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feat(sql): count_if, boolean aggregates, any_value, max_by and min_by - #214
Registers the Spark aggregates count_if, bool_and/every, bool_or/some/any,
any_value(x [, ignoreNulls]) and the two-argument max_by/min_by, in grouped,
global and window form. first, first_value, last and last_value (with the
ignoreNulls flag) were already registered and are unchanged.
count_if and the boolean aggregates reuse existing accumulators. The planner
maps registry names to executor aggregates in one place, shared by the
aggregate and window planners: count_if(c) is count(nullif(c, false)),
bool_and/every are min and bool_or/some/any are max over a boolean argument.
The registry types their argument as boolean, so a wrong argument type is
DATATYPE_MISMATCH.UNEXPECTED_INPUT_TYPE. Result names keep the called
function's name.
New accumulator variants: any_value (the first row's value, or the first
non-NULL with ignoreNulls, kept in the state first and last use) and
max_by/min_by. max_by/min_by keep the value and the ordering key as two
state columns; a NULL key skips the row, a NULL value is a result, the first
row wins a tie and a merge keeps the earlier partial on a tie, so partial
states from any split of the input in order give the single-phase answer.
Keys compare as min and max do (NaN largest, -0.0 = 0.0). Both columns are
accounted in the table's reservation (Accumulator::projected takes the key
column too) and spill through the existing partial-state path. A running
window frame over string or binary max_by/min_by is refused with
KRISHIV_UNSUPPORTED_FEATURE (per-row snapshots would not be accounted);
whole-partition frames take them.
The three-argument max_by/min_by returns an array and stays unsupported
(KRISHIV_UNSUPPORTED_FEATURE from the registry rule), as do collect_list,
collect_set and array_agg.
Also: Spark names any_value(x, true) as any_value(x), and IGNORE NULLS /
RESPECT NULLS apply to any_value as to first and last. The planner now maps
mean to avg for grouped aggregates as the window planner already did.
The plan codec does not encode the new functions yet: the encoder returns
its typed "cannot encode" error for them, and the proto is unchanged.
New public items: AggregateFunction::{AnyValue, MaxBy, MinBy},
AggregateExpr::by (all krishiv-exec) and Aggregate::{AnyValue, MaxBy, MinBy}
(krishiv-functions). The function-matrix manifest gains the new functions'
cases that need VALUES, which the parser does not read yet.
Tests: Spark 4.2.0 goldens for the boolean aggregates (18 statements of
group-by.sql) and the any_value window statement of window.sql replay
through a session; unit tests cover semantics, merges (including a property
test over arbitrary splits), both table layouts, spills and accounting; SQL
tests cover count_if, any_value, max_by and min_by.
Not verified: count_if, any_value with a flag, max_by and min_by have no
statements in the vendored goldens (max-min-by-k.sql only uses the
three-argument form); their expectations follow Spark's definitions. Not
claimed: every("true") needs a string to boolean cast, which the engine
does not have; the GROUP BY ALL statements over t1 need GROUP BY ALL.
Registers the Spark aggregates count_if, bool_and/every, bool_or/some/any,
any_value(x [, ignoreNulls]) and the two-argument max_by/min_by, in grouped,
global and window form. first, first_value, last and last_value (with the
ignoreNulls flag) were already registered and are unchanged.
count_if and the boolean aggregates reuse existing accumulators. The planner
maps registry names to executor aggregates in one place, shared by the
aggregate and window planners: count_if(c) is count(nullif(c, false)),
bool_and/every are min and bool_or/some/any are max over a boolean argument.
The registry types their argument as boolean, so a wrong argument type is
DATATYPE_MISMATCH.UNEXPECTED_INPUT_TYPE. Result names keep the called
function's name.
New accumulator variants: any_value (the first row's value, or the first
non-NULL with ignoreNulls, kept in the state first and last use) and
max_by/min_by. max_by/min_by keep the value and the ordering key as two
state columns; a NULL key skips the row, a NULL value is a result, the first
row wins a tie and a merge keeps the earlier partial on a tie, so partial
states from any split of the input in order give the single-phase answer.
Keys compare as min and max do (NaN largest, -0.0 = 0.0). Both columns are
accounted in the table's reservation (Accumulator::projected takes the key
column too) and spill through the existing partial-state path. A running
window frame over string or binary max_by/min_by is refused with
KRISHIV_UNSUPPORTED_FEATURE (per-row snapshots would not be accounted);
whole-partition frames take them.
The three-argument max_by/min_by returns an array and stays unsupported
(KRISHIV_UNSUPPORTED_FEATURE from the registry rule), as do collect_list,
collect_set and array_agg.
Also: Spark names any_value(x, true) as any_value(x), and IGNORE NULLS /
RESPECT NULLS apply to any_value as to first and last. The planner now maps
mean to avg for grouped aggregates as the window planner already did.
The plan codec does not encode the new functions yet: the encoder returns
its typed "cannot encode" error for them, and the proto is unchanged.
New public items: AggregateFunction::{AnyValue, MaxBy, MinBy},
AggregateExpr::by (all krishiv-exec) and Aggregate::{AnyValue, MaxBy, MinBy}
(krishiv-functions). The function-matrix manifest gains the new functions'
cases that need VALUES, which the parser does not read yet.
Tests: Spark 4.2.0 goldens for the boolean aggregates (18 statements of
group-by.sql) and the any_value window statement of window.sql replay
through a session; unit tests cover semantics, merges (including a property
test over arbitrary splits), both table layouts, spills and accounting; SQL
tests cover count_if, any_value, max_by and min_by.
Not verified: count_if, any_value with a flag, max_by and min_by have no
statements in the vendored goldens (max-min-by-k.sql only uses the
three-argument form); their expectations follow Spark's definitions. Not
claimed: every("true") needs a string to boolean cast, which the engine
does not have; the GROUP BY ALL statements over t1 need GROUP BY ALL.
Signed-off-by: gopal tathe <44522021+techgopal@users.noreply.github.com>
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Walkthrough
The pull request adds registry, planner, and executor support for any_value, max_by, and min_by, and registers count_if and boolean aggregates. It updates window handling, aggregate state accounting, tests, and plan-codec error handling.
Changes: Aggregate Function Support
Layer / File(s)
Summary
Registry and SQL contracts crates/krishiv-functions/src/registry/*, crates/krishiv-functions/CLAUDE.md, crates/krishiv-sql/src/lower.rs, crates/krishiv-sql/tests/lower.rs, crates/krishiv-plan/src/analyzer/exprs.rs
The registry adds any_value, max_by, min_by, count_if, and boolean aggregates with their type and arity rules. SQL lowering handles any_value null-treatment flags, and expression display omits its Boolean flag.
Aggregate expressions and planning crates/krishiv-exec/src/aggregate/mod.rs, crates/krishiv-exec/src/planner/{aggregate,window}.rs, crates/krishiv-session/tests/aggregate_simple.rs
Aggregate expressions carry value and key types for max_by and min_by. Shared lowering maps aggregate calls for grouped and window planning. Integration tests cover count_if, mean, argument validation, and related cases.
Accumulator state and merging crates/krishiv-exec/src/aggregate/{accumulator.rs,accumulator/differential.rs,rowtable.rs,table.rs,tests.rs,tests/simple.rs}, crates/krishiv-session/tests/aggregate_simple.rs
max_by and min_by retain selected values and keys in accumulator state. Ties retain the existing selection, NULL keys are ignored, and final results contain only the selected value. The changes also update memory projection and cover merging, spill, weights, and supported types.
Window handling and Spark golden tests crates/krishiv-exec/src/window/eval.rs, crates/krishiv-session/tests/aggregate_goldens.rs
The byte-value window path handles any_value using its null-setting behavior. Spark golden tests cover selected boolean aggregates and an any_value window case.
Plan codec handling crates/krishiv-plan-codec/src/{encode.rs,features.rs}, crates/krishiv-plan-codec/tests/roundtrip.rs
Encoding aggregate and window expressions containing AnyValue, MaxBy, or MinBy returns an unsupported error.
KrishivAI/krishiv-engine#185: Changes disk-backed window replay and aggregate folding, which must preserve aggregate state during replay.
KrishivAI/krishiv-engine#143: Enforces literal Boolean null flags for first and last; this change applies the same contract to any_value.
Merge Risk: Low · up to 06c06
The remaining concerns affect test reliability and coverage, not an established production failure. The change is mergeable with these focused test fixes or owner follow-up.
Security architecture risk: Moderate · up to 06c06
The new whole-partition functions can copy string or binary data without charging it against the query’s memory allowance. Their disk-backed fallback can also reject otherwise supported queries. These issues weaken memory-pressure containment; impact on other users depends on how the engine is hosted.
Retained concerns
Medium · security · observed: Whole-partition MaxBy/MinBy windows retain copied string/binary values and ordering keys outside their memory estimate. Window admission reserves the input buffers plus eval::cost, but that cost charges only fixed per-group storage. Evaluation subsequently copies winning bytes without a further reservation. Even a primitive-valued max_by(v, long_string_key) window creates uncharged key storage while retaining the input. Query-controlled payload sizes and partition counts can therefore exceed the intended memory allowance and threaten process availability.
Medium · reliability · inferred: External replay breaks the supported whole-partition contract for byte-backed MaxBy/MinBy. copy_partition folds whole-partition aggregates through Fold::Rows, whose fold method calls update_running even when every snapshot flag is false. The new byte-state guard rejects any such call. A query accepted and executable in memory therefore fails with an unsupported-feature error when its partition requires external evaluation, disabling the existing memory-pressure fallback for these operations.
Security review details
Security Blast Radius
inferred — The resource exposure requires the ability to execute the new window functions and influence their byte-valued inputs or expressions. Extra retained payload scales with winning values and keys across evaluated partitions. Exhaustion could affect the hosting process, but cross-user or cross-tenant impact is not established by the supplied deployment evidence.
Security Findings and Attack Paths
inferred — A whole-partition max_by(v, k) query with a primitive value and a long string key can pass input-buffer reservation, then allocate an additional owned key per winning partition without charging its length. The key is not returned, so result size does not reveal or bound that retained state. This is newly reachable through the added aggregate support; an actual process-exhaustion outcome was not exercised.
Trust Boundaries and Controls
observed — The running-frame guard rejects MaxBy/MinBy when either value or key is byte-backed, preventing unaccounted per-row byte snapshots. AnyValue uses the existing in-memory byte-selection path, which gathers views of input buffers. These controls do not reserve the owned payloads used by whole-partition MaxBy/MinBy.
Resilience and Maintainability Implications
inferred — External evaluation is not a working recovery path for whole-partition byte-backed MaxBy/MinBy: its whole-partition fold enters the running-update API and encounters the byte-state rejection. Support consequently depends on whether memory pressure selects external replay, rather than only on the declared frame and input types.
Hardening Proposals
proposed — Make ownership of variable-length window state budgeted for both value and key, and distinguish whole-partition folding from snapshot-producing running updates during external recovery.
Pre-merge checks: 3 · 2
Failed checks: 2 warnings
Check name
Status
Explanation
Resolution
Description check
The description provides a detailed summary and test coverage overview, but it does not include the required Tests run section with commands and result lines or the required Checklist.
Add the template sections. Under Tests run, list the exact commands executed and their result lines. Include the completed Checklist, including DCO, licensing, test-first coverage, error handling, distributed readiness, API, dependency, and…
Docstring Coverage
Docstring coverage is 70.43% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 115 functions across 21 files. (1 skipped…
Write docstrings for the functions missing them to satisfy the coverage threshold.
Passed checks: 3 passed
Check name
Status
Explanation
Title check
The title clearly and concisely names the main aggregate functions added by the pull request.
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: Description check
Resolution
Add the template sections. Under Tests run, list the exact commands executed and their result lines. Include the completed Checklist, including DCO, licensing, test-first coverage, error handling, distributed readiness, API, dependency, and performance reviews.
Full details: Docstring Coverage
Explanation
Docstring coverage is 70.43% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 115 functions across 21 files. (1 skipped: 1 unsupported.)
Resolution
Write docstrings for the functions missing them to satisfy the coverage threshold.
Finishing Touches: 💡 1
⚔️ Resolve merge conflicts: 💡
Resolve merge conflict in branch feat/aggregate-boolean-and-by
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Actionable comments posted: 3
🪄 Fix comments: Fix CodeRabbit comments on this PR
Prompt to fix review comments
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Inline comments:
Review comments at @crates/krishiv-plan-codec/tests/roundtrip.rs:
- Line 817: Update the `CodecError::Unsupported` check in the `matches!`
assertion to compare the complete expected error message rather than checking
whether it contains “aggregate function”.
Review comments at @crates/krishiv-session/tests/aggregate_goldens.rs:
- Line 256: Update the `case.query` ordering check so window-level `ORDER BY`
clauses do not cause result rows to be compared in sequence; detect only
result-level ordering, or compare unordered results as a sorted multiset.
Preserve sequential comparison when the complete query orders its results.
- Line 318: The current `any_value(false) OVER w AS any_value` selection covers
only Boolean values, not nullable string or binary inputs under default null
handling. Add a focused default-mode window case with a NULL followed by a
value, and verify its exact output against a stated Spark 4.2 oracle or
generated golden; do not duplicate the existing `IGNORE NULLS` case.
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Registers the Spark aggregates count_if, bool_and/every, bool_or/some/any,
any_value(x [, ignoreNulls]) and the two-argument max_by/min_by, in grouped,
global and window form. first, first_value, last and last_value (with the
ignoreNulls flag) were already registered and are unchanged.
count_if and the boolean aggregates reuse existing accumulators. The planner
maps registry names to executor aggregates in one place, shared by the
aggregate and window planners: count_if(c) is count(nullif(c, false)),
bool_and/every are min and bool_or/some/any are max over a boolean argument.
The registry types their argument as boolean, so a wrong argument type is
DATATYPE_MISMATCH.UNEXPECTED_INPUT_TYPE. Result names keep the called
function's name.
New accumulator variants: any_value (the first row's value, or the first
non-NULL with ignoreNulls, kept in the state first and last use) and
max_by/min_by. max_by/min_by keep the value and the ordering key as two
state columns; a NULL key skips the row, a NULL value is a result, the first
row wins a tie and a merge keeps the earlier partial on a tie, so partial
states from any split of the input in order give the single-phase answer.
Keys compare as min and max do (NaN largest, -0.0 = 0.0). Both columns are
accounted in the table's reservation (Accumulator::projected takes the key
column too) and spill through the existing partial-state path. A running
window frame over string or binary max_by/min_by is refused with
KRISHIV_UNSUPPORTED_FEATURE (per-row snapshots would not be accounted);
whole-partition frames take them.
The three-argument max_by/min_by returns an array and stays unsupported
(KRISHIV_UNSUPPORTED_FEATURE from the registry rule), as do collect_list,
collect_set and array_agg.
Also: Spark names any_value(x, true) as any_value(x), and IGNORE NULLS /
RESPECT NULLS apply to any_value as to first and last. The planner now maps
mean to avg for grouped aggregates as the window planner already did.
The plan codec does not encode the new functions yet: the encoder returns
its typed "cannot encode" error for them, and the proto is unchanged.
New public items: AggregateFunction::{AnyValue, MaxBy, MinBy},
AggregateExpr::by (all krishiv-exec) and Aggregate::{AnyValue, MaxBy, MinBy}
(krishiv-functions). The function-matrix manifest gains the new functions'
cases that need VALUES, which the parser does not read yet.
Tests: Spark 4.2.0 goldens for the boolean aggregates (18 statements of
group-by.sql) and the any_value window statement of window.sql replay
through a session; unit tests cover semantics, merges (including a property
test over arbitrary splits), both table layouts, spills and accounting; SQL
tests cover count_if, any_value, max_by and min_by.
Not verified: count_if, any_value with a flag, max_by and min_by have no
statements in the vendored goldens (max-min-by-k.sql only uses the
three-argument form); their expectations follow Spark's definitions. Not
claimed: every("true") needs a string to boolean cast, which the engine
does not have; the GROUP BY ALL statements over t1 need GROUP BY ALL.