BE-271: HashQL: Implement `PostInline` pass #8240

indietyp · 2026-01-03T21:18:29Z

🌟 What is the purpose of this PR?

Implements the PostInline pass for HashQL MIR, which runs canonicalization optimizations after inlining to exploit newly exposed optimization opportunities such as constant propagation, dead code elimination, and branch simplification. Additionally, adds is_leaf recomputation after inlining to expose more bonuses and adjusts the config slightly.

🔍 What does this change?

Adds PostInline pass as a thin wrapper around Canonicalization with a higher iteration limit (16 vs 8 for PreInline) since inlining can expose more optimization opportunities
Ensures is_leaf status properly propagates through the call graph analysis during inlining
Adds IdVec::fill_to method for efficiently extending vectors to a specific length
Adds comprehensive UI tests covering:
- Cascading simplification after inlining
- Closure environment cleanup
- Constant propagation after inlining
- Dead code elimination from inlined functions
- Nested branch elimination
- Full showcase demonstrating complex nested conditionals collapsing to a single return value
Adds benchmark pipeline for post-inline pass

Pre-Merge Checklist 🚀

🚢 Has this modified a publishable library?

This PR:

does not modify any publishable blocks or libraries, or modifications do not need publishing

📜 Does this require a change to the docs?

The changes in this PR:

are internal and do not require a docs change

🕸️ Does this require a change to the Turbo Graph?

The changes in this PR:

do not affect the execution graph

cursor · 2026-01-03T21:18:34Z

PR Summary

Introduces a post-inlining optimization stage and aligns inlining analysis to better exploit newly exposed opportunities.

Adds transform::PostInline (canonicalization tuned for post-inline, 16 iterations) and exposes it via transform::mod; integrates into compiletest suite (mir_pass_transform_post_inline) and benchmark pipeline (PreInline → Inline → PostInline)
Refines inlining analysis/heuristics: BodyProperties now carries source; recomputes is_leaf after inlining via new FindApplyCall; CallGraph::{is_leaf,is_single_caller,unique_caller} now filter to Apply edges; tweak InlineHeuristicsConfig defaults (e.g., always_inline=16, size_penalty_factor=0.9)
Extends IdVec with from_domain_derive{,_in} helpers for allocator-aware domain-derived vectors
Bench updates to return body arrays and add an inlining scenario; numerous new UI tests validating post-inline effects (cascading simplification, constant propagation, dead code and branch elimination, closure env cleanup)

^{Written by Cursor Bugbot for commit c9fa861. This will update automatically on new commits. Configure here.}

indietyp · 2026-01-03T21:19:27Z

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augmentcode · 2026-01-03T21:21:56Z

🤖 Augment PR Summary

Summary: Introduces a new post-inlining optimization stage for HashQL MIR to better capitalize on optimization opportunities exposed by inlining.

Changes:

Adds PostInline as a thin wrapper over Canonicalization with a higher iteration cap (16) and wires it into the MIR pass pipeline.
Extends the compiletest harness with a dedicated mir/pass/transform/post-inline suite (plus D2 rendering support) and adds multiple UI fixtures showcasing post-inline simplifications.
Updates MIR bench pipeline to run PreInline → Inline → PostInline and reuses a single GlobalTransformState across stages.
Refactors inlining analysis data: BodyProperties now carries source, and cost/loop metadata is grouped in CostEstimationResidual.
Recomputes is_leaf after inlining to unlock additional heuristic bonuses on newly simplified bodies.
Tweaks inlining heuristic defaults (always_inline and size_penalty_factor).
Adds a small IdVec helper (from_domain_derive*) to build property vectors from an existing domain.

Technical Notes: The PR also adjusts call-graph “single/unique caller” logic to consider only Apply edges and adds tracing as a dependency for MIR instrumentation.

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libs/@local/hashql/mir/src/pass/transform/inline/mod.rs

libs/@local/hashql/mir/src/pass/transform/inline/find.rs

codspeed-hq · 2026-01-03T21:25:22Z

CodSpeed Performance Report

Merging this PR will degrade performance by 23.94%

_{Comparing bm/be-271-hashql-implement-postinline-pass (c9fa861) with main (7b09441)}

Summary

❌ 3 (👁 3) regressed benchmarks
✅ 14 untouched benchmarks
🆕 1 new benchmark
🗄️ 12 archived benchmarks run¹

Performance Changes

	Benchmark	`BASE`	`HEAD`	Efficiency
👁	`linear`	38.2 µs	50.3 µs	-23.94%
🆕	`inline`	N/A	232.3 µs	N/A
👁	`diamond`	68.9 µs	84.5 µs	-18.48%
👁	`complex`	100.2 µs	119.3 µs	-16.03%

12 benchmarks were run, but are now archived. If they were deleted in another branch, consider rebasing to remove them from the report. Instead if they were added back, click here to restore them. ↩

graphite-app · 2026-01-19T10:16:25Z

Merge activity

Jan 19, 10:16 AM UTC: Graphite rebased this pull request, because this pull request is set to merge when ready.

cursor

Cursor Bugbot has reviewed your changes and found 2 potential issues.

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libs/@local/hashql/mir/src/pass/transform/inline/find.rs

cursor · 2026-01-19T10:23:32Z

libs/@local/hashql/mir/src/pass/transform/inline/find.rs

+        let target_source = self.properties[ptr].source;
+        if matches!(target_source, Source::Intrinsic(_)) {
+            return ControlFlow::Continue(());
+        }


Unbounded array access in FindApplyCall may panic

Medium Severity

FindApplyCall::visit_rvalue_apply accesses self.properties[ptr] directly without bounds checking. The properties slice contains entries for user-defined bodies with sequential small DefIds (0 to N-1), but intrinsic DefIds are in the 0xFFFF_FE00 range. If a body after inlining contains an Apply call to an intrinsic, accessing properties[intrinsic_def_id] causes an out-of-bounds panic. Using self.properties.get(ptr) and treating None as "target not in domain" (continue searching) would fix this.

github-actions · 2026-01-19T11:51:56Z

Benchmark results

@rust/hash-graph-benches – Integrations

policy_resolution_large

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2002	$$28.8 \mathrm{ms} \pm 272 \mathrm{μs}\left({\color{gray}-3.134 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.24 \mathrm{ms} \pm 17.1 \mathrm{μs}\left({\color{gray}-2.191 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1001	$$12.5 \mathrm{ms} \pm 89.6 \mathrm{μs}\left({\color{gray}0.053 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 3314	$$42.8 \mathrm{ms} \pm 316 \mathrm{μs}\left({\color{gray}0.489 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$14.5 \mathrm{ms} \pm 108 \mathrm{μs}\left({\color{gray}-3.694 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 1526	$$23.8 \mathrm{ms} \pm 168 \mathrm{μs}\left({\color{gray}-0.861 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 2078	$$30.8 \mathrm{ms} \pm 196 \mathrm{μs}\left({\color{lightgreen}-28.617 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.62 \mathrm{ms} \pm 19.8 \mathrm{μs}\left({\color{lightgreen}-82.550 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 1033	$$14.6 \mathrm{ms} \pm 122 \mathrm{μs}\left({\color{lightgreen}-49.488 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_medium

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 102	$$3.64 \mathrm{ms} \pm 19.2 \mathrm{μs}\left({\color{gray}-0.384 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.86 \mathrm{ms} \pm 13.3 \mathrm{μs}\left({\color{gray}0.022 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 51	$$3.22 \mathrm{ms} \pm 15.7 \mathrm{μs}\left({\color{gray}-1.973 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 269	$$5.00 \mathrm{ms} \pm 23.3 \mathrm{μs}\left({\color{gray}-0.814 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$3.42 \mathrm{ms} \pm 15.7 \mathrm{μs}\left({\color{gray}-0.383 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 107	$$3.98 \mathrm{ms} \pm 28.7 \mathrm{μs}\left({\color{gray}-0.095 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 133	$$4.27 \mathrm{ms} \pm 23.5 \mathrm{μs}\left({\color{gray}-0.779 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.32 \mathrm{ms} \pm 16.2 \mathrm{μs}\left({\color{gray}0.830 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 63	$$3.86 \mathrm{ms} \pm 22.0 \mathrm{μs}\left({\color{gray}-1.169 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_none

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2	$$2.62 \mathrm{ms} \pm 14.1 \mathrm{μs}\left({\color{red}6.88 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.54 \mathrm{ms} \pm 14.4 \mathrm{μs}\left({\color{red}7.54 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1	$$2.65 \mathrm{ms} \pm 14.1 \mathrm{μs}\left({\color{red}9.52 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 8	$$2.87 \mathrm{ms} \pm 24.6 \mathrm{μs}\left({\color{red}8.26 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.76 \mathrm{ms} \pm 13.0 \mathrm{μs}\left({\color{red}8.50 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 3	$$2.95 \mathrm{ms} \pm 21.6 \mathrm{μs}\left({\color{red}7.76 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_small

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 52	$$2.91 \mathrm{ms} \pm 17.2 \mathrm{μs}\left({\color{gray}4.24 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.61 \mathrm{ms} \pm 13.1 \mathrm{μs}\left({\color{red}6.30 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 25	$$2.78 \mathrm{ms} \pm 17.1 \mathrm{μs}\left({\color{red}5.77 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 94	$$3.29 \mathrm{ms} \pm 17.3 \mathrm{μs}\left({\color{gray}4.31 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$2.95 \mathrm{ms} \pm 19.6 \mathrm{μs}\left({\color{red}8.64 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 26	$$3.07 \mathrm{ms} \pm 15.7 \mathrm{μs}\left({\color{gray}5.00 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 66	$$3.16 \mathrm{ms} \pm 14.8 \mathrm{μs}\left({\color{gray}4.81 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.82 \mathrm{ms} \pm 18.6 \mathrm{μs}\left({\color{red}5.61 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 29	$$3.06 \mathrm{ms} \pm 19.1 \mathrm{μs}\left({\color{gray}4.95 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_complete

Function	Value	Mean	Flame graphs
entity_by_id;one_depth	1 entities	$$39.4 \mathrm{ms} \pm 141 \mathrm{μs}\left({\color{gray}1.19 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	10 entities	$$79.4 \mathrm{ms} \pm 515 \mathrm{μs}\left({\color{red}5.14 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	25 entities	$$44.3 \mathrm{ms} \pm 172 \mathrm{μs}\left({\color{gray}0.383 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	5 entities	$$45.7 \mathrm{ms} \pm 241 \mathrm{μs}\left({\color{gray}-0.160 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	50 entities	$$55.0 \mathrm{ms} \pm 283 \mathrm{μs}\left({\color{gray}2.94 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	1 entities	$$40.1 \mathrm{ms} \pm 170 \mathrm{μs}\left({\color{gray}-2.204 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	10 entities	$$419 \mathrm{ms} \pm 887 \mathrm{μs}\left({\color{gray}1.94 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	25 entities	$$91.1 \mathrm{ms} \pm 383 \mathrm{μs}\left({\color{gray}-4.681 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	5 entities	$$86.8 \mathrm{ms} \pm 433 \mathrm{μs}\left({\color{gray}1.93 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	50 entities	$$283 \mathrm{ms} \pm 587 \mathrm{μs}\left({\color{gray}1.11 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	1 entities	$$14.7 \mathrm{ms} \pm 76.1 \mathrm{μs}\left({\color{gray}-2.190 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	10 entities	$$15.2 \mathrm{ms} \pm 68.6 \mathrm{μs}\left({\color{gray}2.50 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	25 entities	$$15.3 \mathrm{ms} \pm 78.7 \mathrm{μs}\left({\color{gray}0.987 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	5 entities	$$15.0 \mathrm{ms} \pm 79.0 \mathrm{μs}\left({\color{gray}-0.146 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	50 entities	$$18.5 \mathrm{ms} \pm 96.9 \mathrm{μs}\left({\color{gray}2.26 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_linkless

Function	Value	Mean	Flame graphs
entity_by_id	1 entities	$$14.6 \mathrm{ms} \pm 85.8 \mathrm{μs}\left({\color{gray}0.037 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10 entities	$$14.9 \mathrm{ms} \pm 85.2 \mathrm{μs}\left({\color{gray}1.42 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	100 entities	$$14.9 \mathrm{ms} \pm 83.5 \mathrm{μs}\left({\color{gray}1.22 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	1000 entities	$$15.5 \mathrm{ms} \pm 90.2 \mathrm{μs}\left({\color{gray}-1.157 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10000 entities	$$23.5 \mathrm{ms} \pm 191 \mathrm{μs}\left({\color{gray}2.84 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity

Function	Value	Mean	Flame graphs
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/block/v/1`	$$30.6 \mathrm{ms} \pm 292 \mathrm{μs}\left({\color{gray}-0.493 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/book/v/1`	$$30.0 \mathrm{ms} \pm 290 \mathrm{μs}\left({\color{gray}-0.805 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/building/v/1`	$$28.9 \mathrm{ms} \pm 275 \mathrm{μs}\left({\color{lightgreen}-5.257 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/organization/v/1`	$$30.6 \mathrm{ms} \pm 302 \mathrm{μs}\left({\color{gray}1.10 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/page/v/2`	$$30.1 \mathrm{ms} \pm 295 \mathrm{μs}\left({\color{gray}-1.723 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/person/v/1`	$$30.5 \mathrm{ms} \pm 338 \mathrm{μs}\left({\color{gray}-1.212 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/playlist/v/1`	$$30.2 \mathrm{ms} \pm 273 \mathrm{μs}\left({\color{lightgreen}-6.050 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/song/v/1`	$$29.8 \mathrm{ms} \pm 340 \mathrm{μs}\left({\color{lightgreen}-5.227 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/uk-address/v/1`	$$29.7 \mathrm{ms} \pm 283 \mathrm{μs}\left({\color{gray}-1.949 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity_type

Function	Value	Mean	Flame graphs
get_entity_type_by_id	Account ID: `bf5a9ef5-dc3b-43cf-a291-6210c0321eba`	$$8.11 \mathrm{ms} \pm 40.8 \mathrm{μs}\left({\color{gray}-0.873 \mathrm{\%}}\right) $$	Flame Graph

representative_read_multiple_entities

Function	Value	Mean	Flame graphs
entity_by_property	traversal_paths=0	0	$$47.6 \mathrm{ms} \pm 181 \mathrm{μs}\left({\color{gray}0.629 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$95.2 \mathrm{ms} \pm 388 \mathrm{μs}\left({\color{gray}-0.168 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$53.6 \mathrm{ms} \pm 291 \mathrm{μs}\left({\color{gray}-0.685 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$61.4 \mathrm{ms} \pm 299 \mathrm{μs}\left({\color{gray}0.578 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$69.9 \mathrm{ms} \pm 393 \mathrm{μs}\left({\color{gray}-0.331 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$75.8 \mathrm{ms} \pm 366 \mathrm{μs}\left({\color{gray}-0.937 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=0	0	$$51.1 \mathrm{ms} \pm 303 \mathrm{μs}\left({\color{gray}1.34 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$78.8 \mathrm{ms} \pm 381 \mathrm{μs}\left({\color{gray}1.30 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$58.5 \mathrm{ms} \pm 392 \mathrm{μs}\left({\color{gray}1.39 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$67.7 \mathrm{ms} \pm 558 \mathrm{μs}\left({\color{gray}4.46 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$67.9 \mathrm{ms} \pm 281 \mathrm{μs}\left({\color{gray}-1.418 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$67.8 \mathrm{ms} \pm 270 \mathrm{μs}\left({\color{gray}-0.901 \mathrm{\%}}\right) $$

scenarios

Function	Value	Mean	Flame graphs
full_test	query-limited	$$145 \mathrm{ms} \pm 391 \mathrm{μs}\left({\color{red}5.32 \mathrm{\%}}\right) $$	Flame Graph
full_test	query-unlimited	$$143 \mathrm{ms} \pm 531 \mathrm{μs}\left({\color{gray}3.62 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-limited	$$41.2 \mathrm{ms} \pm 231 \mathrm{μs}\left({\color{lightgreen}-60.192 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-unlimited	$$591 \mathrm{ms} \pm 1.04 \mathrm{ms}\left({\color{gray}-0.325 \mathrm{\%}}\right) $$	Flame Graph

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BE-269: HashQL: Move PreInline out into Canonicalization pass #8239

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feat: tests

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feat: ensure that is_leaf propagates

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feat: create new inlineable cfg benchmark

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indietyp deleted the bm/be-271-hashql-implement-postinline-pass branch January 19, 2026 11:01

BE-271: HashQL: Implement PostInline pass #8240

BE-271: HashQL: Implement PostInline pass #8240

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indietyp commented Jan 3, 2026

🌟 What is the purpose of this PR?

🔍 What does this change?

Pre-Merge Checklist 🚀

🚢 Has this modified a publishable library?

📜 Does this require a change to the docs?

🕸️ Does this require a change to the Turbo Graph?

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PR Summary

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CodSpeed Performance Report

Merging this PR will degrade performance by 23.94%

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graphite-app bot commented Jan 19, 2026

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Unbounded array access in FindApplyCall may panic

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github-actions bot commented Jan 19, 2026

Benchmark results

@rust/hash-graph-benches – Integrations

policy_resolution_large

policy_resolution_medium

policy_resolution_none

policy_resolution_small

read_scaling_complete

read_scaling_linkless

representative_read_entity

representative_read_entity_type

representative_read_multiple_entities

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3 participants

BE-271: HashQL: Implement `PostInline` pass #8240

BE-271: HashQL: Implement `PostInline` pass #8240

cursor bot commented Jan 3, 2026 •

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indietyp commented Jan 3, 2026 •

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codspeed-hq bot commented Jan 3, 2026 •

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Unbounded array access in `FindApplyCall` may panic