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exec, optimizer: window group limit for rank filters over a window (P2-T055) - #196
Spark's window group limit (InferWindowGroupLimit + WindowGroupLimitExec): a filter that keeps only the rows a row_number, rank or dense_rank window function ranks at most k now drops the rest of each partition before the window is evaluated. Results are unchanged; the plan shows a WindowGroupLimit node between the window and its sort.
Rule conditions (checked against Spark 4.2.0 plans)
off when spark.sql.optimizer.windowGroupLimitThreshold is -1 (new config krishiv.sql.optimizer.windowGroupLimitThreshold, Spark's key as alias, default 1000);
the window is ordered, and every expression of the window node runs over ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW and is neither size-based (ntile, percent_rank, cume_dist) nor look-around (lag, lead);
a conjunct compares the rank column with an INT literal: = k, <= k, < k (k - 1 in 32-bit arithmetic, as Spark's Int), literal on either side; casts the analyzer widened (rn <= 2L, rn < 2.5) are read as Spark's UnwrapCastInBinaryComparison leaves them;
row_number limits are taken first, then the smallest limit (the first on ties);
the limit is at most the threshold and below the window input's row bound;
a limit below 1 keeps no rows; row_number without partitioning takes the top-N path (a top-N below the window).
Plans differ from Spark's only in shape, never in results: the input row bound is this optimizer's (VALUES/range are unbounded here), and the top-N path is a top-N below the window rather than Spark's limit above it.
Execution
WindowGroupLimit operator: the exact limit over the sorted input (Spark's Final), ties kept for rank and dense_rank.
A sort directly below drops rows past the limit within each sorted batch (Spark's Partial), and stops looking when its first batches lose less than a tenth of their rows.
Plan codec: the new node is the plan feature window_group_limit (ADR 0040; kind Optimization, legacy level 6), golden window_group_limit/.
Tests
planner: every limit form Spark reads and refuses, the frame and function conditions, the threshold and bounded inputs, row_number precedence, plan snapshots;
results against Spark 4.2.0 rows (ties, NULL partitions, descending orders, running aggregates, the top-N path), with the limit and without it;
operator: a property test (random partitions, ties, NULL keys, batch splits, two drivers) that the limited plan keeps exactly the filtered rows; pruning and its switch-off; weights; invalid plans;
codec: round trip, the feature and its legacy level, corrupt input.
Measurements
Benchmark server, krishiv-bench ab (ABBA, 3 blocks, 12 threads, 24 GiB), A = main 8cae295:
query
A
B (this PR)
B/A
H2O groupby q08 (1e8 rows)
65.5 s
62.5 s
0.95 (CI 0.93-0.98)
q08's partitions (1M, ~100 rows each) are spread over every sorted batch, so the per-batch pruning finds nothing to drop and switches itself off; the gain is the window's work on 2% of the rows. The large part of q08 is the single-threaded sort merge, which the sort-buckets PR addresses (#198; with both and the limit applied in its bucket merge, q08 is 63.1 s → 3.74 s).
…2-T055)
A filter keeping the rows a row_number, rank or dense_rank window function
ranks at most k now drops the rest of each partition before the window is
evaluated, with Spark's InferWindowGroupLimit conditions (checked against
Spark 4.2.0 plans): every window expression runs over ROWS BETWEEN
UNBOUNDED PRECEDING AND CURRENT ROW and is neither size-based nor
look-around, the window is ordered, a conjunct compares the rank column
with an INT literal (=, <, <=, literal on either side; casts widened by
the analyzer are read as Spark's UnwrapCastInBinaryComparison leaves
them), row_number limits first then the smallest, the limit at most
spark.sql.optimizer.windowGroupLimitThreshold (default 1000, -1 off) and
below the input's row bound. A limit below 1 keeps no rows;
row_number without partitioning takes the top-N path.
- krishiv-optimizer: infer_window_group_limit, the decision.
- krishiv-exec: the WindowGroupLimit plan node and operator (the exact
limit over the sorted input, ties kept for rank and dense_rank); a sort
directly below prunes each sorted batch while that drops a tenth of its
rows or more (Spark's partial mode).
- krishiv-plan-codec: format version 6 for the new node.
- Config krishiv.sql.optimizer.windowGroupLimitThreshold, aliased to
Spark's key.
Signed-off-by: gopal tathe <44522021+techgopal@users.noreply.github.com>
The planner now infers limits for eligible ranking filters and can plan TopN or per-partition window limits. Execution adds rank-aware filtering and sorted-batch pruning. The plan codec adds the new physical node in format version 6.
Changes: Window group limit
Layer / File(s)
Summary
Configuration and limit inference crates/krishiv-common/src/config/registry.rs, crates/krishiv-optimizer/src/group_limit.rs, crates/krishiv-optimizer/src/lib.rs, crates/krishiv-optimizer/CLAUDE.md
Adds the 1000 default threshold and Spark and Krishiv setting keys. The optimizer infers eligible limits for row_number, rank, and dense_rank filters.
Planner and physical plan construction crates/krishiv-exec/src/planner/*, crates/krishiv-exec/src/physical/*, crates/krishiv-exec/tests/planner/*
The planner passes inferred limits into window planning. It uses local and distributed TopN for eligible limits, or adds a WindowGroupLimit physical node. Planner tests cover eligibility, thresholds, plans, and results.
Ranking and sorted-batch pruning crates/krishiv-exec/src/window/*, crates/krishiv-exec/src/operators/sort/sort.rs, crates/krishiv-exec/CLAUDE.md
WindowGroupLimit filters rows by rank within each partition and tracks state across batches. Sort can prune qualifying batches and reports pruned-row metrics. Execution tests cover ranking, ties, weighted input, validation, and pruning.
Plan codec version 6 proto/krishiv/plan/v1/plan.proto, crates/krishiv-plan-codec/src/*, crates/krishiv-plan-codec/tests/*
Adds the protobuf node and rank-function enum. The encoder and decoder support the node, and round-trip tests verify version requirements and reject invalid limits and function values.
KrishivAI/krishiv-engine#144: Adds the window planner, ranking functions, sort-based window execution, and window plan types extended by this change.
KrishivAI/krishiv-engine#189: Adds window frame and function variants that the new limit inference rule evaluates.
KrishivAI/krishiv-engine#193: Updates plan codec compatibility features; this change also modifies the reader-version contract.
Merge Risk: Low · up to 19eea
The feature should return correct results. Before merging, fix the double-counting of pruning metrics when a sort batch is retried after a memory shortfall. Also make EXPLAIN ANALYZE attribute the inlined sort's metrics to the sort rather than the enclosing group-limit node.
Security architecture risk: Low · up to 19eea
The inspected paths retain the original filter, isolate ranking state per execution, and reject incompatible plans rather than silently executing them. No introduced security vulnerability was established. Deployment compatibility and incomplete execution-boundary coverage leave some residual uncertainty.
Retained concerns
No architecture-level concerns identified.
Security review details
Security Blast Radius
inferred — The demonstrated exposure concerns results and execution resources for eligible queries, plus compatibility for consumers of their serialized plans. The inspected new operator works on supplied batches with driver-local ranking state; deployment-wide or cross-tenant exposure was not established.
Trust Boundaries and Controls
observed — The new serialized node rejects zero limits and invalid rank-function values, reuses existing bounded node/depth and child-reference checks, and reaches an operator constructor that validates column ranges. The rank threshold is not an allocation size. These checks counter malformed-plan concerns without establishing who can submit plan bytes.
Resilience and Maintainability Implications
inferred — Memory-pressure retries can advance pruning statistics before a batch is accepted, potentially overcounting dropped rows or disabling the optional optimization earlier. Retained buffering still requires a successful reservation, and the downstream exact limiter remains authoritative; the inspected discrepancy does not demonstrate a memory-control bypass or cross-query state leak.
Pre-merge checks: 3 · 2
Failed checks: 2 warnings
Check name
Status
Explanation
Resolution
Docstring Coverage
Docstring coverage is 48.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 75 functions across 20 files. (3 skipped:…
Write docstrings for the functions missing them to satisfy the coverage threshold.
Description check
The description provides a detailed summary, implementation conditions, test scope, and measurements. However, it does not follow the required template: it omits the Task/spec/ADR field, provides no t…
Add the required Summary section with task, specification, and ADR links. Replace the test overview with the exact commands run and their result lines. Complete every applicable checklist item, including explicit N/A markings where appropri…
Passed checks: 3 passed
Check name
Status
Explanation
Title check
The title clearly identifies the main change: adding window group limits for rank filters in the execution and optimizer components. It also includes the task identifier.
Linked Issues check
Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check
Check skipped because no linked issues were found for this pull request.
Full details: Docstring Coverage
Explanation
Docstring coverage is 48.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 75 functions across 20 files. (3 skipped: 3 unsupported.)
Resolution
Write docstrings for the functions missing them to satisfy the coverage threshold.
Full details: Description check
Explanation
The description provides a detailed summary, implementation conditions, test scope, and measurements. However, it does not follow the required template: it omits the Task/spec/ADR field, provides no test commands or result lines, and omits the complete checklist, including DCO, licensing, documentation, safety, error, distributed-execution, API, and performance attestations.
Resolution
Add the required Summary section with task, specification, and ADR links. Replace the test overview with the exact commands run and their result lines. Complete every applicable checklist item, including explicit N/A markings where appropriate.
Finishing Touches: 💡 1
⚔️ Resolve merge conflicts: 💡
Resolve merge conflict in branch p2b/t055-part2-wgl
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Actionable comments posted: 4
🪄 Fix comments: Fix CodeRabbit comments on this PR
Prompt to fix review comments
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Inline comments:
Review comments at @crates/krishiv-exec/src/operators/sort/sort.rs:
- Around line 186-199: Update Sort::prune to return the pruning result and
input/output row counts without updating probe statistics or counters.pruned; in
Sort::sink, apply those metrics only after b.batches.push(...) succeeds.
Preserve the existing pruning result and ensure retries that return
SinkStatus::NeedMemory do not count the batch.
Review comments at @crates/krishiv-exec/src/physical/instantiate.rs:
- Around line 340-349: In the `PhysNode::Sort` branch of the `WindowGroupLimit`
input handling, preserve the original sort node identity and apply its
registered EXPLAIN ANALYZE tag to the `Built` node returned by `breaker`; leave
the existing fallback path unchanged when no sort ID is registered.
Review comments at @crates/krishiv-exec/src/planner/window.rs:
- Around line 143-162: Extract the duplicated LocalTopN construction and
sites.clear() behavior into a shared local_top_n helper alongside local_limit;
replace the inline block in the window-planning path and the corresponding block
in Planner::top_n with calls to that helper, passing input, keys, n, and detail.
Review comments at @crates/krishiv-exec/tests/planner/group_limit.rs:
- Around line 367-374: In the `for (sql, want) in &cases` loop, compare the
`EXPLAIN` outputs from `explain_with` for the unlimited and `Some("-1")` plans
and assert they differ before checking `sorted_rows`. Keep both result
assertions so each case verifies both the optimization and its output.
After applying the fix, consider running `coderabbit review --agent` for local
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System calls cannot be simulated consistently and are left out of the measure. The reported value understates the real cost. Switch to the Walltime instrument to measure them.
Resolves the conflicts with the window group limit (#196): a sorted
batch is pruned bucket by bucket (the bucket bounds follow the kept
rows), and a group limit directly over a bucketed sort keeps the
buckets.
Signed-off-by: techysneha14 <343708768+shehatathe@users.noreply.github.com>
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What
Spark's window group limit (
InferWindowGroupLimit+WindowGroupLimitExec): a filter that keeps only the rows arow_number,rankordense_rankwindow function ranks at mostknow drops the rest of each partition before the window is evaluated. Results are unchanged; the plan shows aWindowGroupLimitnode between the window and its sort.Rule conditions (checked against Spark 4.2.0 plans)
spark.sql.optimizer.windowGroupLimitThresholdis -1 (new configkrishiv.sql.optimizer.windowGroupLimitThreshold, Spark's key as alias, default 1000);ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROWand is neither size-based (ntile,percent_rank,cume_dist) nor look-around (lag,lead);INTliteral:= k,<= k,< k(k - 1in 32-bit arithmetic, as Spark'sInt), literal on either side; casts the analyzer widened (rn <= 2L,rn < 2.5) are read as Spark'sUnwrapCastInBinaryComparisonleaves them;row_numberlimits are taken first, then the smallest limit (the first on ties);row_numberwithout partitioning takes the top-N path (a top-N below the window).Plans differ from Spark's only in shape, never in results: the input row bound is this optimizer's (
VALUES/rangeare unbounded here), and the top-N path is a top-N below the window rather than Spark's limit above it.Execution
WindowGroupLimitoperator: the exact limit over the sorted input (Spark'sFinal), ties kept forrankanddense_rank.Partial), and stops looking when its first batches lose less than a tenth of their rows.window_group_limit(ADR 0040; kindOptimization, legacy level 6), goldenwindow_group_limit/.Tests
Measurements
Benchmark server,
krishiv-bench ab(ABBA, 3 blocks, 12 threads, 24 GiB), A = main 8cae295:q08's partitions (1M, ~100 rows each) are spread over every sorted batch, so the per-batch pruning finds nothing to drop and switches itself off; the gain is the window's work on 2% of the rows. The large part of q08 is the single-threaded sort merge, which the sort-buckets PR addresses (#198; with both and the limit applied in its bucket merge, q08 is 63.1 s → 3.74 s).
Regressions (same harness, A = main 8cae295):