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Add StatisticsContext parameter to partition_statistics#21815

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asolimando wants to merge 6 commits intoapache:mainfrom
asolimando:asolimando/partition-statistics-context
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Add StatisticsContext parameter to partition_statistics#21815
asolimando wants to merge 6 commits intoapache:mainfrom
asolimando:asolimando/partition-statistics-context

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@asolimando
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@asolimando asolimando commented Apr 23, 2026

Which issue does this PR close?

Closes #20184

Rationale for this change

ExecutionPlan::partition_statistics forces each operator to re-fetch child statistics internally, causing redundant subtree walks in deep plans and making it impossible to inject enriched statistics from external sources (e.g., expression-level analyzers, custom statistics providers).

What changes are included in this PR?

  • partition_statistics is deprecated in favor of partition_statistics_with_context, which accepts a StatisticsContext carrying pre-computed child statistics. The default implementation delegates to the deprecated method, so existing custom operators continue to work unchanged. Migration guide added to docs/source/library-user-guide/upgrading/54.0.0.md.

  • StatisticsContext includes a shared StatsCache keyed by (plan node pointer, partition), eliminating redundant subtree walks within a single compute_statistics call. The cache is scoped to one call (not persisted across optimizer rules).

  • compute_statistics_inner always pre-computes children with partition=None. ctx.child_stats() always contains overall statistics. Partition-preserving operators request per-partition child stats on demand via ctx.compute_child_statistics(child, partition). Partition-merging operators use ctx.child_stats() directly.

  • Criterion micro-benchmark on two plan shapes from [EPIC] Improve query planning speed #19795:

    • CoalescePartitionsExec chain (depth 50): ~25x speedup over non-shared-cache baseline
    • CrossJoinExec binary tree (depth 7, 128 leaves): ~3x speedup, mirrors physical_many_self_joins from sql_planner.rs
  • All direct plan.partition_statistics() calls in optimizer rules and tests are replaced with compute_statistics(plan, partition).

Tests

Existing tests pass unchanged. New unit test child_stats_always_returns_overall_stats verifies the overall-stats contract.

What remains for follow-up

  • Cross-call StatsCache with stable node IDs (Option 2 from review discussion)
  • Adding expression-level analyzers and custom statistics providers to StatisticsContext

Test plan

  • cargo fmt --all
  • cargo clippy --all-targets --all-features -- -D warnings
  • cargo test --profile ci --all-features on affected crates
  • Criterion benchmark: 5-run average, ~25x (coalesce chain) and ~3x (cross-join tree) speedup

Disclaimer: I used AI to assist in the code generation, I have manually reviewed the output and it matches my intention and understanding.

@github-actions github-actions Bot added documentation Improvements or additions to documentation optimizer Optimizer rules core Core DataFusion crate datasource Changes to the datasource crate physical-plan Changes to the physical-plan crate labels Apr 23, 2026
@asolimando
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Hi @xudong963, I have opened the PR as a prerequisite for #21122, as discussed.

This is a breaking change and I therefore added a section under .../library-user-guide/upgrading/54.0.0.md‎, I have checked around what usually goes there, but I'd appreciate if you could take a deeper look and confirm if I captured what's expected for the update guide.

Looking forward to your feedback!

@xudong963
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@asolimando thanks, I'll review it next Monday! /cc @jonathanc-n

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@asolimando thanks, I'll review it next Monday! /cc @jonathanc-n

Gentle reminder @xudong963 :)

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@asolimando thanks! I'm sorry that I'm busy with others this week.

This PR doesn't fully solve the problem it claims to. The stated goal in the PR description and #20184 is to eliminate exponential recomputation. But for any plan containing a CoalescePartitionsExec, SortPreservingMergeExec, RepartitionExec, HashJoinExec (CollectLeft/Auto), CrossJoinExec, or NestedLoopJoinExec — which is most non-trivial plans — the operator restarts a fresh bottom-up walk from inside its own partition_statistics IIUC. So the recomputation isn't gone;

Caching sounds good, how about making caching part of StatisticsContext from day one, then we can have some benchmarks to show off the gains which will be easier for the community to accept the PR, wdyt?

///
/// [`StatisticsContext`]: crate::statistics_context::StatisticsContext
/// [`compute_statistics`]: crate::statistics_context::compute_statistics
fn partition_statistics(
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Noted and I will make sure to keep both APIs in the future! I will address this in the next iteration on the code and will resolve the discussion at that point.

let child_stats = plan
.children()
.iter()
.map(|child| compute_statistics(child.as_ref(), partition))
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compute_statistics always recurses with the same partition. For partition-merging operators this is wasted work because they'll discard the context and recompute with None anyway

@asolimando
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@asolimando thanks! I'm sorry that I'm busy with others this week.

This PR doesn't fully solve the problem it claims to. The stated goal in the PR description and #20184 is to eliminate exponential recomputation. But for any plan containing a CoalescePartitionsExec, SortPreservingMergeExec, RepartitionExec, HashJoinExec (CollectLeft/Auto), CrossJoinExec, or NestedLoopJoinExec — which is most non-trivial plans — the operator restarts a fresh bottom-up walk from inside its own partition_statistics IIUC. So the recomputation isn't gone;

Caching sounds good, how about making caching part of StatisticsContext from day one, then we can have some benchmarks to show off the gains which will be easier for the community to accept the PR, wdyt?

Thank you for your input @xudong963, no need to apologies, it's understandable!

You raise a fair point, we fully avoid the recomputation only for linear plans, but operators that call compute_statistics(child, None) internally don't benefit. This is noted in the "What remains for follow-up" section but I agree it might not be enough for the first iteration, and I anyway should have marked "partially closes #20184".

Re. the cache, I identified the need for the StatisticsRegistry already, and we discussed with @kosiew in the related PR (#21483, comment, branch asolimando/statistics-planner-with-statscache-v2). We agreed to defer it to limit scope, but this is the right place to discuss it.

One limitation I identified on the StatsCache (as I called it there), is around the cache key, which should "identify" an ExecutionPlan, which doesn't have any stable id other than its memory pointer ( so the cache key is effectively (Arc::as_ptr, partition)), but I am concerned of nodes being disposed (and re-used).

Cache lifecycle/scope:

  1. single invocation of compute_statistics (as described in Let partition_statistics accept pre-computed children statistics #20184): if we agree on this, then the concern is not valid, as the plan tree is "stable" during the lifetime. When e.g. CoalescePartitionsExec calls compute_statistics(child, None) internally, the cache already has the subtree results, fully eliminating redundant walks.

  2. multiple invocations of compute_statistics (same rule or cross-rules): here we necessarily need a stable node ID and we can't rely on the pointer, since nodes can be dropped/recreated

The scope of #20184 is, in my understanding, 1. (single walk), if you agree with that, I plan to use (Arc::as_ptr, partition) as cache key, and introducing node IDs and expanding the cache lifetime IMO be tackled as a followup (I can create issues for that, if the direction is confirmed), as with this solution we should already see computational benefits.

Re. benchmarks, do you have a specific workload in mind (e.g., TPC-DS, Q99)? Also, could I be added to the allowlist to trigger benchmark runs so I can iterate without requiring manual re-runs, in case I need multiple iterations?

WDYT?

@xudong963
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Thanks for the thoughtful response @asolimando — the framing is exactly right, and the prior discussion with @kosiew in #21483 is helpful context.

On scope: agreed, let's land per-call caching in this PR (your Option 1) and treat cross-call caching with stable node IDs as a follow-up. Could you open an issue for Option 2 so we don't lose track?

On the cache key: (Arc::as_ptr, partition) is safe within a single synchronous compute_statistics walk — the Arcs are held by the plan tree and can't be dropped during the call, so pointer reuse isn't a concern. Good call.

On benchmarks: I'd avoid full TPC-DS Q99 — statistics computation is a small fraction of total query time and will get lost in noise. A targeted micro-bench is more informative:

  • Build a deeply nested plan (e.g., a 10+ deep UnionExec chain, or a chain of hash joins + repartitions) and time compute_statistics(plan, None) before/after this PR.
  • Optionally reuse a reproducer from [EPIC] Improve query planning speed #19795 (planning-speed EPIC) since deep plans are exactly that issue's pain point.

That should cleanly demonstrate the gain.

@asolimando
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Thanks for the thoughtful response @asolimando — the framing is exactly right, and the prior discussion with @kosiew in #21483 is helpful context.

On scope: agreed, let's land per-call caching in this PR (your Option 1) and treat cross-call caching with stable node IDs as a follow-up. Could you open an issue for Option 2 so we don't lose track?

On the cache key: (Arc::as_ptr, partition) is safe within a single synchronous compute_statistics walk — the Arcs are held by the plan tree and can't be dropped during the call, so pointer reuse isn't a concern. Good call.

On benchmarks: I'd avoid full TPC-DS Q99 — statistics computation is a small fraction of total query time and will get lost in noise. A targeted micro-bench is more informative:

  • Build a deeply nested plan (e.g., a 10+ deep UnionExec chain, or a chain of hash joins + repartitions) and time compute_statistics(plan, None) before/after this PR.
  • Optionally reuse a reproducer from [EPIC] Improve query planning speed #19795 (planning-speed EPIC) since deep plans are exactly that issue's pain point.

That should cleanly demonstrate the gain.

Thanks for the confirmation and the clarifications, I will hopefully get to it early next week and I will ping you back as soon as I will have some updates!

asolimando added 6 commits May 3, 2026 19:31
Introduce StatisticsContext that carries pre-computed child statistics
and external context for statistics computation. Change the
ExecutionPlan::partition_statistics signature to accept it, and add
compute_statistics() utility for bottom-up computation with automatic
child stats threading.

Update all ~35 in-tree ExecutionPlan implementations and ~40 call
sites. Passthrough operators return ctx.child_stats() directly,
transform operators use it instead of re-fetching from children,
and operators that always need overall child stats (RepartitionExec,
CoalescePartitionsExec, SortPreservingMergeExec, SortExec non-preserving,
HashJoinExec CollectLeft/Auto, CrossJoinExec, NestedLoopJoinExec)
call compute_statistics with None internally.
Non-breaking change per API health policy: existing impls continue
to work via default delegation. Fixes missed ScalarSubqueryExec.
Memoize results within a single compute_statistics invocation using
pointer-based cache keys. Operators now use ctx.compute_child_statistics
instead of calling compute_statistics directly, so partition-merging
and asymmetric join operators hit the cache for subtrees already walked.
Coalesce chain and cross-join tree (apache#19795) benchmarks comparing
cached vs non-shared-cache statistics computation.
@asolimando asolimando force-pushed the asolimando/partition-statistics-context branch from e135e8a to a8a3d6c Compare May 3, 2026 18:48
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github-actions Bot commented May 3, 2026

Thank you for opening this pull request!

Reviewer note: cargo-semver-checks reported the current version number is not SemVer-compatible with the changes in this pull request (compared against the base branch).

Details
     Cloning apache/main
    Building datafusion v53.1.0 (current)
       Built [  79.147s] (current)
     Parsing datafusion v53.1.0 (current)
      Parsed [   0.031s] (current)
    Building datafusion v53.1.0 (baseline)
       Built [  76.999s] (baseline)
     Parsing datafusion v53.1.0 (baseline)
      Parsed [   0.031s] (baseline)
    Checking datafusion v53.1.0 -> v53.1.0 (no change; assume patch)
     Checked [   0.735s] 222 checks: 222 pass, 30 skip
     Summary no semver update required
    Finished [ 158.876s] datafusion
    Building datafusion-datasource v53.1.0 (current)
       Built [  33.983s] (current)
     Parsing datafusion-datasource v53.1.0 (current)
      Parsed [   0.027s] (current)
    Building datafusion-datasource v53.1.0 (baseline)
       Built [  34.063s] (baseline)
     Parsing datafusion-datasource v53.1.0 (baseline)
      Parsed [   0.028s] (baseline)
    Checking datafusion-datasource v53.1.0 -> v53.1.0 (no change; assume patch)
     Checked [   0.305s] 222 checks: 222 pass, 30 skip
     Summary no semver update required
    Finished [  70.295s] datafusion-datasource
    Building datafusion-physical-optimizer v53.1.0 (current)
       Built [  34.885s] (current)
     Parsing datafusion-physical-optimizer v53.1.0 (current)
      Parsed [   0.020s] (current)
    Building datafusion-physical-optimizer v53.1.0 (baseline)
       Built [  34.633s] (baseline)
     Parsing datafusion-physical-optimizer v53.1.0 (baseline)
      Parsed [   0.021s] (baseline)
    Checking datafusion-physical-optimizer v53.1.0 -> v53.1.0 (no change; assume patch)
     Checked [   0.133s] 222 checks: 222 pass, 30 skip
     Summary no semver update required
    Finished [  71.204s] datafusion-physical-optimizer
    Building datafusion-physical-plan v53.1.0 (current)
       Built [  30.206s] (current)
     Parsing datafusion-physical-plan v53.1.0 (current)
      Parsed [   0.117s] (current)
    Building datafusion-physical-plan v53.1.0 (baseline)
       Built [  30.493s] (baseline)
     Parsing datafusion-physical-plan v53.1.0 (baseline)
      Parsed [   0.117s] (baseline)
    Checking datafusion-physical-plan v53.1.0 -> v53.1.0 (no change; assume patch)
     Checked [   0.686s] 222 checks: 221 pass, 1 fail, 0 warn, 30 skip

--- failure trait_method_marked_deprecated: trait method #[deprecated] added ---

Description:
A trait method is now #[deprecated]. Downstream crates will get a compiler warning when using this method.
        ref: https://doc.rust-lang.org/reference/attributes/diagnostics.html#the-deprecated-attribute
       impl: https://github.com/obi1kenobi/cargo-semver-checks/tree/v0.47.0/src/lints/trait_method_marked_deprecated.ron

Failed in:
  method partition_statistics in trait datafusion_physical_plan::execution_plan::ExecutionPlan in /home/runner/work/datafusion/datafusion/datafusion/physical-plan/src/execution_plan.rs:97
  method partition_statistics in trait datafusion_physical_plan::ExecutionPlan in /home/runner/work/datafusion/datafusion/datafusion/physical-plan/src/execution_plan.rs:97

     Summary semver requires new minor version: 0 major and 1 minor checks failed
    Finished [  63.182s] datafusion-physical-plan

@asolimando
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Hey @xudong963, I've pushed new commits implementing what we discussed (force-pushed to rebase on latest main, but the first two commits (f36ef32, 12a2fc1) are unchanged from the previous push).

A walkthrough of the new commits:

  • f36ef32 adds StatisticsContext parameter to partition_statistics, keeping the old method as deprecated, as required by the API health guidelines
  • b380893 adds partition_statistics_with_context as the new entry point
  • bb09951 adds StatsCache to StatisticsContext, shared across the entire compute_statistics walk
  • 2f843ef adds a Criterion micro-benchmark on two plan shapes from [EPIC] Improve query planning speed #19795:
    • CoalescePartitionsExec chain (depth 50): ~25x speedup over non-shared-cache baseline
    • CrossJoinExec binary tree (depth 7, 128 leaves): ~3x speedup, mirrors physical_many_self_joins from sql_planner.rs
  • a8a3d6c addresses the wasted partition forwarding: compute_statistics_inner now always pre-computes children with partition=None. Partition-preserving operators request per-partition stats on demand via compute_child_statistics, so partition-merging operators use child_stats() directly instead of triggering re-walks

Re. the benchmark: the numbers are from the average of 5 local runs, and they are conservative, as the baseline still benefits from an ephemeral per-walk cache within each re-walk, the true baseline would be no caching at all, and it would show a larger gap. Since this benchmark is new, I couldn't find a better way to show a before/after run. The improvement is clear anyway, but I just wanted to mention it for completeness.

Will open a follow-up issue for cross-call caching with stable node IDs (Option 2) once this lands, as StatsCache exists nowhere at the moment, I am afraid it would be confusing if filed now.

Looking forward to your review!

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Let partition_statistics accept pre-computed children statistics

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