `window_ms`: series per group times scrapes -/// in the window. -fn items_per_group(facts: &MetricFacts, grouping: &LabelSet, window_ms: u64) -> f64 { - let series = facts.cardinality[&facts.labels] as f64; - let groups = facts.cardinality[grouping] as f64; - series / groups * window_ms as f64 / facts.scrape_interval_ms as f64 -} - -/// The grid values equal to `target`, else the two either side of it; -/// `None` outside the grid. -fn bracket(values: impl Iterator, target: f64) -> Option> { - let mut values: Vec = values.collect(); - values.sort_by(f64::total_cmp); - values.dedup(); - if values.contains(&target) { - return Some(vec![target]); - } - let above = values.partition_point(|&v| v < target); - (above > 0 && above < values.len()).then(|| vec![values[above - 1], values[above]]) -} - -/// `config`'s params, less a top-k `heap`: `{"params": {"rows": 3, "cols": -/// 1024, "heap": 128}}` → `{rows: 3, cols: 1024}`. Curves are measured at a -/// heap of `TOPK_K`, and a larger heap reads the same curve. -fn config_params(config: &AtomicCostEntry) -> Option> { - config.sketch_config["params"] - .as_object()? - .iter() - .filter(|(name, _)| *name != "heap") - .map(|(name, value)| Some((name.clone(), value.as_f64()?))) - .collect() -} - -/// [`HYDRA_SATURATION`]'s rows of the Hydra variants some capability plans -/// (the rest, such as hydra-cms, are skipped), each column's worst over -/// records and seeds. -fn load_hydra( - path: &Path, -) -> io::Result> { - let mut out: BTreeMap<_, HydraErrors> = BTreeMap::new(); - let columns = [ - "variant", - "config", - "dataset", - "group_columns", - "merge_shards", - HYDRA_COVERAGE[0].1, - HYDRA_COVERAGE[1].1, - HYDRA_COVERAGE[2].1, - ]; - for row in read_csv(path, &columns)? { - let variant = row["variant"].as_str(); - if !is_candidate(variant) || !family_properties(variant).answers_any_subgrouping { - continue; - } - let shards: u64 = row["merge_shards"] - .parse() - .map_err(|_| invalid(format!("{HYDRA_SATURATION}: merge_shards: {row:?}")))?; - let mut errors = [None; 3]; - for (error, (_, column)) in errors.iter_mut().zip(HYDRA_COVERAGE) { - if !row[column].is_empty() { - *error = Some(parse_number(&row[column])?); - } - } - let key = ( - variant.to_string(), - format!("{:?}", parse_config(&row["config"])?), - row["dataset"].clone(), - row["group_columns"] - .split(',') - .map(str::to_string) - .collect(), - ); - let direction = accuracy_key(variant).1; - let by_shards = out.entry(key).or_default(); - match by_shards.get_mut(&shards) { - None => { - by_shards.insert(shards, errors); - } - Some(worst) => { - // An empty cell means no covered group was scored in that - // run (say, all had zero truth): it bounds nothing. - for (worst, error) in worst.iter_mut().zip(errors) { - *worst = match (*worst, error) { - (Some(a), Some(b)) => Some(worse(a, b, direction)), - (a, b) => a.or(b), - }; - } - } - } - } - Ok(out) -} - -/// `"rows=3 cols=1024"` → `{rows: 3, cols: 1024}`; the grid's config strings. -/// Fails on a pair that isn't `name=number`. -fn parse_config(config: &str) -> io::Result> { - config - .split_whitespace() - .map(|pair| { - let (name, value) = pair - .split_once('=') - .ok_or_else(|| invalid(format!("config {config:?}: {pair:?} is not name=value")))?; - let value = value - .parse() - .map_err(|_| invalid(format!("config {config:?}: {name} is not a number")))?; - Ok((name.to_string(), value)) - }) - .collect() -} - -/// A grid row's `dist`, `param` and `cardinality` as a [`MeasuredShape`]. -fn parse_shape(row: &CsvRow) -> io::Result { - let param = parse_number(&row["param"])?; - match row["dist"].as_str() { - "zipf" => Ok(MeasuredShape::Zipf { - skew: param, - keys: parse_number(&row["cardinality"])?, - }), - "pareto" => Ok(MeasuredShape::Pareto { tail_index: param }), - dist => Err(invalid(format!("unknown dist {dist:?}"))), - } -} - -fn parse_number(text: &str) -> io::Result { - text.parse() - .map_err(|_| invalid(format!("not a number: {text:?}"))) -} - -fn invalid(message: String) -> io::Error { - io::Error::new(io::ErrorKind::InvalidData, message) -} - -type CsvRow = BTreeMap; - -/// Fields may be quoted: `hydra_saturation.csv`'s groupings hold commas. -/// Fails unless the header has every `required` column and every row -/// exactly one field per header column. -fn read_csv(path: &Path, required: &[&str]) -> io::Result> { - let failed = |e: csv::Error| invalid(format!("{}: {e}", path.display())); - let text = std::fs::read_to_string(path) - .map_err(|e| io::Error::new(e.kind(), format!("{}: {e}", path.display())))?; - let mut reader = csv::ReaderBuilder::new() - .flexible(true) - .from_reader(text.as_bytes()); - let header = reader.headers().map_err(failed)?.clone(); - let missing: Vec<_> = required - .iter() - .filter(|&&column| !header.iter().any(|name| name == column)) - .collect(); - if !missing.is_empty() { - return Err(invalid(format!( - "{}: missing columns {missing:?}", - path.display() - ))); - } - reader - .records() - .map(|record| { - let record = record.map_err(failed)?; - if record.len() != header.len() { - return Err(invalid(format!( - "{}: line {}: {} fields, the header has {}", - path.display(), - record.position().map_or(0, |p| p.line()), - record.len(), - header.len() - ))); - } - Ok(header - .iter() - .zip(record.iter()) - .map(|(name, value)| (name.to_string(), value.to_string())) - .collect()) - }) - .collect() -} - -#[cfg(test)] -mod tests { - use super::*; - use crate::test_support::{facts, raqe, METRIC}; - use crate::Capability; - - const TOPK: &str = "cms-heap-topk-fastpath-vector2d"; - - /// UnivMon's heaps are rebuilt on merge, so its cardinality reads merge - /// curves like KLL; HLL's register max and DDSketch's bucket sums are - /// exact. - #[test] - fn univmon_cardinality_merges_lossily_hll_and_dd_do_not() { - assert!(merges_lossily("univmon-cardinality")); - assert!(!merges_lossily("hll")); - assert!(!merges_lossily("dd")); - } - - /// Top-k rows=3 cols=1024 at θ ∈ {1.0, 1.2}, K ∈ {1e3, 1e5}. Precision - /// falls with N; θ = 1.0 is worse, and K = 1e5 never saturates. Merged - /// from 4 shards it loses 0.02 more, from 16 shards 0.1. - fn curves() -> SaturationCurves { - let curve = |offset: f64| { - vec![ - (1e3, 1.0 - offset, 0.0), - (1e4, 0.95 - offset, 0.01), - (1e5, 0.9 - offset, 0.0), - ] - }; - let point = |theta: f64, keys: f64, offset: f64, n_sat: Option| GridPoint { - params: parse_config("rows=3 cols=1024").unwrap(), - shape: MeasuredShape::Zipf { skew: theta, keys }, - error_metric: "precision_at_k".into(), - n_sat, - curve: curve(offset), - merged: BTreeMap::from([(4, curve(offset + 0.02)), (16, curve(offset + 0.1))]), - }; - SaturationCurves { - points_by_sketch: BTreeMap::from([( - TOPK.to_string(), - vec![ - point(1.0, 1e3, 0.05, Some(1e4)), - point(1.2, 1e3, 0.0, Some(1e4)), - point(1.0, 1e5, 0.1, None), - point(1.2, 1e5, 0.08, None), - ], - )]), - ..SaturationCurves::default() - } - } - - /// One group of `series` series scraped every second, shaped `shape`. - fn workload(series: u64, shape: DataShape) -> WorkloadFacts { - let mut facts = facts(1, series); - let metric = facts.get_mut(METRIC).unwrap(); - metric.data_shape.insert(LabelSet::new(), shape); - facts - } - - fn shape(zipf_s: f64, distinct_keys: f64) -> DataShape { - DataShape { - zipf_s, - distinct_keys, - tail_index: 2.0, - } - } - - fn topk_raqe(lookback_ms: u64) -> Raqe { - Raqe { - capability: Capability::TopKByValue, - ..raqe(lookback_ms, lookback_ms) - } - } - - fn deployment(sketch: &str, window_ms: u64) -> Deployment { - Deployment { - capability: Capability::TopKByValue, - metric: METRIC.into(), - spatial_filter: String::new(), - grouping_labels: LabelSet::new(), - config: AtomicCostEntry { - sketch: sketch.into(), - sketch_config: serde_json::json!({"params": {"rows": 3, "cols": 1024}}), - mem_bytes_per_instance: 1.0, - insert_cpu_secs: 1.0, - merge_cpu_secs: 1.0, - query_cpu_secs: 1.0, - query_accuracy: BTreeMap::from([("precision_at_k".into(), 1.0)]), - accuracy_metric: crate::test_support::metric_of(sketch), - measured_at: crate::test_support::measured_at(), - }, - window_ms, - slide_ms: window_ms, - key_tracker: None, - } - } - - #[test] - fn reads_the_worse_neighbour_between_checkpoints_and_the_plateau_past_the_last() { - let point = &curves().points_by_sketch[TOPK][1]; - let higher = AccuracyDirection::HigherIsBetter; - assert_eq!(point.error_at(1e4, higher), Some(0.95)); - assert_eq!(point.error_at(3e4, higher), Some(0.9)); - assert_eq!(point.error_at(5e2, higher), None); - assert_eq!(point.error_at(1e9, higher), Some(0.9)); - let unsaturated = &curves().points_by_sketch[TOPK][2]; - assert_eq!(unsaturated.error_at(1e9, higher), None); - } - - #[test] - fn takes_the_worst_bracketing_grid_point() { - // 10 series × 1000 s = 1e4 items; θ = 1.1 brackets 1.0 and 1.2. - let facts = workload(10, shape(1.1, 1e3)); - let r = topk_raqe(1_000_000); - let value = curves().accuracy(&r, &deployment(TOPK, 1_000_000), &facts); - assert_eq!( - value, - Some(0.95 - 0.05), - "θ = 1.0 at 1e4 is worse than θ = 1.2" - ); - } - - /// A hole in the grid (a bracketing point the study didn't measure) - /// leaves the shape unknown rather than bridging to farther points: - /// accuracy needn't be monotone in K. - #[test] - fn a_hole_in_the_grid_has_no_accuracy() { - let mut curves = curves(); - // Drop θ = 1.2, K = 1e3. - let hole = MeasuredShape::Zipf { - skew: 1.2, - keys: 1e3, - }; - curves - .points_by_sketch - .get_mut(TOPK) - .unwrap() - .retain(|p| p.shape != hole); - let r = topk_raqe(1_000_000); - let d = deployment(TOPK, 1_000_000); - assert_eq!( - curves.accuracy(&r, &d, &workload(10, shape(1.1, 1e3))), - None - ); - // θ = 1.0 at K = 1e3 is still measured. - assert!(curves - .accuracy(&r, &d, &workload(10, shape(1.0, 1e3))) - .is_some()); - } - - #[test] - fn a_shape_outside_the_grid_or_without_a_fit_has_no_accuracy() { - let r = topk_raqe(1_000_000); - let d = deployment(TOPK, 1_000_000); - assert_eq!( - curves().accuracy(&r, &d, &workload(10, shape(0.5, 1e3))), - None - ); - assert_eq!( - curves().accuracy(&r, &d, &workload(10, shape(1.0, 1e7))), - None - ); - assert_eq!(curves().accuracy(&r, &d, &facts(1, 10)), None); - } - - #[test] - fn merged_answers_read_the_merge_curve_at_the_lookback_count() { - // 10 items/s: the 1e4 s lookback covers 1e5 items whatever the - // window; top-k reads the curve of the sketch merged from L/x. - let facts = workload(10, shape(1.2, 1e3)); - let r = topk_raqe(10_000_000); - let at = |window_ms| curves().accuracy(&r, &deployment(TOPK, window_ms), &facts); - // One window: the plain curve. - assert_eq!(at(10_000_000), Some(0.9)); - // 4 windows: the 4-shard curve. - assert!((at(2_500_000).unwrap() - 0.88).abs() < 1e-12); - // 10 windows sit between 4 and 16 shards: the worse, 16. - assert!((at(1_000_000).unwrap() - 0.8).abs() < 1e-12); - // 100 windows: past the largest measured count, unmeasured. - assert_eq!(at(100_000), None); - } - - /// p99 by service from KLL by (service, endpoint) reads the coarse - /// group's shape and items, merged from its 10 endpoints. - #[test] - fn a_kll_roll_up_reads_the_merge_curve_at_fan_out_times_windows() { - use crate::test_support::{kll_by, label_set, quantile_by, service_endpoint_facts}; - let curve = |error: f64| vec![(1e3, error, 0.0), (1e4, error + 0.01, 0.0)]; - let kll = GridPoint { - params: parse_config("k=200").unwrap(), - shape: MeasuredShape::Pareto { tail_index: 2.0 }, - error_metric: "mean_rank_err".into(), - n_sat: None, - curve: curve(0.01), - merged: BTreeMap::from([(4, curve(0.02)), (16, curve(0.05))]), - }; - let curves = SaturationCurves { - points_by_sketch: BTreeMap::from([("kll-percall".to_string(), vec![kll])]), - ..SaturationCurves::default() - }; - let mut facts = service_endpoint_facts(); - let metric = facts.get_mut(METRIC).unwrap(); - // Only the coarse grouping's shape is on the grid. - metric - .data_shape - .insert(label_set(&["service"]), shape(1.0, 1.0)); - metric.data_shape.insert( - label_set(&["service", "endpoint"]), - DataShape { - tail_index: 9.0, - ..shape(1.0, 1.0) - }, - ); - // 10 series per service × 100 s = 1e3 items, in one window. - let by_service = quantile_by(&["service"], 100_000, 100_000); - let accuracy = - |grouping: &[&str]| curves.accuracy(&by_service, &kll_by(grouping, 100_000), &facts); - assert_eq!(accuracy(&["service"]), Some(0.01)); - // 10 merged endpoints sit between 4 and 16 shards: the worse, 16. - assert_eq!(accuracy(&["service", "endpoint"]), Some(0.05)); - } - - /// A heap of m · k merged from m windows reads as one sketch: the plain - /// curve, not the merge curve, and no merge curve is needed. - #[test] - fn a_heap_of_m_times_k_reads_the_plain_curve() { - let facts = workload(10, shape(1.2, 1e3)); - let r = topk_raqe(10_000_000); - let with_heap = |window_ms, heap: u64| { - let mut d = deployment(TOPK, window_ms); - d.config.sketch_config["params"]["heap"] = heap.into(); - d - }; - let mut plain_only = curves(); - for point in plain_only.points_by_sketch.get_mut(TOPK).unwrap() { - point.merged.clear(); - } - // 4 windows: a heap of 4k reads the plain curve; 2k doesn't, and - // without a merge curve has no accuracy. - assert_eq!( - plain_only.accuracy(&r, &with_heap(2_500_000, 4 * TOPK_K), &facts), - Some(0.9) - ); - assert_eq!( - plain_only.accuracy(&r, &with_heap(2_500_000, 2 * TOPK_K), &facts), - None - ); - // With merge curves, a small heap still reads them. - assert!( - (curves() - .accuracy(&r, &with_heap(2_500_000, TOPK_K), &facts) - .unwrap() - - 0.88) - .abs() - < 1e-12 - ); - } - - /// Past a merge curve's last checkpoint the merged answer is unknown, - /// even when the plain curve saturated: nothing shows the merged sketch - /// stopped changing. - #[test] - fn a_merge_curve_has_no_plateau() { - let point = &curves().points_by_sketch[TOPK][1]; - let higher = AccuracyDirection::HigherIsBetter; - assert_eq!(point.n_sat, Some(1e4)); - assert!((point.merged_error_at(4, 1e5, higher).unwrap() - 0.88).abs() < 1e-12); - assert_eq!(point.merged_error_at(4, 1e9, higher), None); - assert_eq!(point.error_at(1e9, higher), Some(0.9)); - } - - /// Without a merge curve, a merged top-k answer has no known accuracy; - /// one window still reads the plain curve. - #[test] - fn a_lossy_merge_without_a_merge_curve_is_unknown() { - let mut curves = curves(); - for point in curves.points_by_sketch.get_mut(TOPK).unwrap() { - point.merged.clear(); - } - let facts = workload(10, shape(1.2, 1e3)); - let r = topk_raqe(10_000_000); - assert_eq!( - curves.accuracy(&r, &deployment(TOPK, 1_000_000), &facts), - None - ); - assert_eq!( - curves.accuracy(&r, &deployment(TOPK, 10_000_000), &facts), - Some(0.9) - ); - } - - #[test] - fn load_reads_merge_curves_beyond_one_shard() { - let dir = std::env::temp_dir().join(format!("rqe-merge-{}", std::process::id())); - let header = "family,sketch,config,dist,param,cardinality"; - let point = "quantile,kll-percall,k=200,pareto,2.0,"; - for run in RUN_DIRS { - std::fs::create_dir_all(dir.join(run)).unwrap(); - std::fs::write( - dir.join(run).join("saturation.csv"), - format!( - "{header},n_sat,final_error,error_metric\n{point},1000,0.01,mean_rank_err\n" - ), - ) - .unwrap(); - std::fs::write( - dir.join(run).join("saturation_curve.csv"), - format!("{header},n,seed_mean_error,seed_se\n{point},1000,0.01,0\n"), - ) - .unwrap(); - } - // One shard is the plain curve. The later run measured only 16 - // shards: it keeps the earlier run's 4. - let merge = |rows: &str| format!("{header},n,shards,seed_mean_error,seed_se\n{rows}"); - let merge_path = |run| dir.join(run).join("saturation_merge_curve.csv"); - std::fs::write( - merge_path(RUN_DIRS[0]), - merge(&format!("{point},1000,1,0.01,0\n{point},1000,4,0.02,0\n")), - ) - .unwrap(); - std::fs::write( - merge_path(RUN_DIRS[1]), - merge(&format!("{point},1000,16,0.03,0\n")), - ) - .unwrap(); - let loaded = SaturationCurves::load(&dir); - // A merged KLL with no merge curve anywhere is refused. - std::fs::remove_file(merge_path(RUN_DIRS[0])).unwrap(); - std::fs::write(merge_path(RUN_DIRS[1]), merge("")).unwrap(); - let missing = SaturationCurves::load(&dir); - std::fs::remove_dir_all(&dir).unwrap(); - let point = &loaded.unwrap().points_by_sketch["kll-percall"][0]; - assert_eq!( - point.merged, - BTreeMap::from([(4, vec![(1e3, 0.02, 0.0)]), (16, vec![(1e3, 0.03, 0.0)])]) - ); - assert!(missing.unwrap_err().to_string().contains("no merge curve")); - } - - /// Past an unsaturated curve the study measured nothing: the answer - /// falls back to the algorithm's guarantee, and says so. A merged top-k - /// answer has no guarantee. - #[test] - fn an_unmeasured_point_falls_back_to_the_guarantee() { - // θ = 1.0, K = 1e5 never saturates; 10 series × 1e8 s = 1e9 items. - let facts = workload(10, shape(1.0, 1e5)); - let r = topk_raqe(100_000_000); - let one_window = curves().accuracy_with_source(&r, &deployment(TOPK, 100_000_000), &facts); - let params = BTreeMap::from([("rows".to_string(), 3.0), ("cols".to_string(), 1024.0)]); - let zipf = MeasuredShape::Zipf { - skew: 1.0, - keys: 1e5, - }; - let bound = theory::bound(TOPK, ¶ms, zipf, 1).unwrap(); - assert_eq!(one_window, Some((bound, AccuracySource::Theory))); - let merged = curves().accuracy_with_source(&r, &deployment(TOPK, 10_000_000), &facts); - assert_eq!(merged, None); - // Measured where the curve has it. - let measured = curves().accuracy_with_source( - &r, - &deployment(TOPK, 100_000_000), - &workload(10, shape(1.2, 1e3)), - ); - assert_eq!( - measured.map(|(_, source)| source), - Some(AccuracySource::Measured) - ); - } - - /// A fallback's floor is the worse last measurement of both shard - /// counts around the merge count, not only the smaller. - #[test] - fn a_fallback_floor_reads_both_bracketing_merge_curves() { - let point = &curves().points_by_sketch[TOPK][1]; - let higher = AccuracyDirection::HigherIsBetter; - // 4 shards end at 0.88, 16 at 0.80: 8 merges floor at 0.80. - assert!((point.last_measured_error(8, higher).unwrap() - 0.8).abs() < 1e-12); - assert!((point.last_measured_error(4, higher).unwrap() - 0.88).abs() < 1e-12); - assert!((point.last_measured_error(1, higher).unwrap() - 0.9).abs() < 1e-12); - // Past the largest count, the largest's. - assert!((point.last_measured_error(100, higher).unwrap() - 0.8).abs() < 1e-12); - } - - /// A top-k answer reads the curve at its k, else the next larger - /// measured k; past every measured k, only the guarantee at k. - #[test] - fn a_topk_answer_reads_the_curve_at_its_k() { - // The fixture's curves are at k = 32; add the same config at k = 100, - // 0.2 lower in precision. - let mut curves = curves(); - let at_100: Vec = curves.points_by_sketch[TOPK] - .iter() - .map(|p| { - let mut p = p.clone(); - p.params.insert("topk_k".into(), 100.0); - p.curve.iter_mut().for_each(|c| c.1 -= 0.2); - p - }) - .collect(); - curves - .points_by_sketch - .get_mut(TOPK) - .unwrap() - .extend(at_100); - // 10 series × 1e4 s = 1e5 items on θ = 1.2, K = 1e3: k = 32 reads 0.9. - let facts = workload(10, shape(1.2, 1e3)); - let read = |k| { - let r = Raqe { - topk_k: Some(k), - ..topk_raqe(10_000_000) - }; - let mut d = deployment(TOPK, 10_000_000); - d.config.sketch_config["params"]["heap"] = (k.max(TOPK_K)).into(); - curves.accuracy_with_source(&r, &d, &facts) - }; - let close = |got: Option<(f64, AccuracySource)>, want: f64, source| { - let (v, s) = got.unwrap(); - assert!((v - want).abs() < 1e-12 && s == source, "{v} {s:?}"); - }; - close(read(32), 0.9, AccuracySource::Measured); - // k = 10 isn't measured: the next larger, 32. - close(read(10), 0.9, AccuracySource::Measured); - // k = 50 reads k = 100's curve. - close(read(50), 0.7, AccuracySource::Measured); - close(read(100), 0.7, AccuracySource::Measured); - // k = 200: past every measured k, the guarantee at 200, no better than - // k = 100's measured 0.7. - let (value, source) = read(200).unwrap(); - assert_eq!(source, AccuracySource::Theory); - assert!(value <= 0.7 + 1e-12, "{value}"); - } - - /// A larger k whose curves don't bracket the shape is skipped: the - /// answer falls to the guarantee, floored by a k that does bracket it. - #[test] - fn a_k_whose_curves_miss_the_shape_falls_to_the_guarantee() { - // k = 100 measured at K = 1e3 only; the shape below needs K = 1e3..1e5. - let mut curves = curves(); - let at_100: Vec = curves.points_by_sketch[TOPK] - .iter() - .filter(|p| p.distinct_keys() == Some(1e3)) - .map(|p| { - let mut p = p.clone(); - p.params.insert("topk_k".into(), 100.0); - p - }) - .collect(); - curves - .points_by_sketch - .get_mut(TOPK) - .unwrap() - .extend(at_100); - let facts = workload(10, shape(1.2, 1e4)); - let r = Raqe { - topk_k: Some(50), - ..topk_raqe(10_000_000) - }; - let mut d = deployment(TOPK, 10_000_000); - d.config.sketch_config["params"]["heap"] = 50.into(); - let (_, source) = curves.accuracy_with_source(&r, &d, &facts).unwrap(); - assert_eq!(source, AccuracySource::Theory); - } - - /// A top-`k` RAQE merged from m windows needs a heap of m·k. - #[test] - fn the_heap_needed_is_merges_times_k() { - assert_eq!(crate::heap_needed(60_000, 15_000, 10), Some(40)); - assert_eq!(crate::heap_needed(60_000, 15_000, TOPK_K), Some(4 * TOPK_K)); - assert_eq!(crate::heap_needed(60_000, 7_000, 10), None); - } - - #[test] - fn exact_accumulators_keep_the_cost_table_value() { - let r = topk_raqe(1_000_000); - let mut d = deployment("exact-sum", 1_000_000); - d.config.query_accuracy = BTreeMap::from([("relative_error".into(), 0.0)]); - let value = curves().accuracy(&r, &d, &facts(1, 10)); - assert_eq!(value, Some(0.0)); - } - - /// A top-k row measured at θ = 1.2, K = 1e3 and `items`, scoring - /// `precision`. - fn measured_row(cols: u64, items: u64, precision: f64) -> AtomicCostEntry { - use aqpbm_core::{DataDistribution, MeasuredAt, ZipfParameter}; - AtomicCostEntry { - sketch_config: serde_json::json!({"params": {"rows": 3, "cols": cols}}), - query_accuracy: BTreeMap::from([("precision_at_k".into(), precision)]), - measured_at: MeasuredAt { - items_per_instance: items, - keys_per_instance: Some(1_000), - value_range: None, - merge_operand_items: None, - distribution: Some(DataDistribution::Zipf(ZipfParameter { - skewness: 1.2, - population_size: 1_000, - seed: 1, - })), - dataset: None, - schema_width: None, - }, - ..deployment(TOPK, 1).config - } - } - - #[test] - fn the_cost_table_is_a_point_on_the_curve() { - // Each top-k row also priced at a large heap, as the study does. - let check = |rows: &[AtomicCostEntry]| { - let mut all = rows.to_vec(); - for row in rows.iter().filter(|row| heap_capacity(row).is_some()) { - let mut large = row.clone(); - large.sketch_config["params"]["heap"] = (64 * TOPK_K).into(); - all.push(large); - } - curves().check_cost_table(&all) - }; - // Priced at one heap only: no merged top-k candidate could be built. - let lone = curves().check_cost_table(&[measured_row(1024, 10_000, 0.93)]); - assert_eq!(lone.mismatched.len(), 1, "{lone:?}"); - assert!(lone.mismatched[0].contains("priced at heap"), "{lone:?}"); - // A heap of k is checked like no heap; a larger heap is cost-only. - let with_heap = |heap: u64, precision| { - let mut row = measured_row(1024, 10_000, precision); - row.sketch_config["params"]["heap"] = heap.into(); - row - }; - assert_eq!(check(&[with_heap(TOPK_K, 0.8)]).mismatched.len(), 1); - assert_eq!( - check(&[with_heap(4 * TOPK_K, 0.8)]), - CostTableCheck::default() - ); - // θ = 1.2, K = 1e3 reads 0.95 ± 3 × 0.01 + 5% at N = 1e4. - assert_eq!( - check(&[measured_row(1024, 10_000, 0.93)]), - CostTableCheck::default() - ); - assert_eq!( - check(&[measured_row(1024, 10_000, 0.8)]).mismatched.len(), - 1 - ); - // Between checkpoints: the range of the two either side. - assert!(check(&[measured_row(1024, 30_000, 0.92)]) - .mismatched - .is_empty()); - // A config off the grid is a mismatch; a shape or N the study didn't - // run is unchecked. - assert_eq!( - check(&[measured_row(2048, 10_000, 0.95)]).mismatched.len(), - 1 - ); - assert_eq!(check(&[measured_row(1024, 100, 0.95)]).unchecked.len(), 1); - let mut pareto = measured_row(1024, 10_000, 0.95); - pareto.measured_at.distribution = None; - assert_eq!(check(&[pareto]).unchecked.len(), 1); - // A row naming another metric than its family's is a mismatch. - let mut other_metric = measured_row(1024, 10_000, 0.95); - other_metric.query_accuracy = BTreeMap::from([("recall_at_k".into(), 0.95)]); - other_metric.accuracy_metric = "recall_at_k".into(); - // (Its large-heap copy names the same wrong metric.) - let wrong = check(&[other_metric]).mismatched; - assert!( - !wrong.is_empty() && wrong.iter().all(|m| m.contains("recall_at_k")), - "{wrong:?}" - ); - // Sketches the study didn't run, exact ones among them, skip the - // curve, but still name their family's metric. - let exact = AtomicCostEntry { - sketch: "exact-sum".into(), - query_accuracy: BTreeMap::from([("relative_error".into(), 0.0)]), - accuracy_metric: "relative_error".into(), - ..measured_row(1024, 10_000, 0.0) - }; - assert_eq!( - check(std::slice::from_ref(&exact)), - CostTableCheck::default() - ); - let misnamed = AtomicCostEntry { - accuracy_metric: "relative_error_mean".into(), - ..exact - }; - assert_eq!(check(&[misnamed]).mismatched.len(), 1); - } - - /// Each top-k k is its own grid: k = 32 on θ 1.0 and k = 10 on θ 1.2 - /// are each a full cross, though their union isn't, and load accepts them. - /// A hole within one k is still refused. - #[test] - fn the_full_cross_is_checked_per_k() { - let write = |tag: &str, points: &[String]| { - let dir = std::env::temp_dir().join(format!("rqe-k-{tag}-{}", std::process::id())); - let header = "family,sketch,config,dist,param,cardinality"; - for run in RUN_DIRS { - std::fs::create_dir_all(dir.join(run)).unwrap(); - let summary: String = points - .iter() - .map(|p| format!("{p},1000,0.9,precision_at_k\n")) - .collect(); - let curve: String = points.iter().map(|p| format!("{p},1000,0.9,0\n")).collect(); - std::fs::write( - dir.join(run).join("saturation.csv"), - format!("{header},n_sat,final_error,error_metric\n{summary}"), - ) - .unwrap(); - std::fs::write( - dir.join(run).join("saturation_curve.csv"), - format!("{header},n,seed_mean_error,seed_se\n{curve}"), - ) - .unwrap(); - } - let loaded = SaturationCurves::load(&dir).map(|_| ()); - std::fs::remove_dir_all(&dir).unwrap(); - loaded - }; - let point = |config: &str, shape: &str| format!("topk,{TOPK},{config},zipf,{shape}"); - let per_k = [ - point("rows=3 cols=1024", "1.0,1000"), - point("rows=3 cols=1024", "1.0,100000"), - point("rows=3 cols=1024 topk_k=10", "1.2,1000"), - point("rows=3 cols=1024 topk_k=10", "1.2,100000"), - ]; - write("ok", &per_k).expect("each k is a full cross"); - let holed = [ - per_k[0].clone(), - per_k[1].clone(), - point("rows=3 cols=1024 topk_k=10", "1.0,1000"), - point("rows=3 cols=1024 topk_k=10", "1.2,100000"), - ]; - let err = write("hole", &holed).unwrap_err().to_string(); - assert!( - err.contains("topk_k") && err.contains("full cross"), - "{err}" - ); - } - - #[test] - fn load_refuses_a_grid_that_isnt_a_full_cross() { - let dir = std::env::temp_dir().join(format!("rqe-cross-{}", std::process::id())); - let header = "family,sketch,config,dist,param,cardinality"; - let points = ["1.0,1000", "1.2,1000", "1.0,100000"] - .map(|shape| format!("topk,{TOPK},rows=3 cols=1024,zipf,{shape}")); - for run in RUN_DIRS { - std::fs::create_dir_all(dir.join(run)).unwrap(); - let summary: String = points - .iter() - .map(|p| format!("{p},1000,0.9,precision_at_k\n")) - .collect(); - let curve: String = points.iter().map(|p| format!("{p},1000,0.9,0\n")).collect(); - std::fs::write( - dir.join(run).join("saturation.csv"), - format!("{header},n_sat,final_error,error_metric\n{summary}"), - ) - .unwrap(); - std::fs::write( - dir.join(run).join("saturation_curve.csv"), - format!("{header},n,seed_mean_error,seed_se\n{curve}"), - ) - .unwrap(); - } - let loaded = SaturationCurves::load(&dir); - std::fs::remove_dir_all(&dir).unwrap(); - let err = loaded.unwrap_err().to_string(); - assert!( - err.contains("full cross") && err.contains("(1.2, 100000.0)"), - "{err}" - ); - } - - #[test] - fn load_takes_a_point_in_both_runs_from_the_1e9_run() { - let dir = std::env::temp_dir().join(format!("rqe-saturation-{}", std::process::id())); - let header = "family,sketch,config,dist,param,cardinality"; - for (run, n_sat, last) in [ - (RUN_DIRS[0], "not_saturated", "1e7"), - (RUN_DIRS[1], "1e8", "1e9"), - ] { - std::fs::create_dir_all(dir.join(run)).unwrap(); - let point = "quantile,kll-percall,k=200,pareto,2.0,"; - std::fs::write( - dir.join(run).join("saturation.csv"), - format!( - "{header},n_sat,final_error,error_metric\n{point},{n_sat},0.01,mean_rank_err\n" - ), - ) - .unwrap(); - std::fs::write( - dir.join(run).join("saturation_curve.csv"), - format!("{header},n,seed_mean_error,seed_se\n{point},1000,0.05,0\n{point},{last},0.01,0\n"), - ) - .unwrap(); - std::fs::write( - dir.join(run).join("saturation_merge_curve.csv"), - format!("{header},n,shards,seed_mean_error,seed_se\n{point},1000,4,0.06,0\n"), - ) - .unwrap(); - } - let loaded = SaturationCurves::load(&dir).unwrap(); - std::fs::remove_dir_all(&dir).unwrap(); - let points = &loaded.points_by_sketch["kll-percall"]; - assert_eq!(points.len(), 1); - assert_eq!(points[0].n_sat, Some(1e8)); - assert_eq!(points[0].curve, vec![(1e3, 0.05, 0.0), (1e9, 0.01, 0.0)]); - assert_eq!(points[0].shape, MeasuredShape::Pareto { tail_index: 2.0 }); - assert_eq!(points[0].params, BTreeMap::from([("k".to_string(), 200.0)])); - } - - /// Top-k merges as one sketch when its heap holds m · k, so a study needs - /// no merge curves for it. - #[test] - fn load_needs_no_merge_curves_for_heap_topk() { - let dir = std::env::temp_dir().join(format!("rqe-topk-nomerge-{}", std::process::id())); - let header = "family,sketch,config,dist,param,cardinality"; - let point = format!("topk,{TOPK},rows=3 cols=1024,zipf,1.2,1000"); - for run in RUN_DIRS { - std::fs::create_dir_all(dir.join(run)).unwrap(); - std::fs::write( - dir.join(run).join("saturation.csv"), - format!( - "{header},n_sat,final_error,error_metric\n{point},1000,0.9,precision_at_k\n" - ), - ) - .unwrap(); - std::fs::write( - dir.join(run).join("saturation_curve.csv"), - format!("{header},n,seed_mean_error,seed_se\n{point},1000,0.9,0\n"), - ) - .unwrap(); - } - let loaded = SaturationCurves::load(&dir); - std::fs::remove_dir_all(&dir).unwrap(); - assert!(loaded.is_ok(), "{:?}", loaded.err()); - } - - /// A curve for another metric than the family's, such as DD's max error - /// before #175, is a stale study: refused at load, whatever the lookback. - #[test] - fn load_refuses_a_curve_for_another_metric() { - let dir = std::env::temp_dir().join(format!("rqe-stale-{}", std::process::id())); - let header = "family,sketch,config,dist,param,cardinality"; - let point = "quantile,dd,alpha=0.01,pareto,2.0,"; - for run in RUN_DIRS { - std::fs::create_dir_all(dir.join(run)).unwrap(); - std::fs::write( - dir.join(run).join("saturation.csv"), - format!( - "{header},n_sat,final_error,error_metric\n{point},1e8,0.01,max_relative_value_error\n" - ), - ) - .unwrap(); - std::fs::write( - dir.join(run).join("saturation_curve.csv"), - format!("{header},n,seed_mean_error,seed_se\n"), - ) - .unwrap(); - } - let loaded = SaturationCurves::load(&dir); - std::fs::remove_dir_all(&dir).unwrap(); - let error = loaded.unwrap_err().to_string(); - assert!( - error.contains("dd curve measured max_relative_value_error"), - "{error}" - ); - } - - const HYDRA_HEADER: &str = "variant,config,R,W,dataset,group_columns,schema_width,\ - records,fanned_mass,merge_shards,seed,err_mean,err_p50,err_p90,err_max,groups_scored,\ - err_max_cov_0.01,err_max_cov_0.05"; - - /// A `hydra_saturation.csv` row on `hydra_http` at rows=3 cols=1024: - /// `variant`, `grouping` (label names), records, merge shards, seed, then - /// err_max, err_max_cov_0.01 and err_max_cov_0.05 as written. - fn hydra_row( - variant: &str, - grouping: &str, - records: f64, - shards: u64, - seed: u64, - errors: [&str; 3], - ) -> String { - let [all, cov_1, cov_5] = errors; - format!( - "{variant},rows=3 cols=1024,3,1024,hydra_http,\"{grouping}\",2,{records},0,{shards},\ - {seed},0,0,0,{all},10,{cov_1},{cov_5}" - ) - } - - /// A saturation dir with empty curves and, when `rows` is `Some`, a - /// `hydra_saturation.csv` of them, loaded. `tag` keeps parallel tests' - /// dirs apart. - fn hydra_curves(tag: &str, rows: Option<&[String]>) -> SaturationCurves { - let csv = rows.map(|rows| format!("{HYDRA_HEADER}\n{}\n", rows.join("\n"))); - load_hydra_csv(tag, csv).unwrap() - } - - /// [`hydra_curves`] with `csv`, when `Some`, as `hydra_saturation.csv`. - fn load_hydra_csv(tag: &str, csv: Option) -> io::Result { - let dir = std::env::temp_dir().join(format!("rqe-hydra-{tag}-{}", std::process::id())); - for run in RUN_DIRS { - std::fs::create_dir_all(dir.join(run)).unwrap(); - let header = "family,sketch,config,dist,param,cardinality"; - std::fs::write( - dir.join(run).join("saturation.csv"), - format!("{header},n_sat,final_error,error_metric\n"), - ) - .unwrap(); - std::fs::write( - dir.join(run).join("saturation_curve.csv"), - format!("{header},n,seed_mean_error,seed_se\n"), - ) - .unwrap(); - } - if let Some(csv) = csv { - std::fs::write(dir.join(HYDRA_SATURATION), csv).unwrap(); - } - let loaded = SaturationCurves::load(&dir); - std::fs::remove_dir_all(&dir).unwrap(); - loaded - } - - /// A malformed `hydra_saturation.csv` fails the load, naming the - /// problem, rather than leaving Hydra silently without accuracy. - #[test] - fn a_malformed_hydra_csv_is_an_error() { - let row = hydra_row("hydra-hll", "service", 1e5, 1, 0, ["0.1"; 3]); - let error = - |tag: &str, csv: String| load_hydra_csv(tag, Some(csv)).unwrap_err().to_string(); - let no_cov = HYDRA_HEADER.trim_end_matches(",err_max_cov_0.05"); - let missing = error("missing", format!("{no_cov}\n{row}\n")); - assert!( - missing.contains("missing columns [\"err_max_cov_0.05\"]"), - "{missing}" - ); - let (short_row, _) = row.rsplit_once(',').unwrap(); - let short = error("short", format!("{HYDRA_HEADER}\n{short_row}\n")); - assert!( - short.contains("line 2: 17 fields, the header has 18"), - "{short}" - ); - let long = error("long", format!("{HYDRA_HEADER}\n{row},extra\n")); - assert!( - long.contains("line 2: 19 fields, the header has 18"), - "{long}" - ); - let wide = row.replace("cols=1024", "cols=wide"); - let config = error("config", format!("{HYDRA_HEADER}\n{wide}\n")); - assert!(config.contains("cols is not a number"), "{config}"); - } - - /// 5 services × 10 endpoints, measured as `hydra_http`. - fn hydra_facts() -> WorkloadFacts { - let mut facts = crate::test_support::service_endpoint_facts(); - let schema = crate::test_support::label_set(&["service", "endpoint"]); - facts.get_mut(METRIC).unwrap().hydra_dataset = Some(("hydra_http".into(), schema)); - facts - } - - fn tracker() -> AtomicCostEntry { - AtomicCostEntry { - sketch: crate::KEY_TRACKER_FAMILY.into(), - sketch_config: serde_json::json!({"params": {}}), - mem_bytes_per_instance: 10.0, - accuracy_metric: "relative_error".into(), - ..deployment(TOPK, 60_000).config - } - } - - /// A `sketch` grid at rows=3 cols=1024 over (service, endpoint), with - /// its key tracker. - fn hydra(sketch: &str, window_ms: u64) -> Deployment { - let base = deployment(sketch, window_ms); - Deployment { - capability: if sketch == "hydra-kll" { - Capability::Quantile - } else { - Capability::Cardinality - }, - grouping_labels: crate::test_support::label_set(&["service", "endpoint"]), - config: AtomicCostEntry { - mem_bytes_per_instance: 2_000.0, - ..base.config.clone() - }, - key_tracker: Some(tracker()), - ..base - } - } - - /// A distinct count by `grouping` over `lookback_ms` within 5%, covering - /// groups of share `covers` (`None`: every group). - fn distinct_by(grouping: &[&str], lookback_ms: u64, covers: Option) -> Raqe { - Raqe { - capability: Capability::Cardinality, - accuracy_sla: 0.05, - accuracy_covers_share: covers, - ..crate::test_support::quantile_by(grouping, lookback_ms, lookback_ms) - } - } - - #[test] - fn hydra_reads_the_column_its_coverage_selects() { - let rows = [hydra_row( - "hydra-hll", - "service", - 1e5, - 1, - 0, - ["0.3", "0.2", "0.1"], - )]; - let curves = hydra_curves("coverage", Some(&rows)); - let at = |covers| { - curves.accuracy( - &distinct_by(&["service"], 60_000, covers), - &hydra("hydra-hll", 60_000), - &hydra_facts(), - ) - }; - assert_eq!(at(None), Some(0.3)); - assert_eq!(at(Some(0.01)), Some(0.2)); - assert_eq!(at(Some(0.05)), Some(0.1)); - assert_eq!(at(Some(0.5)), Some(0.1)); - // Between the measured shares, the next stricter column; below the - // smallest, every group's. - assert_eq!(at(Some(0.03)), Some(0.2)); - assert_eq!(at(Some(0.001)), Some(0.3)); - } - - /// Error is taken to depend on shares, not N: the worst over the - /// measured records and seeds. An empty cell (no covered group scored) - /// is skipped; a column every row left empty is unknown. - #[test] - fn hydra_takes_the_worst_over_records_and_seeds() { - let rows = [ - hydra_row("hydra-hll", "service", 1e5, 1, 0, ["0.1", "", "0.1"]), - hydra_row("hydra-hll", "service", 1e6, 1, 0, ["0.15", "0.1", ""]), - hydra_row("hydra-hll", "service", 1e6, 1, 1, ["0.12", "", ""]), - ]; - let curves = hydra_curves("worst", Some(&rows)); - let at = |covers| { - curves.accuracy( - &distinct_by(&["service"], 60_000, covers), - &hydra("hydra-hll", 60_000), - &hydra_facts(), - ) - }; - assert_eq!(at(None), Some(0.15)); - assert_eq!(at(Some(0.01)), Some(0.1)); - assert_eq!(at(Some(0.05)), Some(0.1)); - let rows = [hydra_row( - "hydra-hll", - "service", - 1e5, - 1, - 0, - ["0.1", "0.1", ""], - )]; - let curves = hydra_curves("all_empty", Some(&rows)); - let at_5pct = curves.accuracy( - &distinct_by(&["service"], 60_000, Some(0.05)), - &hydra("hydra-hll", 60_000), - &hydra_facts(), - ); - assert_eq!(at_5pct, None); - } - - /// hydra-kll merges lossily: `L / x` windows read the worse of the - /// measured shard counts either side, and past the largest nothing. - /// hydra-hll merges exactly and reads one shard at any merge count. - #[test] - fn a_lossy_hydra_merge_reads_the_worse_bracketing_shard_count() { - let rows: Vec = ["hydra-kll", "hydra-hll"] - .iter() - .flat_map(|variant| { - [(1, "0.01"), (4, "0.02"), (16, "0.05")] - .map(|(shards, err)| hydra_row(variant, "service", 1e5, shards, 0, [err; 3])) - }) - .collect(); - let curves = hydra_curves("merges", Some(&rows)); - let at = |sketch: &str, merges: u64| { - let mut r = distinct_by(&["service"], 60_000 * merges, None); - if sketch == "hydra-kll" { - r.capability = Capability::Quantile; - } - curves.accuracy(&r, &hydra(sketch, 60_000), &hydra_facts()) - }; - assert_eq!(at("hydra-kll", 1), Some(0.01)); - assert_eq!(at("hydra-kll", 2), Some(0.02)); - assert_eq!(at("hydra-kll", 4), Some(0.02)); - assert_eq!(at("hydra-kll", 8), Some(0.05)); - assert_eq!(at("hydra-kll", 16), Some(0.05)); - assert_eq!(at("hydra-kll", 32), None); - assert_eq!(at("hydra-hll", 32), Some(0.01)); - } - - /// Hydra's accuracy is its own measurement or nothing: no file, no - /// dataset for the metric, or an unmeasured grouping leaves it unknown. - #[test] - fn without_a_hydra_measurement_no_hydra_deployment_has_accuracy() { - let r = distinct_by(&["service"], 60_000, None); - let d = hydra("hydra-hll", 60_000); - let rows = [hydra_row("hydra-hll", "service", 1e5, 1, 0, ["0.01"; 3])]; - let measured = hydra_curves("measured", Some(&rows)); - assert_eq!(measured.accuracy(&r, &d, &hydra_facts()), Some(0.01)); - let absent = hydra_curves("absent", None); - assert_eq!(absent.accuracy(&r, &d, &hydra_facts()), None); - let no_dataset = crate::test_support::service_endpoint_facts(); - assert_eq!(measured.accuracy(&r, &d, &no_dataset), None); - let by_endpoint = distinct_by(&["service", "endpoint"], 60_000, None); - assert_eq!(measured.accuracy(&by_endpoint, &d, &hydra_facts()), None); - } - - /// Distinct counts by (service) and (service, endpoint). Per-group HLL - /// at (service, endpoint) serves both, a roll-up for (service), at - /// 50 · 1000 B per window; one Hydra grid over the same schema holds - /// them in 2000 B plus a 10 B tracker key per group. Memory-priced, the - /// grid wins while its measured error clears the SLA, and loses - /// otherwise. - #[test] - fn a_hydra_grid_beats_per_group_hll_on_memory_only_when_accurate() { - use crate::candidates::build_all_candidates; - use crate::milp::minimize_usage_cost; - let facts = hydra_facts(); - let raqes = [ - distinct_by(&["service"], 60_000, None), - distinct_by(&["service", "endpoint"], 60_000, None), - ]; - let named = |sketch: &str, memory: f64| AtomicCostEntry { - mem_bytes_per_instance: memory, - accuracy_metric: "relative_error".into(), - measured_at: aqpbm_core::MeasuredAt { - dataset: Some("hydra_http".into()), - ..crate::test_support::measured_at() - }, - ..deployment(sketch, 60_000).config - }; - let costs = [ - named("hll", 1_000.0), - named("hydra-hll", 2_000.0), - tracker(), - ]; - let plan = |error: &str| { - let rows = ["service", "service,endpoint"] - .map(|grouping| hydra_row("hydra-hll", grouping, 1e5, 1, 0, [error; 3])); - let curves = hydra_curves(&format!("milp-{error}"), Some(&rows)); - // HLL passes everywhere; Hydra reads its measurement. - let accuracy = |r: &Raqe, d: &Deployment| { - if d.properties().answers_any_subgrouping { - curves.accuracy(r, d, &facts) - } else { - Some(0.0) - } - }; - let without = build_all_candidates(&raqes, &costs, &facts, false, &accuracy); - assert!(without.iter().all(|d| d.config.sketch == "hll")); - let candidates = build_all_candidates(&raqes, &costs, &facts, true, &accuracy); - let solved = - minimize_usage_cost(&raqes, &candidates, &facts, 0.0, 1.0, &accuracy, None, None) - .unwrap(); - solved - .mapping - .iter() - .map(|&d| candidates[d].clone()) - .collect::>() - }; - let accurate = plan("0.01"); - assert!(accurate.iter().all(|d| d.config.sketch == "hydra-hll")); - assert_eq!(accurate[0], accurate[1], "one grid serves both"); - let inaccurate = plan("0.2"); - assert!(inaccurate.iter().all(|d| d.config.sketch == "hll")); - } -} diff --git a/rqe-optimizer/src/theory.rs b/rqe-optimizer/src/theory.rs deleted file mode 100644 index e204d314..00000000 --- a/rqe-optimizer/src/theory.rs +++ /dev/null @@ -1,190 +0,0 @@ -//! Each sketch algorithm's published accuracy guarantee, at 95% confidence, -//! in the metric the optimizer checks it by. [`crate::saturation`] falls back -//! to it where the study measured nothing: past an unsaturated curve, past -//! the measured merge counts or N, or with no merge curve for a point. -//! -//! The bounds hold for any stream length, so they don't depend on N. They are -//! looser than the measured curves. - -use std::collections::BTreeMap; - -use aqpbm_core::MeasuredShape; - -/// Confidence the bounds are stated at. Rank and relative errors are -/// absolute, so their bounds are two-sided; CMS only overestimates, so the -/// top-k bound is one-sided. -pub const CONFIDENCE: f64 = 0.95; -/// The standard normal quantile at [`CONFIDENCE`], two-sided. -const Z: f64 = 1.96; - -/// The bound on `sketch`'s error with `params` on data shaped `shape`, -/// answering from `merges` merged instances, or `None` when the algorithm -/// has none at [`CONFIDENCE`]: -/// -/// - `kll-percall`: normalized rank error of one quantile. DataSketches -/// publishes `2.296 / k^0.9723` at 99%; KLL's error scales with -/// `sqrt(ln(2/δ))` (two-sided), which gives the 95% value. KLL merges without loss of -/// this guarantee. -/// - `dd`: relative value error `α` for an unbounded store (sketch-bench's -/// DDSketch collapses no buckets); merges exactly. The measured metric can -/// exceed `α` (zero-bucket values, rank conventions), which the caller's -/// floor at the last measurement covers. -/// - `hll`: relative error `z · 1.04 / sqrt(2^lg_k)`, `z` the two-sided -/// normal quantile; merges exactly. -/// - `cms-heap-topk-fastpath-vector2d`: precision@k on Zipf(θ) over `K` -/// keys, for every key at once. A CMS row overshoots a key by more than -/// `t` with probability at most `N / (w·t)` (Markov), and the `d` rows are -/// independent, so a non-top key overtakes top key `i` with probability at -/// most `(w·(p_i − p_{k+1}))^−d`. A union bound over the `K − k` non-top -/// keys, then over the counted top keys, keeps the total failure within -/// `1 − CONFIDENCE`: precision is the share of the top `k` counted, -/// taken from the most frequent down. It assumes the heap ranks keys by -/// their final CMS estimates. A merged answer has none: each shard's heap -/// keeps only its own top `k`. -pub fn bound( - sketch: &str, - params: &BTreeMap, - shape: MeasuredShape, - merges: u64, -) -> Option { - match sketch { - "kll-percall" => { - let k = params.get("k")?; - let at_99 = 2.296 / k.powf(0.9723); - // Two-sided tails: ln(2/δ). - Some(at_99 * ((2.0 / (1.0 - CONFIDENCE)).ln() / 200f64.ln()).sqrt()) - } - "dd" => params.get("alpha").copied(), - "hll" => { - let registers = 2f64.powf(*params.get("lg_k")?); - Some(Z * 1.04 / registers.sqrt()) - } - "cms-heap-topk-fastpath-vector2d" if merges <= 1 => { - let (rows, cols) = (*params.get("rows")?, *params.get("cols")?); - let MeasuredShape::Zipf { skew, keys } = shape else { - return None; - }; - let k = crate::params_topk_k(params.get("topk_k").copied()); - Some(topk_precision(skew, keys, rows, cols, k as usize)) - } - _ => None, - } -} - -/// The share of the top `k` keys of Zipf(`skew`) over `keys` keys that a CMS -/// with `rows` × `cols` keeps ranked, all at once with probability -/// [`CONFIDENCE`] (see [`bound`]). -fn topk_precision(skew: f64, keys: f64, rows: f64, cols: f64, k: usize) -> f64 { - if keys <= k as f64 { - return 1.0; - } - let total = zipf_normalizer(skew, keys); - let share = |rank: usize| (rank as f64).powf(-skew) / total; - let next = share(k + 1); - let mut failure = 0.0; - let mut ranked = 0; - for i in 1..=k { - failure += (keys - k as f64) * (cols * (share(i) - next)).powf(-rows); - if failure > 1.0 - CONFIDENCE { - break; - } - ranked += 1; - } - ranked as f64 / k as f64 -} - -/// `Σ_{n=1}^{K} n^−θ`: the first terms exactly, the rest by its integral -/// with the endpoint correction (Euler–Maclaurin), so large `K` costs O(1). -fn zipf_normalizer(skew: f64, keys: f64) -> f64 { - const EXACT: f64 = 1000.0; - let head = keys.min(EXACT) as usize; - let mut total: f64 = (1..=head).map(|n| (n as f64).powf(-skew)).sum(); - if keys > EXACT { - let integral = |x: f64| { - if (skew - 1.0).abs() < 1e-12 { - x.ln() - } else { - x.powf(1.0 - skew) / (1.0 - skew) - } - }; - total += integral(keys) - integral(EXACT) + (keys.powf(-skew) - EXACT.powf(-skew)) / 2.0; - } - total -} - -#[cfg(test)] -mod tests { - use super::*; - - fn params(pairs: &[(&str, f64)]) -> BTreeMap { - pairs.iter().map(|&(k, v)| (k.to_string(), v)).collect() - } - - const PARETO: MeasuredShape = MeasuredShape::Pareto { tail_index: 2.0 }; - - #[test] - fn kll_hll_and_dd_bounds_ignore_n_and_merges() { - let kll = bound("kll-percall", ¶ms(&[("k", 200.0)]), PARETO, 64).unwrap(); - // 1.32% at 99%, scaled by sqrt(ln 40 / ln 200). - assert!((kll - 0.01109).abs() < 1e-4, "{kll}"); - assert_eq!( - bound("dd", ¶ms(&[("alpha", 0.01)]), PARETO, 1000), - Some(0.01) - ); - let hll = bound("hll", ¶ms(&[("lg_k", 12.0)]), PARETO, 4).unwrap(); - assert!((hll - 1.96 * 1.04 / 64.0).abs() < 1e-12); - } - - #[test] - fn topk_precision_holds_for_every_key_at_once() { - let zipf = MeasuredShape::Zipf { - skew: 1.1, - keys: 1e4, - }; - let topk = |rows: f64, cols: f64, merges| { - bound( - "cms-heap-topk-fastpath-vector2d", - ¶ms(&[("rows", rows), ("cols", cols)]), - zipf, - merges, - ) - }; - let wide = topk(5.0, 16384.0, 1).unwrap(); - let narrow = topk(3.0, 256.0, 1).unwrap(); - // More rows and columns keep more of the top 32 ranked; the 32nd and - // 33rd keys of Zipf 1.1 are too close for any of them. - assert!(wide > narrow, "{wide} {narrow}"); - assert!(wide < 1.0 && wide > 0.0, "{wide}"); - // Fewer top keys to separate: a smaller k is easier. - let at_k = |k: f64| { - bound( - "cms-heap-topk-fastpath-vector2d", - ¶ms(&[("rows", 3.0), ("cols", 1024.0), ("topk_k", k)]), - zipf, - 1, - ) - .unwrap() - }; - assert!(at_k(10.0) >= at_k(100.0), "{} {}", at_k(10.0), at_k(100.0)); - assert_eq!(at_k(32.0), topk(3.0, 1024.0, 1).unwrap()); - // Merged heaps: no guarantee. - assert_eq!(topk(5.0, 16384.0, 4), None); - } - - #[test] - fn the_normalizer_matches_the_exact_sum() { - for (skew, keys) in [(1.1, 1e4), (0.5, 5e4), (1.0, 2e3), (2.0, 1e5)] { - let exact: f64 = (1..=keys as usize).map(|n| (n as f64).powf(-skew)).sum(); - let approx = zipf_normalizer(skew, keys); - assert!( - (approx - exact).abs() / exact < 1e-6, - "{skew} {keys}: {approx} {exact}" - ); - } - } - - #[test] - fn sketches_without_a_published_bound_have_none() { - assert_eq!(bound("univmon-topk", &BTreeMap::new(), PARETO, 1), None); - } -} diff --git a/rqe-optimizer/src/usage.rs b/rqe-optimizer/src/usage.rs deleted file mode 100644 index 349cd3b7..00000000 --- a/rqe-optimizer/src/usage.rs +++ /dev/null @@ -1,226 +0,0 @@ -//! A plan's resource use over time, its cost billed by use, and its batch -//! latency (`docs/rqe_optimizer_cost_model.md`, "Cost by use and batch -//! latency"). -//! -//! Ingest is a steady load on `⌈ρ⌉` workers per deployment, split by sample. -//! Each closed window is compacted (the workers' partials merged into one), -//! and each RAQE firing runs one query job (merge, then estimate) once the -//! newest window is compacted. A job uses at most one core: sketch-bench's -//! operations are single-threaded, their CPU time equal to their wall time. -//! CPU is elastic, so every job starts when it is ready. - -use std::collections::BTreeMap; - -use crate::analytical_cost_model::{self, BYTES_PER_GIB}; -use crate::{Deployment, Mapping, Millis, Raqe, WorkloadFacts}; - -/// One active deployment's steady load. -#[derive(Debug, Clone)] -pub struct DeploymentLoad { - /// Ingest CPU, vCPUs. - pub ingest_cpu: f64, - /// Open windows on every worker, bytes. - pub ingest_bytes: f64, - /// Closed windows kept for the longest lookback served, bytes. - pub storage_bytes: f64, - /// CPU-seconds to compact one closed window (0 with one worker). - pub compaction_secs: f64, - /// Bytes a compaction holds while it runs: the closed window's partial - /// copies (0 with one worker). - pub compaction_bytes: f64, - /// Windows close every slide. - pub slide_ms: Millis, -} - -/// One RAQE's query job, run every interval. -#[derive(Debug, Clone)] -pub struct QueryLoad { - /// Index into [`PlanLoad::deployments`]. - pub deployment: usize, - pub interval_ms: Millis, - /// Compaction of the newest window, then this job, on their own cores, ms - /// ([`analytical_cost_model::chain_ms`]). - pub chain_ms: f64, - /// Merge then estimate, CPU-seconds (one core at most). - pub work_secs: f64, - /// Merge accumulators and output, held while the job runs. - pub memory_bytes: f64, -} - -/// A plan as load over time. -#[derive(Debug, Clone)] -pub struct PlanLoad { - pub deployments: Vec, - /// Index-aligned with the plan's RAQEs. - pub queries: Vec, -} - -impl PlanLoad { - /// The load of `mapping` (one candidate index per RAQE). - pub fn new( - raqes: &[Raqe], - candidates: &[Deployment], - mapping: &Mapping, - facts: &WorkloadFacts, - ) -> PlanLoad { - let mut index: BTreeMap = BTreeMap::new(); - let mut deployments: Vec = Vec::new(); - let mut queries = Vec::with_capacity(raqes.len()); - for (raqe, &candidate) in raqes.iter().zip(mapping) { - let deployment = &candidates[candidate]; - let at = *index.entry(candidate).or_insert_with(|| { - let ingest = analytical_cost_model::ingest(deployment, facts); - let workers = analytical_cost_model::ingest_workers(deployment, facts); - deployments.push(DeploymentLoad { - ingest_cpu: ingest.cpu_secs_per_sec, - ingest_bytes: ingest.memory_bytes * workers, - storage_bytes: 0.0, - compaction_secs: analytical_cost_model::compaction_secs(deployment, facts), - compaction_bytes: analytical_cost_model::compaction_bytes(deployment, facts), - slide_ms: deployment.slide_ms, - }); - deployments.len() - 1 - }); - let storage = analytical_cost_model::storage_bytes(raqe, deployment, facts); - let load = &mut deployments[at]; - load.storage_bytes = load.storage_bytes.max(storage); - queries.push(QueryLoad { - deployment: at, - interval_ms: raqe.interval_ms, - chain_ms: analytical_cost_model::chain_ms(raqe, deployment, facts), - work_secs: analytical_cost_model::query_latency_ms(raqe, deployment, facts) - / 1000.0, - memory_bytes: analytical_cost_model::merge(raqe, deployment, facts).memory_bytes - + analytical_cost_model::query(raqe, deployment, facts).memory_bytes, - }); - } - PlanLoad { - deployments, - queries, - } - } - - /// Steady ingest CPU, vCPUs. - pub fn ingest_cpu(&self) -> f64 { - self.deployments.iter().map(|d| d.ingest_cpu).sum() - } - - /// Ingest and storage memory, held at all times, bytes. - pub fn static_bytes(&self) -> f64 { - self.deployments - .iter() - .map(|d| d.ingest_bytes + d.storage_bytes) - .sum() - } - - /// Mean CPU, vCPUs: ingest, compaction and queries, by use. - pub fn auc_cpu(&self) -> f64 { - self.ingest_cpu() - + self - .deployments - .iter() - .map(|d| d.compaction_secs / secs(d.slide_ms)) - .sum::() - + self - .queries - .iter() - .map(|q| q.work_secs / secs(q.interval_ms)) - .sum::() - } - - /// Mean memory, bytes: ingest and storage always; each compaction's - /// partial copies for its run on one core every slide; each query's memory - /// for its run on one core (`work_secs`) every interval. - pub fn auc_bytes(&self) -> f64 { - self.static_bytes() - + self - .deployments - .iter() - .map(|d| d.compaction_bytes * d.compaction_secs / secs(d.slide_ms)) - .sum::() - + self - .queries - .iter() - .map(|q| q.memory_bytes * q.work_secs / secs(q.interval_ms)) - .sum::() - } - - /// The longest chain, ms: the worst batch latency, since CPU is elastic. - pub fn longest_chain_ms(&self) -> f64 { - self.queries.iter().map(|q| q.chain_ms).fold(0.0, f64::max) - } -} - -/// A plan's cost billed by use, and its query latency. -#[derive(Debug, Clone)] -pub struct UsageCost { - /// Mean vCPUs. - pub cpu: f64, - /// Mean bytes. - pub bytes: f64, - /// `w_cpu · cpu + w_mem · GiB`. - pub value: f64, - /// Worst batch latency, ms: the longest chain, since CPU is elastic. - pub latency_ms: f64, -} - -/// `w_cpu · AUC(CPU) + w_mem · AUC(memory)` of `load`, and its latency. -pub fn usage_cost(load: &PlanLoad, w_cpu: f64, w_mem: f64) -> UsageCost { - let (cpu, bytes) = (load.auc_cpu(), load.auc_bytes()); - UsageCost { - cpu, - bytes, - value: w_cpu * cpu + w_mem * bytes / BYTES_PER_GIB, - latency_ms: load.longest_chain_ms(), - } -} - -fn secs(ms: Millis) -> f64 { - ms as f64 / 1000.0 -} - -#[cfg(test)] -mod tests { - use super::*; - - fn load(ingest_cpu: f64, compaction_secs: f64, queries: &[(Millis, f64)]) -> PlanLoad { - PlanLoad { - deployments: vec![DeploymentLoad { - ingest_cpu, - ingest_bytes: 10.0, - storage_bytes: 5.0, - compaction_secs, - compaction_bytes: 50.0, - slide_ms: 1_000, - }], - queries: queries - .iter() - .map(|&(interval_ms, work_secs)| QueryLoad { - deployment: 0, - interval_ms, - chain_ms: 1000.0 * (compaction_secs + work_secs), - work_secs, - memory_bytes: 100.0, - }) - .collect(), - } - } - - #[test] - fn cpu_and_memory_are_billed_by_use() { - // Ingest 0.5 vCPU; compaction 0.2 s holding 50 bytes each second; a - // 0.1 s query holding 100 bytes each second; 15 bytes always. - let plan = load(0.5, 0.2, &[(1_000, 0.1)]); - let cost = usage_cost(&plan, 1.0, 0.0); - assert!((cost.cpu - 0.8).abs() < 1e-12); - assert!((cost.bytes - (15.0 + 50.0 * 0.2 + 100.0 * 0.1)).abs() < 1e-12); - assert_eq!(cost.value, cost.cpu); - } - - #[test] - fn the_latency_is_the_longest_chain() { - // Compaction 0.2 s before every query; queries of 0.1 s and 0.5 s. - let plan = load(0.0, 0.2, &[(1_000, 0.1), (2_000, 0.5)]); - assert!((usage_cost(&plan, 1.0, 0.0).latency_ms - 700.0).abs() < 1e-9); - } -} diff --git a/rqe-optimizer/tests/mixed_grouping.rs b/rqe-optimizer/tests/mixed_grouping.rs deleted file mode 100644 index 0850ac2c..00000000 --- a/rqe-optimizer/tests/mixed_grouping.rs +++ /dev/null @@ -1,170 +0,0 @@ -//! A workload asking the same question at two groupings, `(service, -//! endpoint)` and `(service)`, solved end to end. A family whose states merge -//! across groups serves both from one fine deployment; one whose don't keeps a -//! deployment per grouping (sketch-bench#187). - -use std::collections::BTreeMap; - -use aqpbm_core::MeasuredAt; -use rqe_optimizer::candidates::build_all_candidates; -use rqe_optimizer::milp::{minimize, MilpSolution, Objective}; -use rqe_optimizer::{ - accuracy_key, validate_facts, AccuracyDirection, AtomicCostEntry, Capability, Deployment, - LabelSet, MetricFacts, Raqe, WorkloadFacts, -}; - -const METRIC: &str = "http_request_duration_seconds"; -const HOUR_MS: u64 = 3_600_000; -const MINUTE_MS: u64 = 60_000; - -fn labels(names: &[&str]) -> LabelSet { - names.iter().map(|name| name.to_string()).collect() -} - -/// 5 services × 10 endpoints × 600 pods = 30,000 series, scraped every 15 s. -fn facts() -> WorkloadFacts { - WorkloadFacts::from([( - METRIC.to_string(), - MetricFacts { - labels: labels(&["service", "endpoint", "pod"]), - scrape_interval_ms: 15_000, - cardinality: BTreeMap::from([ - (labels(&["service"]), 5), - (labels(&["service", "endpoint"]), 50), - (labels(&["service", "endpoint", "pod"]), 30_000), - ]), - value_range: None, - data_shape: BTreeMap::new(), - hydra_dataset: None, - }, - )]) -} - -fn raqe(id: &str, capability: Capability, grouping: &[&str], accuracy_sla: f64) -> Raqe { - Raqe { - id: id.into(), - capability, - lookback_ms: HOUR_MS, - interval_ms: MINUTE_MS, - metric: METRIC.into(), - spatial_filter: String::new(), - grouping_labels: labels(grouping), - accuracy_sla, - latency_sla_ms: None, - topk_k: None, - accuracy_covers_share: None, - } -} - -fn raqes() -> Vec { - vec![ - raqe( - "p99_by_endpoint", - Capability::Quantile, - &["service", "endpoint"], - 0.05, - ), - raqe("p99_by_service", Capability::Quantile, &["service"], 0.05), - raqe( - "top_by_endpoint", - Capability::TopKByValue, - &["service", "endpoint"], - 0.9, - ), - raqe("top_by_service", Capability::TopKByValue, &["service"], 0.9), - ] -} - -/// Ingest dominates merge and query, as it does in the measured table, so -/// sharing one deployment is cheaper than keeping two whenever it is allowed. -fn cost_row(sketch: &str, params: serde_json::Value) -> AtomicCostEntry { - let (accuracy_metric, _) = accuracy_key(sketch); - AtomicCostEntry { - sketch: sketch.into(), - sketch_config: serde_json::json!({ "params": params }), - mem_bytes_per_instance: 1_000.0, - insert_cpu_secs: 1e-6, - merge_cpu_secs: 1e-6, - query_cpu_secs: 1e-6, - query_accuracy: BTreeMap::from([(accuracy_metric.to_string(), 0.0)]), - accuracy_metric: accuracy_metric.into(), - measured_at: MeasuredAt { - items_per_instance: 1_000_000, - keys_per_instance: Some(10_000), - value_range: None, - merge_operand_items: None, - distribution: None, - dataset: None, - schema_width: None, - }, - } -} - -fn cost_table() -> Vec { - vec![ - cost_row("kll-percall", serde_json::json!({ "k": 200 })), - // A heap large enough for any merge, so heap size never decides the plan. - cost_row( - "cms-heap-topk-fastpath-vector2d", - serde_json::json!({ "rows": 3, "cols": 1024, "heap": 1u64 << 40 }), - ), - ] -} - -/// Every candidate meets every SLA, so the plan is decided by eligibility and -/// cost alone. -fn perfect_accuracy(_: &Raqe, deployment: &Deployment) -> Option { - match accuracy_key(&deployment.config.sketch).1 { - AccuracyDirection::LowerIsBetter => Some(0.0), - AccuracyDirection::HigherIsBetter => Some(1.0), - } -} - -fn solve(raqes: &[Raqe]) -> MilpSolution { - let facts = facts(); - validate_facts(raqes, &facts).expect("valid workload facts"); - let candidates = build_all_candidates(raqes, &cost_table(), &facts, false, &perfect_accuracy); - minimize( - raqes, - &candidates, - &facts, - Objective::default(), - &perfect_accuracy, - ) - .expect("feasible plan") -} - -/// The planned deployment serving the RAQE named `id`. -fn deployment_serving<'a>(solution: &'a MilpSolution, raqes: &[Raqe], id: &str) -> &'a Deployment { - let index = raqes.iter().position(|raqe| raqe.id == id).unwrap(); - &solution.deployments[solution.raqes[index].deployment].deployment -} - -#[test] -fn a_fine_kll_deployment_serves_both_quantile_raqes() { - let raqes = raqes(); - let solution = solve(&raqes); - let by_endpoint = deployment_serving(&solution, &raqes, "p99_by_endpoint"); - let by_service = deployment_serving(&solution, &raqes, "p99_by_service"); - assert_eq!( - by_endpoint, by_service, - "one KLL deployment should serve both groupings" - ); - assert_eq!( - by_endpoint.grouping_labels, - labels(&["service", "endpoint"]) - ); -} - -#[test] -fn top_k_never_rolls_up() { - let raqes = raqes(); - let solution = solve(&raqes); - let by_endpoint = deployment_serving(&solution, &raqes, "top_by_endpoint"); - let by_service = deployment_serving(&solution, &raqes, "top_by_service"); - assert_eq!( - by_endpoint.grouping_labels, - labels(&["service", "endpoint"]) - ); - assert_eq!(by_service.grouping_labels, labels(&["service"])); -} diff --git a/scripts/export_autosketch_eval_table.py b/scripts/export_autosketch_eval_table.py index f677cb74..b04c2017 100644 --- a/scripts/export_autosketch_eval_table.py +++ b/scripts/export_autosketch_eval_table.py @@ -250,7 +250,7 @@ def rqe_entry(q, curves, curves_big, sat, sat_big, merge): } SYNTHETIC_TEMPLATE_SETS = ["all", "classic", "multigroup"] -# Multi-grouping templates (sketch-bench docs/rqe_optimizer_hydra.md): two +# Multi-grouping templates (ASAPQuery rqe-optimizer/docs/rqe_optimizer_hydra.md): two # metrics with an ordered label schema, each value read at several groupings # on one stream, so a fine deployment can serve a coarse RQE (a roll-up) and # one Hydra grid can serve them all. A label has `cardinality` values, or, diff --git a/scripts/extract_autosketch_plans.py b/scripts/extract_autosketch_plans.py deleted file mode 100644 index dede6932..00000000 --- a/scripts/extract_autosketch_plans.py +++ /dev/null @@ -1,44 +0,0 @@ -#!/usr/bin/env python3 -"""Chosen plans from an autosketch_vs_asap run kept with its plans (`--runs 1`, -without `--no-chosen`), in the `plans/
    .plans.json.gz` format of -rqe-optimizer/results/autosketch-vs-asap-hydra/README.md: for each method and -weight setting the cheapest plan (`kind: "cheapest"`), then the plan at each -feasible SLA (`kind: "sla"`, `sla_ms`). - -Usage: extract_autosketch_plans.py RESULT.json OUT.plans.json.gz -""" -import gzip -import json -import sys - -FIELDS = ["objective", "cpu", "gib", "latency_ms", "active_deployments", "rolled_up_rqes", - "chosen"] - - -def plans(result): - cheapest = {} - for r in result["results"]: - key = (r["method"], r["weights"]) - if key not in cheapest or r["objective"] < cheapest[key]["objective"]: - cheapest[key] = r - out = [{"kind": "cheapest", "method": method, "weights": weights, - **{f: r.get(f) for f in FIELDS}} - for (method, weights), r in sorted(cheapest.items(), key=lambda kv: kv[0][::-1])] - out += [{"kind": "sla", "sla_ms": r.get("sla_ms"), "method": r["method"], - "weights": r["weights"], **{f: r.get(f) for f in FIELDS}} - for r in result["sla_results"] if r.get("objective") is not None] - return {"workload": result["workload"], "rqes": result["rqes"], - "cost_model": result["cost_model"], "plans": out} - - -def main(argv): - src, dst = argv - with open(src) as f: - result = json.load(f) - with gzip.open(dst, "wt") as f: - json.dump(plans(result), f) - return 0 - - -if __name__ == "__main__": - sys.exit(main(sys.argv[1:])) diff --git a/scripts/plot_autosketch_vs_asap_sla.py b/scripts/plot_autosketch_vs_asap_sla.py deleted file mode 100644 index b6b80c01..00000000 --- a/scripts/plot_autosketch_vs_asap_sla.py +++ /dev/null @@ -1,189 +0,0 @@ -#!/usr/bin/env python3 -"""Plot version 2 (a batch latency SLA) of the AutoSketch vs. ASAP evaluation -(ProjectASAP/ASAPQuery#777; sketch-bench docs/rqe_optimizer_cost_model.md, -"Cost by use and batch latency") from the `sla_results` of -`autosketch_vs_asap synthetic` runs. - -Writes, into OUT: - fig_cost_vs_sla.png one panel per workload (columns) and weight setting - (rows): cost by use (absolute, labeled) at each SLA, - ASAP, ASAP (no roll-ups, dashed) and PerQuery as - lines over the SLAs they can meet, - AutoSketch as a horizontal reference at its one plan's - cost, a filled marker where its latency meets the SLA - and a cross where it misses - summary_sla.md per workload, weights and SLA: each method's cost, - latency, CPU and GiB; AutoSketch's planning time as - search + measured benchmark; the roll-up ablation - (RQEs rolled up, deployments and cost with and - without roll-ups, saving %) - -Usage: scripts/plot_autosketch_vs_asap_sla.py OUT RESULT.json... -""" - -import json -import pathlib -import re -import sys - -import matplotlib - -matplotlib.use("Agg") -import matplotlib.pyplot as plt # noqa: E402 - -METHODS = ["asap", "asap-norollup", "perquery", "autosketch"] -LABELS = {"asap": "ASAP", "asap-norollup": "ASAP (no roll-ups)", - "perquery": "PerQuery-CostAware", "autosketch": "AutoSketch-Adapted"} -COLORS = {"asap": "#1f77b4", "asap-norollup": "#4a3aa7", "perquery": "#ff7f0e", - "autosketch": "#2ca02c"} -MARKERS = {"asap": "o", "asap-norollup": "^", "perquery": "o"} -LINESTYLES = {"asap-norollup": "--"} -UNITS = {"cpu": "vCPU", "fargate": "$/hour"} - - -def title(data): - """`mixed, r = 8` from `synthetic/templates=classic/shared=8/tp95`.""" - m = re.search(r"templates=(\w+)/shared=(\d+)(?:/metrics=(\d+))?", data["workload"]) - if not m: - return data["workload"] - name = {"classic": "mixed", "all": "mixed + multi-grouping", "multigroup": "multi-grouping"}.get(m.group(1), m.group(1)) - if m.group(3): - return f"{name}, m = {m.group(3)}" - if m.group(2) != "1": - return f"{name}, r = {m.group(2)}" - return name - - -def rows(data, method, weights): - return sorted((r for r in data["sla_results"] - if r["method"] == method and r["weights"] == weights), - key=lambda r: r["sla_ms"]) - - -def fmt(x, digits=3): - return "—" if x is None else f"{x:.{digits}g}" - - -def autosketch_planning_secs(data): - """Search + measured benchmark (scripts/autosketch_benchmark_time.py).""" - a = data["autosketch"] - measured = a.get("benchmark_secs_measured") - return None if measured is None else a["search_secs"] + measured - - -def figure(runs, out): - weights = [w["name"] for w in runs[0]["weights"]] - fig, axes = plt.subplots(len(weights), len(runs), figsize=(3.6 * len(runs), 3.0 * len(weights)), - squeeze=False) - for col, data in enumerate(runs): - for row, w in enumerate(weights): - ax = axes[row][col] - for m in ("asap", "asap-norollup", "perquery"): - pts = [(r["sla_ms"], r["objective"]) for r in rows(data, m, w) - if not r.get("infeasible") and "objective" in r] - if not pts: - continue - ax.plot(*zip(*pts), marker=MARKERS[m], markersize=4, linewidth=2, - linestyle=LINESTYLES.get(m, "-"), color=COLORS[m], label=LABELS[m]) - for x, y in pts: - ax.annotate(fmt(y), (x, y), textcoords="offset points", xytext=(3, 3), - fontsize=6, color="#333333") - auto = rows(data, "autosketch", w) - if auto: - cost = auto[0]["objective"] - ax.axhline(cost, color=COLORS["autosketch"], linewidth=1.5, linestyle="--", - label=LABELS["autosketch"]) - for r in auto: - ax.plot(r["sla_ms"], cost, marker="o" if r["meets_sla"] else "x", - markersize=8 if r["meets_sla"] else 7, - color=COLORS["autosketch"], linestyle="none") - ax.annotate(f"{fmt(cost)} (latency {fmt(auto[0]['latency_ms'])} ms)", - (auto[0]["sla_ms"], cost), textcoords="offset points", - xytext=(0, -11), fontsize=6, color="#333333") - ax.set_xscale("log") - ax.set_yscale("log") - ax.tick_params(labelsize=7) - ax.grid(True, which="major", color="#e5e5e5", linewidth=0.6) - if row == 0: - ax.set_title(f"{title(data)} ({data['rqes']} RQEs)", fontsize=9) - if row == len(weights) - 1: - ax.set_xlabel("batch latency SLA (ms)", fontsize=8) - if col == 0: - ax.set_ylabel(f"cost by use ({UNITS.get(w, w)})", fontsize=8) - handles, labels = axes[0][0].get_legend_handles_labels() - fig.legend(handles, labels, loc="upper center", ncol=4, fontsize=8, - title="AutoSketch: ● meets the SLA, × misses it", title_fontsize=7) - fig.tight_layout(rect=(0, 0, 1, 0.9)) - fig.savefig(out / "fig_cost_vs_sla.png", dpi=150) - plt.close(fig) - - -def summary(runs, out): - lines = ["# Version 2: cost by use under a batch latency SLA\n", - "Each method's cost by use (`w_cpu · AUC(CPU) + w_mem · AUC(memory)`) at each " - "SLA; ASAP and PerQuery plan for the SLA (the cheapest plan whose batch latency, " - "the longest chain, is at most it), AutoSketch's one plan ignores it.\n"] - for data in runs: - planning = autosketch_planning_secs(data) - lines.append(f"## {title(data)}: {data['rqes']} RQEs\n") - lines.append(f"Sanity violations: {len(data['sanity_violations'])}. Tightest feasible " - f"SLA: ASAP {fmt(data['frontier']['asap_tightest_bound_ms'])} ms, PerQuery " - f"{fmt(data['frontier']['perquery_tightest_bound_ms'])} ms. AutoSketch " - f"latency {fmt(data['frontier']['autosketch_latency_ms'])} ms; planning " - f"time (search + measured benchmark) {fmt(planning)} s.\n") - lines.append("| weights | SLA (ms) | method | cost | latency (ms) | CPU (vCPU) | GiB | " - "meets SLA |") - lines.append("|---|---|---|---|---|---|---|---|") - for w in [x["name"] for x in data["weights"]]: - for sla in data["sla_grid_ms"]: - for m in METHODS: - r = next((r for r in rows(data, m, w) if r["sla_ms"] == sla), None) - if r is None: - continue - if r.get("infeasible"): - lines.append(f"| {w} | {fmt(sla)} | {LABELS[m]} | infeasible (tightest " - f"{fmt(r['tightest_bound_ms'])} ms) |||||") - continue - meets = "yes" if m != "autosketch" or r["meets_sla"] else "**no**" - lines.append(f"| {w} | {fmt(sla)} | {LABELS[m]} | {fmt(r['objective'])} | " - f"{fmt(r['latency_ms'])} | {fmt(r['cpu'])} | {fmt(r['gib'])} | " - f"{meets} |") - lines.append("") - lines.extend(rollup_section(data)) - (out / "summary_sla.md").write_text("\n".join(lines)) - - -def rollup_section(data): - """The roll-up ablation at each weight setting and SLA where both ASAP and - ASAP (no roll-ups) have a plan; none without the ablation.""" - out = [] - for w in [x["name"] for x in data["weights"]]: - for sla in data["sla_grid_ms"]: - with_, without = (next((r for r in rows(data, m, w) if r["sla_ms"] == sla - and "objective" in r), None) - for m in ("asap", "asap-norollup")) - if with_ is None or without is None: - continue - saving = 100 * (without["objective"] - with_["objective"]) / without["objective"] - out.append(f"| {w} | {fmt(sla)} | {with_.get('rolled_up_rqes', '—')} | " - f"{with_['active_deployments']} | {without['active_deployments']} | " - f"{fmt(with_['objective'])} | {fmt(without['objective'])} | " - f"{saving:.1f}% |") - if not out: - return [] - return ["Roll-up ablation (ASAP vs. ASAP without roll-ups):\n", - "| weights | SLA (ms) | RQEs rolled up | deployments with | deployments without | " - "cost with | cost without | saving |", - "|---|---|---|---|---|---|---|---|", *out, ""] - - -def main(): - out = pathlib.Path(sys.argv[1]) - runs = [json.loads(pathlib.Path(p).read_text()) for p in sys.argv[2:]] - runs.sort(key=lambda d: d["rqes"]) - figure(runs, out) - summary(runs, out) - - -if __name__ == "__main__": - main() diff --git a/scripts/plot_autosketch_vs_asap_synthetic.py b/scripts/plot_autosketch_vs_asap_synthetic.py deleted file mode 100644 index 25a5667e..00000000 --- a/scripts/plot_autosketch_vs_asap_synthetic.py +++ /dev/null @@ -1,313 +0,0 @@ -#!/usr/bin/env python3 -"""Plot the synthetic AutoSketch vs. ASAP evaluation (ProjectASAP/ASAPQuery#777, -section 7) from the JSON files of `autosketch_vs_asap synthetic`. - -Every plan is priced by use (sketch-bench docs/rqe_optimizer_cost_model.md, -"Cost by use and batch latency"): w_cpu * AUC(CPU) + w_mem * AUC(memory), with -CPU elastic, and its query latency (the longest chain) is reported. ASAP and -PerQuery-CostAware are solved without a bound and for a sweep of latency -bounds (their cost-latency frontiers); AutoSketch-Adapted is one plan. - -Writes, next to the inputs: - fig_frontier.png cost vs. query latency, one column per workload, one - row per weight setting; ASAP, ASAP (no roll-ups) - and PerQuery frontiers as lines, AutoSketch as a - point; every point labeled with its cost - fig_rollup_ablation.png the roll-up ablation: per workload, ASAP's and ASAP - (no roll-ups)'s unbounded cost as grouped bars, one - panel per weight setting, absolute values labeled - fig_planning_time.png planning time vs. RQEs over the metrics dimension; - AutoSketch's is its search plus its measured - benchmark (60 s per probe only as a labeled reference) - summary_synthetic.md every workload and weight setting: each method's - unbounded plan, its cost at AutoSketch's latency, and - the frontier points; the roll-up ablation (RQEs - rolled up, deployments and cost with and without - roll-ups, saving %) - -Usage: scripts/plot_autosketch_vs_asap_synthetic.py DIR -""" - -import json -import pathlib -import re -import sys - -import matplotlib - -matplotlib.use("Agg") -import matplotlib.pyplot as plt # noqa: E402 - -METHODS = ["asap", "asap-norollup", "perquery", "autosketch"] -LABELS = {"asap": "ASAP", "asap-norollup": "ASAP (no roll-ups)", - "perquery": "PerQuery-CostAware", "autosketch": "AutoSketch-Adapted"} -# Categorical slots 1-3 and 7 of the dataviz reference palette, with marker -# shapes (and the ablation's dashes) as a second encoding. -COLORS = {"asap": "#2a78d6", "asap-norollup": "#4a3aa7", "perquery": "#eb6834", - "autosketch": "#1baf7a"} -MARKERS = {"asap": "o", "asap-norollup": "^", "perquery": "s", "autosketch": "D"} -LINESTYLES = {"asap-norollup": "--"} -UNITS = {"cpu": "vCPU", "fargate": "$/hour"} -WEIGHT_TITLES = {"cpu": "CPU only", "fargate": "Fargate prices"} - - -# Each template set's title (export_autosketch_eval_table.py's TEMPLATE_SETS). -TEMPLATE_TITLES = {"classic": "mixed", "all": "mixed + multi-grouping", "multigroup": "multi-grouping"} - - -def point(data): - """(templates, shared, metrics) of a result's workload name.""" - m = re.search(r"templates=(\w+)/shared=(\d+)(?:/metrics=(\d+))?", data["workload"]) - return m.group(1), int(m.group(2)), int(m.group(3) or 1) - - -def title(key): - templates, shared, metrics = key - name = TEMPLATE_TITLES.get(templates, templates) - if shared > 1: - return f"{name}, r = {shared}" - if metrics > 1: - return f"{name}, m = {metrics}" - return name - - -def load(directory): - runs = {} - for path in sorted(pathlib.Path(directory).glob("synthetic-*.json")): - data = json.loads(path.read_text()) - runs[point(data)] = data - return dict(sorted(runs.items())) - - -def records(data, method, weights): - return [r for r in data["results"] - if r["method"] == method and r["weights"] == weights and "objective" in r] - - -def unbounded(data, method, weights): - """The method's unbounded plan; None for results without the method.""" - return next((r for r in records(data, method, weights) if r["bound_ms"] is None), None) - - -def at_autosketch_latency(data, method, weights): - """The method's plan at the bound closest to AutoSketch's latency.""" - target = data["frontier"]["autosketch_latency_ms"] - bounded = [r for r in records(data, method, weights) if r["bound_ms"] is not None] - return min(bounded, key=lambda r: abs(r["bound_ms"] - target)) if bounded else None - - -def fmt(x, digits=3): - return "—" if x is None else f"{x:.{digits}g}" - - -def frontier(data, method, weights): - """Distinct (latency, cost) points of the method, by latency.""" - return sorted({(r["latency_ms"], r["objective"]) for r in records(data, method, weights)}) - - -def fig_frontier(runs, out): - weights = [w["name"] for w in next(iter(runs.values()))["weights"]] - fig, axes = plt.subplots(len(weights), len(runs), - figsize=(3.6 * len(runs), 3.2 * len(weights)), squeeze=False) - for col, (key, data) in enumerate(runs.items()): - for row, w in enumerate(weights): - ax = axes[row][col] - for m in METHODS: - pts = frontier(data, m, w) - if not pts: - continue - style = dict(color=COLORS[m], marker=MARKERS[m], markersize=4, linewidth=1.5, - label=LABELS[m]) - if m == "autosketch": - ax.plot(*zip(*pts), linestyle="none", **style) - else: - ax.plot(*zip(*pts), linestyle=LINESTYLES.get(m, "-"), **style) - for x, y in pts: - ax.annotate(fmt(y), (x, y), textcoords="offset points", xytext=(3, 3), - fontsize=5, color="#52514e") - ax.set_xscale("log") - ax.set_yscale("log") - ax.grid(True, which="major", color="#e5e4e0", linewidth=0.5) - ax.tick_params(labelsize=6) - if row == 0: - ax.set_title(f"{title(key)} ({data['rqes']} RQEs)", fontsize=8) - if row == len(weights) - 1: - ax.set_xlabel("query latency (ms)", fontsize=7) - if col == 0: - ax.set_ylabel(f"{WEIGHT_TITLES.get(w, w)}: cost by use ({UNITS.get(w, w)})", - fontsize=7) - handles, labels = axes[0][0].get_legend_handles_labels() - fig.legend(handles, labels, loc="upper center", ncol=4, fontsize=8) - fig.tight_layout(rect=(0, 0, 1, 0.93)) - fig.savefig(out / "fig_frontier.png", dpi=150) - plt.close(fig) - - -def fig_rollup_ablation(runs, out): - """Per workload with the ablation: ASAP's and ASAP (no roll-ups)'s unbounded - cost, grouped bars, one panel per weight setting.""" - runs = {k: d for k, d in runs.items() - if any(unbounded(d, "asap-norollup", w["name"]) for w in d["weights"])} - if not runs: - return - weights = [w["name"] for w in next(iter(runs.values()))["weights"]] - fig, axes = plt.subplots(1, len(weights), squeeze=False, - figsize=((1.2 * len(runs) + 1.0) * len(weights), 3.6)) - width = 0.38 - for col, w in enumerate(weights): - ax = axes[0][col] - for i, m in enumerate(("asap", "asap-norollup")): - xs, ys = [], [] - for x, data in enumerate(runs.values()): - r = unbounded(data, m, w) - if r is not None: - xs.append(x + (i - 0.5) * width) - ys.append(r["objective"]) - bars = ax.bar(xs, ys, width=width * 0.95, color=COLORS[m], label=LABELS[m], - hatch="//" if m == "asap-norollup" else None, edgecolor="white", - linewidth=0.5) - ax.bar_label(bars, labels=[fmt(y) for y in ys], fontsize=6, color="#52514e", - padding=2) - ax.set_xticks(range(len(runs))) - ax.set_xticklabels([f"{title(k).replace(', ', chr(10))}\n({d['rqes']} RQEs)" - for k, d in runs.items()], - fontsize=6) - ax.set_ylabel(f"cost by use, unbounded ({UNITS.get(w, w)})", fontsize=7) - ax.set_title(WEIGHT_TITLES.get(w, w), fontsize=8) - ax.tick_params(axis="y", labelsize=6) - ax.grid(True, axis="y", color="#e5e4e0", linewidth=0.5) - ax.set_axisbelow(True) - ax.margins(y=0.12) - handles, labels = axes[0][0].get_legend_handles_labels() - fig.legend(handles, labels, loc="upper center", ncol=2, fontsize=8) - fig.tight_layout(rect=(0, 0, 1, 0.9)) - fig.savefig(out / "fig_rollup_ablation.png", dpi=150) - plt.close(fig) - - -def fig_planning_time(runs, out): - """Over the metrics dimension (shared = 1) of the `all` set, CPU-only - weights.""" - series = {"asap": [], "perquery": [], "search": [], "measured": [], "paper": []} - for (templates, shared, _), data in runs.items(): - if templates != "all" or shared != 1: - continue - n = data["rqes"] - a = data["autosketch"] - auto = unbounded(data, "autosketch", "cpu") - series["asap"].append((n, unbounded(data, "asap", "cpu")["planning_secs"])) - series["perquery"].append((n, unbounded(data, "perquery", "cpu")["planning_secs"])) - series["search"].append((n, auto["planning_secs"])) - if "benchmark_secs_measured" in a: - series["measured"].append((n, auto["planning_secs"] + a["benchmark_secs_measured"])) - series["paper"].append((n, auto["planning_secs"] + a["benchmark_secs_paper_rate"])) - fig, ax = plt.subplots(figsize=(6.0, 3.8)) - lines = [ - ("asap", "ASAP (candidates + MILP)", COLORS["asap"], MARKERS["asap"], "-"), - ("perquery", "PerQuery-CostAware", COLORS["perquery"], MARKERS["perquery"], "-"), - ("measured", "AutoSketch-Adapted: search + measured benchmark", COLORS["autosketch"], - MARKERS["autosketch"], "-"), - ("search", "AutoSketch-Adapted: search only", COLORS["autosketch"], - MARKERS["autosketch"], ":"), - ("paper", "reference: AutoSketch search + 60 s per probe (paper rate)", "#898781", - "x", "--"), - ] - for key, label, color, marker, style in lines: - pts = sorted(series[key]) - if not pts: - continue - ax.plot(*zip(*pts), color=color, marker=marker, markersize=4, linestyle=style, - linewidth=1.5, label=label) - for x, y in pts: - ax.annotate(fmt(y) + " s", (x, y), textcoords="offset points", xytext=(3, 3), - fontsize=5, color="#52514e") - ax.set_xscale("log") - ax.set_yscale("log") - ax.grid(True, which="major", color="#e5e4e0", linewidth=0.5) - ax.set_xlabel(f"RQEs ({TEMPLATE_TITLES['all']}, m = 1, 8, 16 metrics)", fontsize=8) - ax.set_ylabel("planning time (s)", fontsize=8) - ax.tick_params(labelsize=7) - ax.legend(fontsize=6, loc="upper center", bbox_to_anchor=(0.5, -0.2), ncol=2) - fig.tight_layout() - fig.savefig(out / "fig_planning_time.png", dpi=150, bbox_inches="tight") - plt.close(fig) - - -def summary(runs, out): - lines = ["# Synthetic workload: AutoSketch vs. ASAP, cost by use\n", - "Cost by use: `w_cpu · AUC(CPU) + w_mem · AUC(memory)` (CPU only, in vCPU; " - "Fargate prices, in $/hour). Latency: a plan's query latency (its longest " - "chain, compaction then query, CPU elastic), and the median over its RQEs. " - "AutoSketch's planning time is its search plus its measured benchmark.\n"] - for key, data in runs.items(): - a = data["autosketch"] - lines.append(f"## {title(key)}\n") - lines.append( - f"{data['rqes']} RQEs on {data['streams']} streams; dropped: " - f"{sum(len(v) for v in data['dropped_unservable'].values())}; sanity violations: " - f"{len(data['sanity_violations'])}. AutoSketch: {a['probes']} probes, " - f"{a['distinct_probes']} distinct (metric, config); measured benchmark " - f"{fmt(a.get('benchmark_secs_measured'))} s at N = {fmt(a.get('benchmark_n'))} " - f"(paper rate {fmt(a['benchmark_secs_paper_rate'])} s). AutoSketch latency " - f"{fmt(data['frontier']['autosketch_latency_ms'])} ms; tightest feasible bound " - f"{fmt(data['frontier']['asap_tightest_bound_ms'])} ms.\n") - lines.append("| weights | method | cost (unbounded) | CPU (vCPU) | GiB | latency (ms) | " - "median latency (ms) | planning (s) | cost at AutoSketch's latency |") - lines.append("|---|---|---|---|---|---|---|---|---|") - for w in [x["name"] for x in data["weights"]]: - for m in METHODS: - r = unbounded(data, m, w) - if r is None: - continue - planning = r.get("planning_secs") - if m == "autosketch" and "benchmark_secs_measured" in a: - planning = planning + a["benchmark_secs_measured"] - at = at_autosketch_latency(data, m, w) - at_cost = r["objective"] if m == "autosketch" else (at and at["objective"]) - lines.append( - f"| {w} | {LABELS[m]} | {fmt(r['objective'])} | {fmt(r['cpu'])} | " - f"{fmt(r['gib'])} | {fmt(r['latency_ms'])} | {fmt(r['median_latency_ms'])} | " - f"{fmt(planning)} | {fmt(at_cost)} |") - lines.append("") - lines.append("Frontier points (latency ms, cost), by weights:\n") - for w in [x["name"] for x in data["weights"]]: - for m in ("asap", "asap-norollup", "perquery"): - pts = ", ".join(f"({fmt(x)}, {fmt(y)})" for x, y in frontier(data, m, w)) - if pts: - lines.append(f"- {w}, {LABELS[m]}: {pts}") - lines.append("") - lines.extend(rollup_section(data, unbounded)) - (out / "summary_synthetic.md").write_text("\n".join(lines)) - - -def rollup_section(data, plan): - """The roll-up ablation's markdown lines: per weight setting, `plan(data, - method, weights)` with and without roll-ups; none without the ablation.""" - rows = [] - for w in [x["name"] for x in data["weights"]]: - with_, without = plan(data, "asap", w), plan(data, "asap-norollup", w) - if with_ is None or without is None: - continue - saving = 100 * (without["objective"] - with_["objective"]) / without["objective"] - rows.append(f"| {w} | {with_.get('rolled_up_rqes', '—')} | " - f"{with_['active_deployments']} | {without['active_deployments']} | " - f"{fmt(with_['objective'])} | {fmt(without['objective'])} | {saving:.1f}% |") - if not rows: - return [] - return ["Roll-up ablation (ASAP vs. ASAP without roll-ups):\n", - "| weights | RQEs rolled up | deployments with | deployments without | " - "cost with | cost without | saving |", - "|---|---|---|---|---|---|---|", *rows, ""] - - -def main(): - out = pathlib.Path(sys.argv[1]) - runs = load(out) - fig_frontier(runs, out) - fig_rollup_ablation(runs, out) - fig_planning_time(runs, out) - summary(runs, out) - - -if __name__ == "__main__": - main() diff --git a/scripts/plot_autosketch_vs_asap_targets.py b/scripts/plot_autosketch_vs_asap_targets.py deleted file mode 100644 index a4601083..00000000 --- a/scripts/plot_autosketch_vs_asap_targets.py +++ /dev/null @@ -1,149 +0,0 @@ -#!/usr/bin/env python3 -"""AutoSketch vs. ASAP across accuracy targets (90%, 95%, 99%). - -Reads `autosketch_vs_asap` results named `
    -t.json`, with level -`acc90`, `p95` (the committed 95% runs) or `acc99`, and writes into OUT: - - fig_cost_vs_target.png each method's cheapest plan (absolute cost) vs. - the accuracy target, one panel per workload - (columns) and weight setting (rows) - fig_cost_vs_target_at_autosketch_latency.png - the same, every method held to AutoSketch's - batch latency at that target - summary_targets.md per workload, weights and target: each method's - cheapest cost, latency and deployments, RQEs - dropped, roll-ups - -Usage: scripts/plot_autosketch_vs_asap_targets.py OUT RESULT.json... -""" - -import json -import os -import re -import sys - -import matplotlib - -matplotlib.use("Agg") -import matplotlib.pyplot as plt # noqa: E402 - -LEVELS = {"acc90": 90, "p95": 95, "acc95": 95, "acc99": 99} -METHODS = ["asap", "asap-norollup", "perquery", "autosketch"] -LABELS = {"asap": "ASAP", "asap-norollup": "ASAP (no roll-ups)", - "perquery": "PerQuery-CostAware", "autosketch": "AutoSketch-Adapted"} -COLORS = {"asap": "#1f77b4", "asap-norollup": "#4a3aa7", "perquery": "#ff7f0e", - "autosketch": "#2ca02c"} -STYLES = {"asap-norollup": "--"} -MARKERS = {"asap": "o", "asap-norollup": "^", "perquery": "s", "autosketch": "D"} -UNITS = {"cpu": "vCPU", "fargate": "$/hour"} -WEIGHTS = ["cpu", "fargate"] -TEMPLATES = {"classic": "mixed", "multigroup": "multi-grouping", - "all": "mixed + multi-grouping"} -NAME = re.compile(r"synthetic-templates(\w+)-shared(\d+)(?:-metrics(\d+))?-t(\w+)\.json$") - - -def workload_title(templates, shared, metrics): - parts = [TEMPLATES.get(templates, templates)] - if shared > 1: - parts.append(f"{shared} users") - if metrics > 1: - parts.append(f"{metrics} metrics") - return ", ".join(parts) - - -def cheapest(data, method, weights, max_latency=None): - plans = [r for r in data["results"] - if r["method"] == method and r["weights"] == weights and "objective" in r - and (max_latency is None or r["latency_ms"] <= max_latency + 1e-6)] - return min(plans, key=lambda r: r["objective"]) if plans else None - - -def load(paths): - """{(templates, shared, metrics): {accuracy %: result}}.""" - runs = {} - for path in paths: - m = NAME.search(os.path.basename(path)) - if not m or m.group(4) not in LEVELS: - continue - key = (m.group(1), int(m.group(2)), int(m.group(3) or 1)) - with open(path) as f: - runs.setdefault(key, {})[LEVELS[m.group(4)]] = json.load(f) - return runs - - -def plot(runs, keys, out, at_autosketch_latency): - fig, axes = plt.subplots(len(WEIGHTS), len(keys), - figsize=(3.2 * len(keys), 2.8 * len(WEIGHTS)), squeeze=False) - for j, key in enumerate(keys): - for i, weights in enumerate(WEIGHTS): - ax = axes[i, j] - for method in METHODS: - xs, ys = [], [] - for acc, data in sorted(runs[key].items()): - limit = (data["frontier"]["autosketch_latency_ms"] - if at_autosketch_latency else None) - plan = cheapest(data, method, weights, limit) - if plan: - xs.append(acc) - ys.append(plan["objective"]) - ax.plot(xs, ys, STYLES.get(method, "-"), color=COLORS[method], - marker=MARKERS[method], ms=4, lw=1.2, label=LABELS[method]) - for x, y in zip(xs, ys): - ax.annotate(f"{y:.3g}", (x, y), xytext=(3, 2), textcoords="offset points", - fontsize=6, color=COLORS[method]) - ax.set_yscale("log") - ax.set_xticks([90, 95, 99]) - ax.set_xticklabels(["90%", "95%", "99%"]) - ax.grid(alpha=0.3, lw=0.3, which="both") - if i == 0: - ax.set_title(f"{workload_title(*key)} ({next(iter(runs[key].values()))['rqes']} RQEs)", - fontsize=8) - if i == len(WEIGHTS) - 1: - ax.set_xlabel("accuracy target") - if j == 0: - ax.set_ylabel(f"cost by use ({UNITS[weights]})") - handles, labels = axes[0, 0].get_legend_handles_labels() - fig.legend(handles, labels, loc="upper center", ncol=len(METHODS), fontsize=8, frameon=False) - fig.tight_layout(rect=(0, 0, 1, 0.94)) - fig.savefig(out, dpi=150) - plt.close(fig) - - -def summary(runs, out): - lines = ["# Accuracy target sweep", "", - "Cheapest plan of each method (no latency bound). Cost by use: CPU only in " - "vCPU, Fargate in $/hour. `dropped`: RQEs ASAP could not serve at the target.", ""] - for key in sorted(runs): - lines += [f"## {workload_title(*key)}", "", - "| Target | Weights | RQEs | dropped | ASAP (latency, deployments, rolled up) " - "| ASAP no roll-ups | PerQuery | AutoSketch (latency) |", - "|---|---|---|---|---|---|---|---|"] - for acc, data in sorted(runs[key].items()): - for weights in WEIGHTS: - p = {m: cheapest(data, m, weights) for m in METHODS} - a, au = p["asap"], p["autosketch"] - cell = lambda r: f"{r['objective']:.4g}" if r else "—" - lines.append( - f"| {acc}% | {weights} | {data['rqes']} | " - f"{len(data['dropped_unservable']['asap'])} | " - f"{cell(a)} ({a['latency_ms'] / 1e3:.2f} s, {a['active_deployments']}, " - f"{a.get('rolled_up_rqes', 0)}) | {cell(p['asap-norollup'])} | " - f"{cell(p['perquery'])} | {cell(au)} ({au['latency_ms']:.0f} ms) |") - lines.append("") - with open(out, "w") as f: - f.write("\n".join(lines)) - - -def main(): - out, paths = sys.argv[1], sys.argv[2:] - runs = load(paths) - # One column per template set at its base point; the scaled points are in - # the summary. - keys = [k for k in [("classic", 1, 1), ("multigroup", 1, 1), ("all", 1, 1)] if k in runs] - plot(runs, keys, os.path.join(out, "fig_cost_vs_target.png"), False) - plot(runs, keys, os.path.join(out, "fig_cost_vs_target_at_autosketch_latency.png"), True) - summary(runs, os.path.join(out, "summary_targets.md")) - - -if __name__ == "__main__": - main() diff --git a/scripts/plot_sec63_paper.py b/scripts/plot_sec63_paper.py deleted file mode 100644 index be84883f..00000000 --- a/scripts/plot_sec63_paper.py +++ /dev/null @@ -1,369 +0,0 @@ -#!/usr/bin/env python3 -"""The paper's §6.3 figures, from the committed AutoSketch-vs-ASAP results. - -Writes into OUT (PDF and PNG each): - sec63_cost_bars each method's cost at AutoSketch-Adapted's batch latency - (CPU only, log scale), per workload (panels) at base, - 8 users and 16 metrics; a baseline's bar also names how - many times ASAP's cost it is. Drawn at a figure*'s - 7 in; sec63_cost_bars_column stacks the panels at one - column's 3.4 in - sec63_frontier cost vs. batch latency (ASAP and PerQuery swept over - latency bounds; AutoSketch one point), CPU and Fargate - sec63_rollups the roll-up ablation: ASAP's cheapest plan with and - without roll-ups (CPU only, log scale) - -Every cost is absolute (vCPU or $/hour). - -Usage: scripts/plot_sec63_paper.py RESULTS_DIR OUT - RESULTS_DIR: rqe-optimizer/results/autosketch-vs-asap-hydra -""" - -import json -import os -import sys - -import matplotlib -import matplotlib.ticker - -matplotlib.use("Agg") -import matplotlib.pyplot as plt # noqa: E402 - -plt.rcParams.update({"font.size": 8, "pdf.fonttype": 42}) - -WORKLOADS = [("classic", "Aggregations"), ("multigroup", "Roll-ups"), - ("all", "Aggregations + roll-ups")] -VARIANTS = [("shared1", "base"), ("shared8", "8 users"), ("shared1-metrics16", "16 metrics")] -METHODS = [("autosketch", "AutoSketch-Adapted", "C2"), ("perquery", "PerQuery", "C1"), - ("asap", "ASAP", "C0")] - - -def load(results, table): - with open(os.path.join(results, f"synthetic-templates{table}-tp95.json")) as f: - return json.load(f) - - -def plans(data, method, weights): - return [r for r in data["results"] - if r["weights"] == weights and r["method"] == method and "objective" in r] - - -def cheapest(data, method, weights="cpu", max_latency_ms=None): - return min((r for r in plans(data, method, weights) - if max_latency_ms is None or r["latency_ms"] <= max_latency_ms + 1e-6), - key=lambda r: r["objective"])["objective"] - - -def save(fig, out, name, tight=False): - """PDF and PNG; `tight` trims the page to the drawing (2 pt margin).""" - extra = {"bbox_inches": "tight", "pad_inches": 0.02} if tight else {} - fig.savefig(os.path.join(out, f"{name}.pdf"), **extra) - fig.savefig(os.path.join(out, f"{name}.png"), dpi=150, **extra) - plt.close(fig) - - -def short(cost): - """2.7k, 161, 4.5, 0.32: three significant figures at most.""" - if cost >= 1000: - return f"{cost / 1000:.3g}k" - return f"{cost:.3g}" if cost >= 1 else f"{cost:.2g}" - - -# Darker shades than matplotlib's defaults, so white labels inside the bars -# read clearly. -WIDE_COLORS = {"autosketch": "#2e7d32", "perquery": "#d35400", "asap": "#1f5fa8"} - - -def cost_bars_wide(results, out): - """The figure* version (7.1 in, drawn at print size). ASAP's cost sits - right on top of its bar; each baseline's multiple of it sits inside the - baseline's bar in white, or just above the bar when the bar is too short - to hold it. The page is trimmed to the drawing.""" - sizes = {"font.size": 9.5, "axes.titlesize": 11, "axes.labelsize": 10.5, - "xtick.labelsize": 9.5, "ytick.labelsize": 9.5, "legend.fontsize": 10.5} - label_pt = 8.5 - with plt.rc_context(sizes): - fig, axes = plt.subplots(1, len(WORKLOADS), figsize=(7.1, 3.0)) - width, gap = 0.3, 1.15 - labels_to_place = [] - for ax, (templates, title) in zip(axes, WORKLOADS): - ticks = [] - for i, (variant, name) in enumerate(VARIANTS): - data = load(results, f"{templates}-{variant}") - limit = data["frontier"]["autosketch_latency_ms"] - cost = {m: cheapest(data, m, max_latency_ms=limit) for m, _, _ in METHODS} - for k, (method, label, _) in enumerate(METHODS): - x = i * gap + (k - 1) * width - ax.bar(x, cost[method], width * 0.95, color=WIDE_COLORS[method], - label=label if i == 0 else None) - if method == "asap": - # Vertical, so it stays within its bar's width. - ax.text(x, cost[method] * 1.08, short(cost[method]), ha="center", - va="bottom", rotation=90, fontsize=label_pt, weight="bold", - color=WIDE_COLORS["asap"]) - else: - ratio = cost[method] / cost["asap"] - text = f"{ratio:.0f}×" if ratio >= 10 else f"{ratio:.1f}×" - labels_to_place.append((ax, x, cost[method], text, - WIDE_COLORS[method])) - ticks.append(f"{name}\n{data['rqes']:,}") - ax.set_yscale("log") - ax.set_xticks([i * gap for i in range(len(VARIANTS))]) - ax.set_xticklabels(ticks, fontsize=8.5, linespacing=1.0) - ax.set_xlim(-0.6, (len(VARIANTS) - 1) * gap + 0.6) - ax.set_ylim(top=ax.get_ylim()[1] * 4) - ax.yaxis.set_major_locator(matplotlib.ticker.LogLocator(numticks=4)) - ax.grid(axis="y", alpha=0.3, lw=0.4) - ax.tick_params(axis="x", length=0) - ax.set_title(title) - ax.set_xlabel("workload scale (# RQEs)") - axes[0].set_ylabel("cost (vCPU)") - handles, names = axes[0].get_legend_handles_labels() - fig.legend(handles, names, loc="upper center", ncol=len(METHODS), frameon=False, - handlelength=1.2, columnspacing=1.2, bbox_to_anchor=(0.5, 1.02)) - fig.tight_layout(rect=(0, 0, 1, 0.9), w_pad=0.3) - # With the axes final: a label goes inside its bar, written - # vertically in white, if the bar is tall enough (in points) to hold - # it; else vertically just above the bar, in the bar's colour. - fig.canvas.draw() - for ax, x, top, text, color in labels_to_place: - bottom = ax.get_ylim()[0] - to_pt = ax.transData.transform - height_pt = (to_pt((x, top))[1] - to_pt((x, bottom))[1]) * 72 / fig.dpi - if height_pt > label_pt * 0.62 * len(text) + 6: - ax.text(x, top / 1.12, text, ha="center", va="top", rotation=90, - fontsize=label_pt, weight="bold", color="white") - else: - ax.text(x, top * 1.08, text, ha="center", va="bottom", rotation=90, - fontsize=label_pt, weight="bold", color=color) - save(fig, out, "sec63_cost_bars_wide", tight=True) - - -# The paper's names: the system, its no-sharing ablation, and the baseline. -SINGLE_METHODS = [("asap", "LiteOlly", "#1f5fa8"), - ("perquery", "LiteOllyPerQuery", "#8fb3de"), - ("autosketch", "AutoSketch baseline", "#2e7d32")] -SINGLE_WORKLOADS = [("classic", "W-RangeHeavy"), ("multigroup", "W-RollupHeavy"), - ("all", "W-Mixed")] -SINGLE_VARIANTS = [("shared1", "base"), ("shared8", "8U"), ("shared1-metrics16", "16M")] - - -def cost_bars_single(results, out, weights="cpu"): - """One set of axes at one column's width (3.4 in, drawn at print size): - every workload and scale as a group of three bars (LiteOlly, - LiteOllyPerQuery, AutoSketch baseline), each at AutoSketch's batch - latency, log scale, under `weights` (CPU only, or Fargate's prices). LiteOlly's cost sits on its bar; each other - bar names how many times LiteOlly's cost it is, inside the bar in white - when it fits, else just above it. The page is trimmed to the drawing.""" - sizes = {"font.size": 8, "axes.labelsize": 8.5, "xtick.labelsize": 7.5, - "ytick.labelsize": 8, "legend.fontsize": 8} - label_pt = 6.5 - with plt.rc_context(sizes): - fig, ax = plt.subplots(figsize=(3.4, 2.5)) - width, gap, block = 0.32, 1.02, 0.35 # bar, group spacing, extra between workloads - x0, ticks, ticklabels, spans, to_place = 0.0, [], [], [], [] - for templates, title in SINGLE_WORKLOADS: - start = x0 - for variant, name in SINGLE_VARIANTS: - data = load(results, f"{templates}-{variant}") - limit = data["frontier"]["autosketch_latency_ms"] - cost = {m: cheapest(data, m, weights, max_latency_ms=limit) - for m, _, _ in SINGLE_METHODS} - for k, (method, label, color) in enumerate(SINGLE_METHODS): - x = x0 + (k - 1) * width - ax.bar(x, cost[method], width * 0.92, color=color, - label=label if not ticks else None) - if method == "asap": - ax.text(x, cost[method] * 1.1, short(cost[method]), ha="center", - va="bottom", rotation=90, fontsize=label_pt, weight="bold", - color=color) - else: - ratio = cost[method] / cost["asap"] - text = f"{ratio:.0f}×" if ratio >= 10 else f"{ratio:.1f}×" - to_place.append((x, cost[method], text, color)) - ticks.append(x0) - ticklabels.append(name) - x0 += gap - spans.append((start, x0 - gap, title)) - x0 += block - ax.set_yscale("log") - ax.set_xticks(ticks) - ax.set_xticklabels(ticklabels, rotation=0) - ax.set_xlim(-0.55, x0 - block - gap + 0.55) - ax.set_ylim(top=ax.get_ylim()[1] * 6) - ax.yaxis.set_major_locator(matplotlib.ticker.LogLocator(numticks=5)) - ax.grid(axis="y", alpha=0.3, lw=0.4) - ax.tick_params(axis="x", length=0, pad=2) - ax.set_ylabel("cost (vCPU)" if weights == "cpu" else "cost ($/hour)") - # Workload names under their three groups; thin separators between them. - for start, end, title in spans: - ax.text((start + end) / 2, -0.11, title, transform=ax.get_xaxis_transform(), - ha="center", va="top", fontsize=8) - for (_, end, _), (start, _, _) in zip(spans, spans[1:]): - ax.axvline((end + start) / 2, color="0.6", lw=0.6, ls=":") - handles, names = ax.get_legend_handles_labels() - fig.tight_layout(rect=(0, 0, 1, 0.9)) - # Placed after the layout, so it does not narrow the axes. - fig.legend(handles, names, loc="upper center", bbox_to_anchor=(0.5, 1.0), ncol=3, - frameon=False, fontsize=7.5, handlelength=0.9, handletextpad=0.4, - columnspacing=0.7) - fig.canvas.draw() - for x, top, text, color in to_place: - bottom = ax.get_ylim()[0] - to_pt = ax.transData.transform - height_pt = (to_pt((x, top))[1] - to_pt((x, bottom))[1]) * 72 / fig.dpi - inside = height_pt > label_pt * 0.62 * len(text) + 5 - ax.text(x, top / 1.12 if inside else top * 1.1, text, ha="center", - va="top" if inside else "bottom", rotation=90, fontsize=label_pt, - weight="bold", - # White inside dark bars; the light LiteOllyPerQuery bar - # takes dark blue inside and above it, where its own - # colour would not read. - color=("#0b2e59" if color == "#8fb3de" else - "white" if inside else color)) - save(fig, out, "sec63_cost_bars" + ("" if weights == "cpu" else f"_{weights}"), - tight=True) - - -def cost_bars(results, out, layout): - """`wide`: three panels side by side at a two-column figure*'s width - (7 in). `column`: stacked, at one column's width (3.4 in). Both are drawn - at their printed size, so fonts are what the paper shows.""" - wide = layout == "wide" - sizes = ({"font.size": 9.5, "axes.titlesize": 11, "axes.labelsize": 10, - "xtick.labelsize": 9.5, "ytick.labelsize": 9.5, "legend.fontsize": 10.5} - if wide else - {"font.size": 8, "axes.titlesize": 9, "axes.labelsize": 8.5, - "xtick.labelsize": 8, "ytick.labelsize": 8, "legend.fontsize": 8.5}) - with plt.rc_context(sizes): - fig, axes = (plt.subplots(1, len(WORKLOADS), figsize=(7.1, 3.1)) if wide else - plt.subplots(len(WORKLOADS), 1, figsize=(3.4, 5.8))) - width = 0.3 if wide else 0.28 - gap = 1.15 if wide else 1.0 # between variant groups - for ax, (templates, title) in zip(axes, WORKLOADS): - labels = [] - for i, (variant, name) in enumerate(VARIANTS): - data = load(results, f"{templates}-{variant}") - limit = data["frontier"]["autosketch_latency_ms"] - cost = {m: cheapest(data, m, max_latency_ms=limit) for m, _, _ in METHODS} - for k, (method, label, color) in enumerate(METHODS): - x = i * gap + (k - 1) * width - ax.bar(x, cost[method], width * 0.95, color=color, - label=label if i == 0 else None) - ratio = cost[method] / cost["asap"] - multiple = f"{ratio:.0f}×" if ratio >= 10 else f"{ratio:.1f}×" - if wide: - # One short label per bar: ASAP's cost, each - # baseline's multiple of it (its cost is on the axis). - text = short(cost[method]) if method == "asap" else multiple - else: - text = short(cost[method]) + ("" if method == "asap" else "\n" + multiple) - # Wide: when ASAP's bar is about as tall as PerQuery's, its - # label rises above PerQuery's, so neighbours never share a - # row (one label is about a factor 2.5 of height here). - y = cost[method] * 1.2 - if wide and method == "asap": - y = max(y, cost["perquery"] * 1.2 * 2.6) - ax.text(x, y, text, ha="center", va="bottom", - fontsize=8 if wide else 6.5, linespacing=0.95, - weight="bold" if wide and method == "asap" else "normal") - labels.append(f"{name}\n{data['rqes']:,}" if wide else - f"{name}\n{data['rqes']:,} RQEs") - ax.set_yscale("log") - ax.set_xticks([i * gap for i in range(len(VARIANTS))]) - ax.set_xticklabels(labels, fontsize=8.5 if wide else 8, linespacing=1.0) - ax.set_xlim(-0.62, (len(VARIANTS) - 1) * gap + 0.62) - ax.set_title(title) - ax.set_ylim(top=ax.get_ylim()[1] * (12 if wide else 25)) - ax.yaxis.set_major_locator(matplotlib.ticker.LogLocator(numticks=4)) - ax.grid(axis="y", alpha=0.3, lw=0.4) - ax.tick_params(axis="x", length=0) - if wide or ax is axes[len(WORKLOADS) // 2]: - ax.set_ylabel("cost (vCPU)") - if wide: - ax.set_xlabel("workload scale (# RQEs)", fontsize=8.5) - if wide and ax is not axes[0]: - ax.set_ylabel("") - handles, names = axes[0].get_legend_handles_labels() - fig.legend(handles, names, loc="upper center", ncol=len(METHODS), frameon=False, - handlelength=1.2, columnspacing=1.2) - fig.tight_layout(rect=(0, 0, 1, 0.89 if wide else 0.95), h_pad=1.4, w_pad=0.3) - save(fig, out, "sec63_cost_bars" + ("" if wide else "_column")) - - -def frontier(results, out): - lines = [("asap", "ASAP", "C0", "o"), ("perquery", "PerQuery", "C1", "s")] - units = [("cpu", "cost (vCPU)"), ("fargate", "cost ($/hour)")] - fig, axes = plt.subplots(2, len(WORKLOADS), figsize=(7, 4)) - for j, (templates, title) in enumerate(WORKLOADS): - data = load(results, f"{templates}-shared1") - for i, (weights, unit) in enumerate(units): - ax = axes[i, j] - for method, label, color, marker in lines: - rs = sorted(plans(data, method, weights), key=lambda r: r["latency_ms"]) - ax.plot([r["latency_ms"] / 1e3 for r in rs], [r["objective"] for r in rs], - "-", color=color, marker=marker, ms=2.5, lw=1, label=label) - auto = plans(data, "autosketch", weights)[0] - ax.scatter([auto["latency_ms"] / 1e3], [auto["objective"]], marker="D", s=20, - color="C2", label="AutoSketch-Adapted", zorder=5) - ax.set_xscale("log") - ax.set_yscale("log") - ax.xaxis.set_major_locator(matplotlib.ticker.LogLocator(subs=(1, 2, 5))) - ax.xaxis.set_major_formatter(matplotlib.ticker.FormatStrFormatter("%g")) - ax.xaxis.set_minor_formatter(matplotlib.ticker.NullFormatter()) - ax.grid(alpha=0.3, which="both", lw=0.3) - if j == 0: - ax.set_ylabel(unit) - if i == 0: - ax.set_title(f"{title} ({data['rqes']} RQEs)", fontsize=8) - if i == len(units) - 1: - ax.set_xlabel("batch latency (s)") - handles, names = axes[0, 0].get_legend_handles_labels() - fig.legend(handles, names, loc="upper center", ncol=3, fontsize=7, frameon=False) - fig.tight_layout(rect=(0, 0, 1, 0.94)) - save(fig, out, "sec63_frontier") - - -def rollups(results, out): - fig, ax = plt.subplots(figsize=(7, 2.3)) - x, ticks, labels = 0, [], [] - for templates, title in WORKLOADS: - for variant, name in VARIANTS: - data = load(results, f"{templates}-{variant}") - for k, (method, label, color) in enumerate( - [("asap-norollup", "ASAP w/o roll-ups", "C9"), ("asap", "ASAP", "C0")]): - cost = cheapest(data, method) - ax.bar(x + (k - 0.5) * 0.38, cost, 0.38, color=color, - label=label if x == 0 else None, hatch="//" if k == 0 else None, - edgecolor="white", lw=0) - ax.text(x + (k - 0.5) * 0.38, cost * 1.08, f"{cost:.3g}", ha="center", - fontsize=6) - ticks.append(x) - labels.append(f"{name}\n({data['rqes']} RQEs)") - x += 1 - ax.text(x - 2, 250, title, ha="center", fontsize=8, weight="bold") - x += 0.6 - ax.set_yscale("log") - ax.set_ylim(0.1, 900) - ax.set_xticks(ticks) - ax.set_xticklabels(labels, fontsize=6.5) - ax.set_ylabel("cheapest plan (vCPU)") - ax.legend(fontsize=7, loc="lower left", bbox_to_anchor=(0, 1), frameon=False, ncol=2) - ax.grid(axis="y", alpha=0.3, lw=0.3) - fig.tight_layout() - save(fig, out, "sec63_rollups") - - -def main(): - results, out = sys.argv[1], sys.argv[2] - os.makedirs(out, exist_ok=True) - cost_bars_wide(results, out) - cost_bars_single(results, out) - cost_bars_single(results, out, "fargate") - cost_bars(results, out, "column") - frontier(results, out) - rollups(results, out) - - -if __name__ == "__main__": - main() diff --git a/scripts/run_autosketch_vs_asap_synthetic.sh b/scripts/run_autosketch_vs_asap_synthetic.sh deleted file mode 100755 index eddc687a..00000000 --- a/scripts/run_autosketch_vs_asap_synthetic.sh +++ /dev/null @@ -1,50 +0,0 @@ -#!/usr/bin/env bash -# Run the synthetic workload grid of the AutoSketch vs. ASAP evaluation -# (ProjectASAP/ASAPQuery#777, section 6 "Workload grid") and plot it. -# -# Usage: -# SATURATION_DIR=DIR scripts/run_autosketch_vs_asap_synthetic.sh TABLES_DIR run -# scripts/run_autosketch_vs_asap_synthetic.sh TABLES_DIR plot -# -# TABLES_DIR holds the tables and plan.tsv of -# `export_autosketch_eval_table.py --synthetic` (the workload). SATURATION_DIR -# holds the curves and the cost table of scripts/study_saturation.py, the -# same inputs the planner reads. `run` evaluates every plan.tsv -# row, JOBS at a time, at every weight setting and SLA of #777; a run over -# JOB_TIMEOUT seconds is stopped and listed in failed.txt. RUNS timing runs -# each. - -set -euo pipefail - -TABLES=${1:?usage: run_autosketch_vs_asap_synthetic.sh TABLES_DIR (run|plot)} -MODE=${2:?usage: run_autosketch_vs_asap_synthetic.sh TABLES_DIR (run|plot)} -OUT=${OUT:-rqe-optimizer/results/autosketch-vs-asap-synthetic} -JOBS=${JOBS:-4} -RUNS=${RUNS:-3} -JOB_TIMEOUT=${JOB_TIMEOUT:-14400} -BIN=${CARGO_TARGET_DIR:-./target}/release/examples/autosketch_vs_asap -mkdir -p "$OUT" - -case "$MODE" in -run) - : "${SATURATION_DIR:?set SATURATION_DIR to the saturation study output directory}" - export SATURATION_DIR JOB_TIMEOUT RUNS OUT - cargo build --release -p rqe-optimizer --example autosketch_vs_asap - : > "$OUT/failed.txt" - while IFS=$'\t' read -r table target result; do - [[ -s "$OUT/$result" ]] || echo "$TABLES/$table $target $OUT/$result" - done < "$TABLES/plan.tsv" | - xargs -P "$JOBS" -L 1 sh -c \ - 'timeout "$JOB_TIMEOUT" "$0" synthetic --table "$1" --target "$2" --runs "$RUNS" \ - --saturation-dir "$SATURATION_DIR" --no-chosen --out "$3" 2>> "$3.log" \ - || echo "$3" >> "$OUT/failed.txt"' \ - "$BIN" - ;; -plot) - python3 scripts/plot_autosketch_vs_asap_synthetic.py "$OUT" - ;; -*) - echo "unknown mode $MODE" >&2 - exit 2 - ;; -esac diff --git a/scripts/study_saturation.py b/scripts/study_saturation.py index f75c6e19..2e0c9469 100644 --- a/scripts/study_saturation.py +++ b/scripts/study_saturation.py @@ -134,7 +134,7 @@ ] EXACT_METRIC = "relative_error" -# --phase hydra (docs/rqe_optimizer_hydra.md; ASAPQuery#777 §6.3): Hydra's +# --phase hydra (ASAPQuery rqe-optimizer/docs/rqe_optimizer_hydra.md; ASAPQuery#777 §6.3): Hydra's # per-group error depends on the group's share of the fanned-out mass, so the # eval reads accuracy measured on datasets with its own schemas # (configs/datagen/hydra_http*.yaml, hydra_flows.yaml, matching @@ -145,7 +145,7 @@ # read. The other statistics run on the generic hydra_hier*.yaml sweep # (schema width 2, 3, 4). HYDRA_R = 3 -# Inner cells: the canonical ones (docs/rqe_optimizer_hydra.md §1), except +# Inner cells: the canonical ones (ASAPQuery rqe-optimizer/docs/rqe_optimizer_hydra.md §1), except # UnivMon's, whose 5 x 2048 x 8-layer cell (~650 KB) makes a 3 x 16384 grid # 32 GB; this one is ~100 KB. HYDRA_UNIVMON_CELL = "cell_heap_size=64 cell_sketch_row=3 cell_sketch_col=256 cell_layer_size=8" diff --git a/scripts/summarize_case_studies.py b/scripts/summarize_case_studies.py deleted file mode 100644 index 085e3058..00000000 --- a/scripts/summarize_case_studies.py +++ /dev/null @@ -1,92 +0,0 @@ -#!/usr/bin/env python3 -"""Each case-study result's ASAP plans, one block per table: cost, MV count -and latency with no SLA and at each batch latency SLA, and each distinct -plan's MVs (sketch, grouping, window/slide, the queries each serves). - - python3 scripts/summarize_case_studies.py RESULT.json... > summary.md -""" - -import json -import sys -from collections import defaultdict - - -def short(rqe_id): - """`case/perf/base=0/quantile_over_time_p0.9/values/86400s` -> `quantile_over_time_p0.9[24h]`.""" - *_, query, _, lookback = rqe_id.split("/") - secs = float(lookback.rstrip("s")) - for unit, size in [("d", 86400), ("h", 3600), ("m", 60), ("s", 1)]: - if secs >= size and secs % size == 0: - return f"{query}[{int(secs // size)}{unit}]" - return f"{query}[1 scrape]" - - -def grouping_of(rqe_id): - """A query's group-by labels, from its id: `p99_by_service,region` and - `quantile_by_job_p0.5` name theirs; `quantile_over_time` is per series.""" - query = rqe_id.split("/")[-3] - if query.startswith("quantile_over_time"): - return ["series"] - return query.split("_by_")[1].split("_")[0].split(",") - - -def secs(value): - return f"{value:g}s" - - -def mvs(plan): - """ASAP's deployments: (description, queries) per deployment index.""" - by_deployment = defaultdict(list) - for choice in plan["chosen"]: - by_deployment[choice["deployment"]].append(choice) - out = [] - for choices in by_deployment.values(): - first = choices[0] - params = ",".join(f"{k}={v}" for k, v in first["params"].items()) - # The runner names the deployment's grouping only where it differs - # from the RQE's (a roll-up or a Hydra grid). - grouping = first.get("deployment_grouping") or grouping_of(first["rqe"]) - out.append((f"{first['sketch']}({params}) by ({','.join(grouping)}) " - f"window {secs(first['window_secs'])} " - f"slide {secs(first['slide_secs'])}", - [f"{short(c['rqe'])} (merges {c['merged_shards']})" for c in choices])) - return out - - -def cheapest(result): - return next(r for r in result["results"] - if r["method"] == "asap" and r["weights"] == "cpu" and r["bound_ms"] is None) - - -def describe(path): - result = json.load(open(path)) - lines = [f"## {result['workload']}", ""] - lines.append("| SLA (ms) | vCPU | MVs | latency (ms) |") - lines.append("|---|---|---|---|") - plans = [("none", cheapest(result))] - plans += [(r["sla_ms"], r) for r in result["sla_results"] - if r["method"] == "asap" and r["weights"] == "cpu"] - for sla, asap in plans: - if asap.get("infeasible"): - lines.append(f"| {sla} | infeasible | | |") - else: - lines.append(f"| {sla} | {asap['cpu']:.3g} | {asap['active_deployments']} " - f"| {asap['latency_ms']:.3g} |") - lines.append("") - seen = set() - for sla, asap in plans: - if asap.get("infeasible"): - continue - plan = mvs(asap) - key = json.dumps(plan) - if key in seen: - continue - seen.add(key) - lines.append(f"Plan at SLA {sla}:") - lines += [f"- {mv}: {', '.join(queries)}" for mv, queries in plan] - lines.append("") - return "\n".join(lines) - - -if __name__ == "__main__": - print("\n".join(describe(path) for path in sys.argv[1:]), end="") diff --git a/scripts/test_export_autosketch_eval_table.py b/scripts/test_export_autosketch_eval_table.py index a3a4b3c1..c8ce614d 100644 --- a/scripts/test_export_autosketch_eval_table.py +++ b/scripts/test_export_autosketch_eval_table.py @@ -371,9 +371,9 @@ def test_a_strictest_copy_below_the_curves_fails_loudly(self): synthetic_queries(templates="multigroup") -COMMITTED_TABLES = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", - "rqe-optimizer", "results", "autosketch-vs-asap-inputs", - "tables") +# A copy of ASAPQuery's rqe-optimizer/results/autosketch-vs-asap-inputs/tables. +COMMITTED_TABLES = os.path.join(os.path.dirname(os.path.abspath(__file__)), "testdata", + "autosketch-vs-asap-tables") # Classic templates 7 and 9, dropped since the committed tables. diff --git a/rqe-optimizer/results/autosketch-vs-asap-inputs/tables/plan.tsv b/scripts/testdata/autosketch-vs-asap-tables/plan.tsv similarity index 100% rename from rqe-optimizer/results/autosketch-vs-asap-inputs/tables/plan.tsv rename to scripts/testdata/autosketch-vs-asap-tables/plan.tsv diff --git a/rqe-optimizer/results/autosketch-vs-asap-inputs/tables/synthetic-templatesall-shared1-metrics16.json b/scripts/testdata/autosketch-vs-asap-tables/synthetic-templatesall-shared1-metrics16.json similarity index 100% rename from rqe-optimizer/results/autosketch-vs-asap-inputs/tables/synthetic-templatesall-shared1-metrics16.json rename to scripts/testdata/autosketch-vs-asap-tables/synthetic-templatesall-shared1-metrics16.json diff --git a/rqe-optimizer/results/autosketch-vs-asap-inputs/tables/synthetic-templatesall-shared1-metrics8.json b/scripts/testdata/autosketch-vs-asap-tables/synthetic-templatesall-shared1-metrics8.json similarity index 100% rename from rqe-optimizer/results/autosketch-vs-asap-inputs/tables/synthetic-templatesall-shared1-metrics8.json rename to scripts/testdata/autosketch-vs-asap-tables/synthetic-templatesall-shared1-metrics8.json diff --git a/rqe-optimizer/results/autosketch-vs-asap-inputs/tables/synthetic-templatesall-shared1.json b/scripts/testdata/autosketch-vs-asap-tables/synthetic-templatesall-shared1.json similarity index 100% rename from rqe-optimizer/results/autosketch-vs-asap-inputs/tables/synthetic-templatesall-shared1.json rename to scripts/testdata/autosketch-vs-asap-tables/synthetic-templatesall-shared1.json diff --git a/rqe-optimizer/results/autosketch-vs-asap-inputs/tables/synthetic-templatesall-shared8.json b/scripts/testdata/autosketch-vs-asap-tables/synthetic-templatesall-shared8.json similarity index 100% rename from rqe-optimizer/results/autosketch-vs-asap-inputs/tables/synthetic-templatesall-shared8.json rename to scripts/testdata/autosketch-vs-asap-tables/synthetic-templatesall-shared8.json From bbc9981f372ddda223ba1232450639750a92a749 Mon Sep 17 00:00:00 2001 From: Milind Srivastava Date: Mon, 26 Oct 2026 21:05:21 -0400 Subject: [PATCH 2/3] chore: drop unused plan.tsv fixture and fix hook comment Co-Authored-By: Claude Opus 5.5 --- .pre-commit-config.yaml | 2 +- scripts/testdata/autosketch-vs-asap-tables/plan.tsv | 4 ---- 2 files changed, 1 insertion(+), 5 deletions(-) delete mode 100644 scripts/testdata/autosketch-vs-asap-tables/plan.tsv diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index b2c2e8f9..d8b0bec8 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -4,7 +4,7 @@ repos: hooks: - id: trailing-whitespace - id: end-of-file-fixer - # Exported tables the exporter test compares byte for byte. + # Exporter test fixtures, kept identical to ASAPQuery's rqe-optimizer copy. exclude: ^scripts/testdata/ - id: check-yaml - id: check-added-large-files diff --git a/scripts/testdata/autosketch-vs-asap-tables/plan.tsv b/scripts/testdata/autosketch-vs-asap-tables/plan.tsv deleted file mode 100644 index db66ac44..00000000 --- a/scripts/testdata/autosketch-vs-asap-tables/plan.tsv +++ /dev/null @@ -1,4 +0,0 @@ -synthetic-templatesall-shared1.json p95 synthetic-templatesall-shared1-tp95.json -synthetic-templatesall-shared8.json p95 synthetic-templatesall-shared8-tp95.json -synthetic-templatesall-shared1-metrics8.json p95 synthetic-templatesall-shared1-metrics8-tp95.json -synthetic-templatesall-shared1-metrics16.json p95 synthetic-templatesall-shared1-metrics16-tp95.json From 7d7e2a931300db0f96c28377e323372f2c769d7a Mon Sep 17 00:00:00 2001 From: Milind Srivastava Date: Fri, 30 Oct 2026 09:33:18 -0400 Subject: [PATCH 3/3] docs: say rqe-optimizer now lives in ASAPQuery Co-Authored-By: Claude Opus 5.5 --- docs/saturation_study.md | 4 ++-- scripts/study_saturation.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/saturation_study.md b/docs/saturation_study.md index 2e5dde96..40678488 100644 --- a/docs/saturation_study.md +++ b/docs/saturation_study.md @@ -6,7 +6,7 @@ records the cost of the sketch at the largest N measured, and finds the stream length N* past which an exact answer costs 10x (100x) more than the sketch. It is stacked on PR #129 (`dev-milind-turboprom`) and does not change -`rqe-optimizer` or its atomic-cost table; it only adds a Pareto generator +`rqe-optimizer` (now in ASAPQuery) or its atomic-cost table; it only adds a Pareto generator (`--dataset pareto`, at `--dtype f64` or `i64`) and `scripts/study_saturation.py`. @@ -79,7 +79,7 @@ atomic-costs` to `rqe_atomic_costs.json` (#174). Top-k configs are measured at h 2048 (`TOPK_HEAPS`, `heap=`): a deployment merging `m` windows keeps `m · k` and rqe-optimizer interpolates between them. The accuracy grid measures top-k at each k in `--topk-ks`, at a heap of k. Put it under the curves' -directory as `optimizer_cost/`, where `rqe-optimizer` reads it. The accuracy +directory as `optimizer_cost/`, where ASAPQuery's `rqe-optimizer` reads it. The accuracy grid also covers that shape (unless `--no-cost-shape`): θ = 1.1 and K = 1e4 join the grid as a full row and column (a = 2 joins the Pareto values), so the grid stays a full cross, each cost-table row is a point on a curve, and diff --git a/scripts/study_saturation.py b/scripts/study_saturation.py index 2e0c9469..d5bb6d93 100644 --- a/scripts/study_saturation.py +++ b/scripts/study_saturation.py @@ -117,7 +117,7 @@ TOPK_HEAPS = (32, 128, 512, 2048) # The k a top-k answer is graded at: curves run at each, a config at k # carrying ` topk_k=k` (its heap defaults to k). TOPK_K is left implicit, -# so its points keep their old keys. Kept equal to rqe-optimizer's TOPK_K +# so its points keep their old keys. Kept equal to ASAPQuery rqe-optimizer's TOPK_K # and sketch-bench's (the cost table's top-k rows are graded at it). TOPK_K = 32 TOPK_KS = (10, 32, 100)