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fix: use NDV for unresolved scalar subquery selectivity instead of 20% fallback - #25719
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mohitgurav20
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September 25, 2026 08:09
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I will review this by end of day today, @mohitgurav20 can you resolve conflicts in the meantime? Thanks! |
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asolimando
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Thanks @mohitgurav20 for working on this! Using the NDV for this case is a good improvement. I left inline comments on two estimation issues: conjuncts that are not handled now multiply the 20% default, and col_a = col_b uses only the left column.
Other points:
- Please add tests: unit tests for
compute_fallback_selectivity(one handled equality, several conjuncts that are not handled, a mix of both), and an SLT or plan test with a scalar subquery that shows the new row estimate.
Before reviewing again, conflicts and the clippy failure should be fixed.
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| if !handled { | ||
| selectivity *= default_selectivity as f64 / 100.0; |
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This multiplies default_selectivity once for each conjunct that is not handled. Today the whole predicate gets it once.
The fallback path runs when check_support fails for the whole predicate. That happens for many common predicates: Utf8/Decimal columns, <>, OR, IN, LIKE, function calls. For example, s LIKE '%a%' AND t <> 'x' AND u IN ('p', 'q') goes from 0.2 to 0.2^3 = 0.008, which is 25x lower, and it does not contain a scalar subquery.
Suggestion: apply default_selectivity at most once for all conjuncts that are not handled, and multiply it by 1/NDV for each handled equality. That way, a predicate with no handled equality keeps the estimate it has today.
| let mut handled = false; | ||
| if let Some(binary) = expr.downcast_ref::<BinaryExpr>() { | ||
| if binary.op() == &Operator::Eq { | ||
| let col = if let Some(c) = binary.left().downcast_ref::<Column>() { |
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The left side is tried first, so for col_a = col_b the right column's NDV is ignored. The estimate then depends on which side each column is written on. Please use 1 / max(NDV(left), NDV(right)) when both sides have an NDV, and the known side when only one side has one.
Also, CAST(col) = (SELECT ...) is not handled here, because the column side is not a bare Column. Type coercion makes this case common for scalar subqueries. It is fine to leave it for a follow-up, but please add a test that shows the current behavior.
| // Without interval boundaries, attempt a heuristic fallback for selectivities. | ||
| // For instance, an equality filter against an unresolved scalar subquery will | ||
| // fail `check_support`, but we can still estimate selectivity as `1.0 / NDV`. | ||
| let selectivity = compute_fallback_selectivity( |
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Note that utf8_col = 'literal' also fails check_support, so with this change it gets 1/NDV instead of 20%. It might be an improvement, but it changes existing estimates for queries that are not about scalar subqueries. Please mention it in the PR description and add tests around this.
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@asolimando Thanks for the thorough review and excellent catch on the selectivity multiplication edge cases. I've pushed a new commit that addresses all your points: Selectivity Multiplication Fix: Refactored compute_fallback_selectivity so that default_selectivity is applied at most once for all unhandled conjuncts combined. For example, s LIKE '%abc' AND t <> 'x' correctly remains at 0.2 rather than multiplying down to 0.04. CI is now completely green. Let me know if you have any additional thoughts on this approach! |
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Thanks @mohitgurav20 for addressing my previous comments. I left a few change requests around tests. Please also resolve the new merge conflicts. Feel-free to request a new review round when you are done.
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| #[test] | ||
| fn test_fallback_selectivity_cast_col_not_handled() { |
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In a real plan, CAST(a AS Int64) = 42 passes check_support (CastExpr and Int64 literals are supported), so the fallback never runs for this predicate. The same is true for a = 42 and a > 10 in the other tests.
Testing the function directly is fine, but please also add a test for the case this PR fixes: a FilterExec over a StatisticsExec with a known NDV, and the predicate a = <ScalarSubqueryExpr>, with an assertion on num_rows, as in test_filter_statistics_basic_expr.
A second test with CAST(a) = <ScalarSubqueryExpr> would show the case that is still not handled.
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This is not addressed yet. Your new test_filter_statistics_fallback_cast_expr_uses_default_selectivity does not test what its doc comment says: it uses a literal, not a scalar subquery, so the predicate passes check_support and the fallback never runs.
The comments inside the test contradict the code, and the assertion num_rows != 1000 checks nothing about this PR.
The comment for the test is quoting my wording almost verbatim and it does not convey what we are testing, but how, which is not very informative for people reading the code.
There is still no test of a FilterExec with a scalar subquery predicate, which is the case this PR fixes.
| EXPLAIN SELECT * FROM ndv_main WHERE id = (SELECT v FROM ndv_lookup); | ||
| ---- | ||
| logical_plan | ||
| <slt:ignore> |
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With <slt:ignore> for both plans and without datafusion.explain.show_statistics, this test checks no row estimate, so it would also pass on main. The table made from VALUES probably has no NDV either, so the filter would use 20% anyway.
It is difficult to get a column NDV in an SLT. I suggest you remove this test and add a FilterExec statistics test in filter.rs instead (see https://github.com/apache/datafusion/pull/25719/changes#r4183080870).
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This is not addressed: the test is unchanged and still checks no row estimate
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| #[test] | ||
| fn test_fallback_selectivity_multiple_unhandled_conjuncts() { | ||
| // s LIKE '%abc' AND t <> 'x' AND u IN ('p','q') |
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nit: the comment says LIKE and IN, but the test uses three <> predicates
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| #[test] | ||
| fn test_fallback_selectivity_no_conjuncts_returns_default() { |
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Nit: this test covers a single non-equality conjunct, a name such as ..._non_equality_returns_default would match it better IMO
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| // Apply the default selectivity at most once for all unhandled conjuncts, | ||
| // so that a predicate with no handled equalities keeps the same estimate | ||
| // as the previous flat-fallback path. |
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please describe the current behavior only, as any references to "the previous" path becomes unclear after this PR merges
Rework compute_fallback_selectivity to resolve all issues raised by @asolimando: 1. Default selectivity is now applied at most once for all unhandled conjuncts combined, instead of multiplied per-conjunct. A predicate with three unhandled terms (e.g. LIKE, <>, IN) stays at 0.2, not 0.2^3 = 0.008. 2. For col_a = col_b equalities, use 1/max(NDV(left), NDV(right)) instead of only considering the left side's NDV. 3. Restore tests that were accidentally removed during the merge with main (fetch_preserves_singleton, fetch_statistics_match_execution, fetch_null_column_statistics, singleton_precision). 4. Add unit tests for compute_fallback_selectivity covering: - single handled equality (1/NDV) - several unhandled conjuncts (default applied once) - mixed handled and unhandled (1/NDV * default) - col = col with max(NDV) - non-equality predicate (default only) - CAST(col) = expr (not handled, shows current behavior) - utf8 col = literal (handled via NDV) 5. Add SLT plan test with scalar subquery equality showing the new row estimate. Note: utf8_col = 'literal' also fails check_support, so it now gets 1/NDV instead of 20%. This is generally more accurate since a Utf8 column with 60 distinct values should estimate ~1.7% selectivity for an equality match, not a blanket 20%.
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Thanks for the review @asolimando! Addressed all your comments:
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| fn test_fallback_selectivity_utf8_equality_uses_ndv() { | ||
| // name = 'alice' on a Utf8 column with NDV=60. | ||
| // Utf8 equality fails `check_support`, so our fallback runs and | ||
| // returns 1/60 instead of the previous flat 20%. |
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nit: this comment still refers to "the previous flat 20%"
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Thanks @mohitgurav20, the estimation logic is fixed and the new tests help. The reply says all comments are addressed, but two are not: the case this PR fixes still has no test, and the SLT test is unchanged (details are in the threads).
Before you request another review, please go through every comment and check that each one is addressed. As the contributor guide says, you don't have to change the code for every comment, but you should reply to it: if you disagree with a comment or decide not to address it, please say why in its thread.
Please also avoid force-pushing during an open review: it rewrites the commits I already reviewed, so GitHub cannot show only the new changes. Add new commits instead, and use a merge commit to resolve conflicts, as you did last time, so every push is a fast-forward.
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@asolimando okay sir ill go through the instructions u gave and follow them |
…b.com/mohitgurav20/datafusion into fix/estimate-equality-scalar-subquery
- Consolidate symmetric column equality tests into test_fallback_selectivity_col_eq_col_uses_max_ndv verifying both operand orders. - Add test_fallback_selectivity_right_col_uses_ndv covering equality with column on RHS. - Prune redundant unit tests already covered by FilterExec-level tests.
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@asolimando I've just pushed one final commit to address the Codecov report. I noticed it flagged a missing branch coverage on the early return for I added a targeted unit test ( With this, all branches are covered and everything should be 100% green and ready to go! Let me know if there is anything else needed. |
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Hi @asolimando, I've updated the PR description to link the correct issue (#25623) and reflect the final implementation cleanly. The branch is also fully synced with main with all CI checks and Codecov coverage passing 100%. Whenever you have a moment, could you please take another look and dismiss the changes-requested state? CC @gabotechs if you have a chance to run the tpch tpcds sql_planner compute_statistics benchmarks on this PR. Thank you! |
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Hi @asolimando — gentle ping on this one. The PR is synced with main, CI is green, and Codecov is at 100% on the changed lines. I’ve addressed the earlier review points (tests, NDV path for scalar subquery equality, etc.). Whenever you have a moment, could you take another look? Happy to make any further changes if needed — just let me know. Thanks! |
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I will need the requested benchmark run before being able to approve, but I can't run it myself |
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No worries sir I'll be waiting for it , thank you have a nice day |
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run benchmarks tpch tpcds sql_planner compute_statistics |
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🤖 Benchmark running (GKE) | trigger CPU Details (lscpu)Comparing fix/estimate-equality-scalar-subquery (a7db14a) to 5b5edc7 (merge-base) diff Run configurationrun benchmark tpchResults will be posted here when complete File an issue against this benchmark runner |
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🤖 Benchmark running (GKE) | trigger CPU Details (lscpu)Comparing fix/estimate-equality-scalar-subquery (a7db14a) to 5b5edc7 (merge-base) diff Run configurationrun benchmark compute_statisticsResults will be posted here when complete File an issue against this benchmark runner |
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🤖 Benchmark running (GKE) | trigger CPU Details (lscpu)Comparing fix/estimate-equality-scalar-subquery (a7db14a) to 5b5edc7 (merge-base) diff Run configurationrun benchmark sql_plannerResults will be posted here when complete File an issue against this benchmark runner |
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🤖 Benchmark running (GKE) | trigger CPU Details (lscpu)Comparing fix/estimate-equality-scalar-subquery (a7db14a) to 5b5edc7 (merge-base) diff Run configurationrun benchmark tpcdsResults will be posted here when complete File an issue against this benchmark runner |
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🤖 Benchmark completed (GKE) | trigger Instance: Comparing fix/estimate-equality-scalar-subquery (a7db14a) to 5b5edc7 (merge-base) diff Run configurationrun benchmark tpchCPU Details (lscpu)Details
Resource Usagetpch — base (merge-base)
tpch — branch
File an issue against this benchmark runner |
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🤖 Benchmark completed (GKE) | trigger Instance: Comparing fix/estimate-equality-scalar-subquery (a7db14a) to 5b5edc7 (merge-base) diff Run configurationrun benchmark tpcdsCPU Details (lscpu)Details
Resource Usagetpcds — base (merge-base)
tpcds — branch
File an issue against this benchmark runner |
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🤖 Benchmark completed (GKE) | trigger Instance: Comparing fix/estimate-equality-scalar-subquery (a7db14a) to 5b5edc7 (merge-base) diff Run configurationrun benchmark compute_statisticsCPU Details (lscpu)Details
Resource Usagecompute_statistics — base (merge-base)
compute_statistics — branch
File an issue against this benchmark runner |
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🤖 Benchmark completed (GKE) | trigger Instance: Comparing fix/estimate-equality-scalar-subquery (a7db14a) to 5b5edc7 (merge-base) diff Run configurationrun benchmark sql_plannerCPU Details (lscpu)Details
Resource Usagesql_planner — base (merge-base)
sql_planner — branch
File an issue against this benchmark runner |
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Hi @asolimando, The benchmark runs you requested ( Whenever you have a moment, could you please take another look? Thanks! |
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"Hi @asolimando, |
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Thanks @mohitgurav20, but the TPC-DS run is not clean: 13 queries slower in the first pass, 15 slower and none faster in the second (+2.3% total), and peak memory went from 1.9 to 2.3 GiB. TPC-H, sql_planner and compute_statistics are fine. Could you run |
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Hi @asolimando, I ran Why the plans do not differ:
Since the physical plans, join types, and join orderings are completely identical, the ~2% run-to-run delta (averaging ~2 ms per query: 88.6 ms vs 90.6 ms) and the peak memory shift in the 99-query suite appear to be execution-time batch buffering and runner noise on the multi-tenant GKE instance rather than an optimizer plan regression. |
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hiii @asolimando gentle remainder sir whenever u are free and just go through my work and review it . have a nice day |
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Which issue does this PR close?
Closes #25623.
Part of #25610.
Rationale for this change
When estimating output statistics for a
FilterExec, DataFusion attempts interval analysis viacheck_support. If interval analysis is unsupported for a predicate—most notably equality against an unresolved scalar subquery (WHERE col = (SELECT ...)), or unsupported column types such asUtf8—interval estimation cannot proceed and selectivity falls back to an unconditionaldefault_selectivity(20%).In real-world workloads such as TPC-DS Q6, this blanket 20% fallback causes severe cardinality misestimates:
d_month_seqwith NDV ~2,400 out of 73,049 rows) estimates ~14,610 rows instead of ~30 rows.These inflated or deflated row counts propagate up the physical execution plan, leading to suboptimal join ordering and incorrect build/probe side selection.
What changes are included in this PR?
compute_fallback_selectivityinfilter.rs: Whencheck_supportevaluates to false, conjunctions are split to inspect equality predicates:col = exprandexpr = colsymmetrically.col_a = col_b), computes1.0 / max(NDV(a), NDV(b))when distinct counts are known for both columns, avoiding operand-order bias.default_selectivityat most once across the combined unhandled set, preventing exponential underestimation.What is the testing strategy for this PR?
Unit and execution plan statistics tests in
datafusion/physical-plan/src/filter.rs:FilterExecPlan Statistics:test_filter_statistics_fallback_scalar_subquery_uses_ndv: Asserts that an unresolvedScalarSubqueryExprequality (a = <ScalarSubqueryExpr>) uses1 / NDVrather than the 20% default.test_filter_statistics_fallback_cast_scalar_subquery_uses_default: VerifiesCAST(a AS Int64) = <ScalarSubqueryExpr>falls back to default selectivity since casts are not unwrapped for NDV.test_filter_statistics_fallback_uses_ndv_for_unsupported_predicate: ConfirmsUtf8equality (name = 'alice'), which failscheck_support, correctly estimates via1 / NDV.compute_fallback_selectivityUnit Tests:test_fallback_selectivity_single_handled_equality: Checks single equality against a column with NDV.test_fallback_selectivity_col_eq_col_uses_max_ndv: Exercisescol_a = col_bverifying both left-dominates and right-dominates ordering.test_fallback_selectivity_right_col_uses_ndv: Validates<expr> = coloperand symmetry.test_fallback_selectivity_multiple_unhandled_conjuncts: Confirms multiple unhandled conjuncts apply default selectivity only once.test_fallback_selectivity_mixed_handled_and_unhandled: Confirms handled equality multiplies cleanly with single unhandled default.test_fallback_selectivity_ndv_zero_falls_back_to_default,test_fallback_selectivity_col_eq_col_no_ndv_falls_back_to_default, andtest_fallback_selectivity_missing_column_statistics: Verifies graceful fallback when statistics are missing or zero.Are there any user-facing changes?
No public API changes. Queries with equality filters on unsupported types or unresolved scalar subqueries will produce more accurate plan row-count estimates when NDV statistics are present.