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

Implements a post-inlining optimization stage and refines related analysis for better inlining outcomes.

Adds transform::PostInline (wrapper over Canonicalization with higher iteration budget) and registers a new compiletest suite mir/pass/transform/post-inline
Integrates PostInline into the benchmark pipeline alongside PreInline and Inline; adds an inlining-focused benchmark and adapts helpers to operate on body arrays
Enhances inlining analysis: BodyProperties now carries source; introduces CostEstimationResidual with is_leaf recomputation; adds FindApplyCall visitor to detect non-intrinsic calls in bodies
Adjusts call graph queries to consider only Apply edges for leaf/single/unique caller checks
Tweaks inline heuristics defaults (always_inline=16.0, size_penalty_factor=0.9)
Extends IdVec with from_domain_derive(_in) for allocator-aware derivation from a domain
Adds comprehensive UI tests validating post-inline effects (constant propagation, dead code/branch elimination, closure env cleanup, cascading simplification) and updates related snapshots

^{Written by Cursor Bugbot for commit 459c71b. 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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augmentcode

Review completed. 2 suggestions posted.

Comment augment review to trigger a new review at any time.

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 23.97%

_{Comparing bm/be-271-hashql-implement-postinline-pass (459c71b) with bm/be-269-hashql-move-out-most-of-preinline-into-a-canonicalization (235c525)}

Summary

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

Performance Changes

	Benchmark	`BASE`	`HEAD`	Efficiency
🆕	`inline`	N/A	232.3 µs	N/A
👁	`complex`	100 µs	119.3 µs	-16.24%
👁	`linear`	38.2 µs	50.3 µs	-23.97%
👁	`diamond`	68.8 µs	84.5 µs	-18.57%

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. ↩

codecov · 2026-01-03T21:26:41Z

Codecov Report

❌ Patch coverage is 96.70330% with 3 lines in your changes missing coverage. Please review.
✅ Project coverage is 65.00%. Comparing base (235c525) to head (459c71b).

Files with missing lines	Patch %	Lines
...local/hashql/mir/src/pass/transform/inline/find.rs	84.21%	2 Missing and 1 partial ⚠️

Additional details and impacted files

@@                                           Coverage Diff                                            @@
##           bm/be-269-hashql-move-out-most-of-preinline-into-a-canonicalization    #8240       +/-   ##
========================================================================================================
- Coverage                                                                89.68%   65.00%   -24.69%     
========================================================================================================
  Files                                                                       77      737      +660     
  Lines                                                                    10446    63084    +52638     
  Branches                                                                   290     3681     +3391     
========================================================================================================
+ Hits                                                                      9369    41006    +31637     
- Misses                                                                     973    21597    +20624     
- Partials                                                                   104      481      +377

Flag	Coverage Δ
apps.hash-ai-worker-ts	`1.40% <ø> (?)`
apps.hash-api	`0.00% <ø> (?)`
local.hash-graph-sdk	`10.88% <ø> (?)`
local.hash-isomorphic-utils	`0.00% <ø> (?)`
rust.hash-graph-api	`2.89% <ø> (?)`
rust.hashql-ast	`87.25% <ø> (?)`
rust.hashql-compiletest	`46.65% <ø> (?)`
rust.hashql-core	`81.76% <ø> (?)`
rust.hashql-eval	`68.54% <ø> (?)`
rust.hashql-hir	`89.10% <ø> (?)`
rust.hashql-mir	`89.77% <96.70%> (+0.08%)`	⬆️
rust.hashql-syntax-jexpr	`94.05% <ø> (?)`

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libs/@local/hashql/mir/src/pass/transform/post_inline.rs

github-actions · 2026-01-04T16:54:28Z

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	$$24.7 \mathrm{ms} \pm 136 \mathrm{μs}\left({\color{lightgreen}-25.882 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.20 \mathrm{ms} \pm 14.9 \mathrm{μs}\left({\color{gray}-2.534 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1001	$$11.8 \mathrm{ms} \pm 73.9 \mathrm{μs}\left({\color{gray}0.411 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 3314	$$41.3 \mathrm{ms} \pm 315 \mathrm{μs}\left({\color{gray}-0.217 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$13.8 \mathrm{ms} \pm 76.8 \mathrm{μs}\left({\color{gray}-4.784 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 1526	$$22.7 \mathrm{ms} \pm 144 \mathrm{μs}\left({\color{gray}-0.563 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 2078	$$25.8 \mathrm{ms} \pm 147 \mathrm{μs}\left({\color{lightgreen}-38.837 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.59 \mathrm{ms} \pm 15.0 \mathrm{μs}\left({\color{lightgreen}-81.725 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 1033	$$13.3 \mathrm{ms} \pm 63.3 \mathrm{μs}\left({\color{lightgreen}-51.233 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_medium

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 102	$$3.65 \mathrm{ms} \pm 18.3 \mathrm{μs}\left({\color{gray}0.095 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.86 \mathrm{ms} \pm 11.0 \mathrm{μs}\left({\color{gray}1.74 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 51	$$3.21 \mathrm{ms} \pm 15.9 \mathrm{μs}\left({\color{gray}1.38 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 269	$$4.97 \mathrm{ms} \pm 24.3 \mathrm{μs}\left({\color{gray}0.182 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$3.38 \mathrm{ms} \pm 15.9 \mathrm{μs}\left({\color{gray}0.380 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 107	$$3.91 \mathrm{ms} \pm 15.6 \mathrm{μs}\left({\color{gray}0.080 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 133	$$4.38 \mathrm{ms} \pm 26.9 \mathrm{μs}\left({\color{gray}2.49 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.28 \mathrm{ms} \pm 14.4 \mathrm{μs}\left({\color{gray}1.51 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 63	$$3.95 \mathrm{ms} \pm 20.0 \mathrm{μs}\left({\color{gray}2.24 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_none

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2	$$2.59 \mathrm{ms} \pm 11.4 \mathrm{μs}\left({\color{red}8.95 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.54 \mathrm{ms} \pm 14.7 \mathrm{μs}\left({\color{red}8.74 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1	$$2.68 \mathrm{ms} \pm 15.3 \mathrm{μs}\left({\color{red}11.3 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 8	$$2.76 \mathrm{ms} \pm 10.3 \mathrm{μs}\left({\color{red}5.72 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.67 \mathrm{ms} \pm 8.53 \mathrm{μs}\left({\color{red}6.66 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 3	$$2.90 \mathrm{ms} \pm 14.8 \mathrm{μs}\left({\color{red}8.03 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_small

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 52	$$2.94 \mathrm{ms} \pm 13.1 \mathrm{μs}\left({\color{red}6.67 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.61 \mathrm{ms} \pm 9.28 \mathrm{μs}\left({\color{red}7.65 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 25	$$2.79 \mathrm{ms} \pm 11.4 \mathrm{μs}\left({\color{red}7.75 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 94	$$3.26 \mathrm{ms} \pm 15.0 \mathrm{μs}\left({\color{red}5.87 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$2.91 \mathrm{ms} \pm 15.2 \mathrm{μs}\left({\color{red}8.34 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 26	$$3.07 \mathrm{ms} \pm 14.8 \mathrm{μs}\left({\color{red}6.69 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 66	$$3.22 \mathrm{ms} \pm 20.9 \mathrm{μs}\left({\color{red}6.65 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.80 \mathrm{ms} \pm 12.2 \mathrm{μs}\left({\color{red}6.34 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 29	$$3.11 \mathrm{ms} \pm 18.4 \mathrm{μs}\left({\color{red}8.54 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_complete

Function	Value	Mean	Flame graphs
entity_by_id;one_depth	1 entities	$$39.2 \mathrm{ms} \pm 155 \mathrm{μs}\left({\color{gray}1.16 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	10 entities	$$75.5 \mathrm{ms} \pm 382 \mathrm{μs}\left({\color{gray}0.634 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	25 entities	$$43.7 \mathrm{ms} \pm 183 \mathrm{μs}\left({\color{gray}4.36 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	5 entities	$$45.3 \mathrm{ms} \pm 182 \mathrm{μs}\left({\color{gray}1.04 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	50 entities	$$53.6 \mathrm{ms} \pm 230 \mathrm{μs}\left({\color{gray}1.23 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	1 entities	$$40.2 \mathrm{ms} \pm 159 \mathrm{μs}\left({\color{gray}-0.225 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	10 entities	$$412 \mathrm{ms} \pm 750 \mathrm{μs}\left({\color{gray}0.094 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	25 entities	$$94.4 \mathrm{ms} \pm 419 \mathrm{μs}\left({\color{gray}0.087 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	5 entities	$$84.7 \mathrm{ms} \pm 330 \mathrm{μs}\left({\color{gray}1.28 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	50 entities	$$283 \mathrm{ms} \pm 719 \mathrm{μs}\left({\color{gray}2.93 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	1 entities	$$14.5 \mathrm{ms} \pm 61.4 \mathrm{μs}\left({\color{gray}0.446 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	10 entities	$$14.9 \mathrm{ms} \pm 68.2 \mathrm{μs}\left({\color{gray}2.85 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	25 entities	$$15.1 \mathrm{ms} \pm 69.2 \mathrm{μs}\left({\color{gray}1.29 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	5 entities	$$14.7 \mathrm{ms} \pm 56.7 \mathrm{μs}\left({\color{gray}1.09 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	50 entities	$$17.9 \mathrm{ms} \pm 114 \mathrm{μs}\left({\color{gray}2.62 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_linkless

Function	Value	Mean	Flame graphs
entity_by_id	1 entities	$$14.7 \mathrm{ms} \pm 75.5 \mathrm{μs}\left({\color{gray}2.24 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10 entities	$$14.8 \mathrm{ms} \pm 78.2 \mathrm{μs}\left({\color{gray}3.03 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	100 entities	$$14.6 \mathrm{ms} \pm 57.1 \mathrm{μs}\left({\color{gray}2.12 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	1000 entities	$$15.3 \mathrm{ms} \pm 67.0 \mathrm{μs}\left({\color{gray}2.63 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10000 entities	$$22.1 \mathrm{ms} \pm 146 \mathrm{μs}\left({\color{gray}0.892 \mathrm{\%}}\right) $$	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`	$$31.1 \mathrm{ms} \pm 321 \mathrm{μs}\left({\color{red}6.35 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/book/v/1`	$$29.6 \mathrm{ms} \pm 253 \mathrm{μs}\left({\color{gray}-2.718 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/building/v/1`	$$30.8 \mathrm{ms} \pm 252 \mathrm{μs}\left({\color{red}7.47 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/organization/v/1`	$$28.8 \mathrm{ms} \pm 278 \mathrm{μs}\left({\color{gray}-3.766 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/page/v/2`	$$30.1 \mathrm{ms} \pm 316 \mathrm{μs}\left({\color{gray}4.39 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/person/v/1`	$$30.0 \mathrm{ms} \pm 260 \mathrm{μs}\left({\color{gray}-0.701 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/playlist/v/1`	$$29.9 \mathrm{ms} \pm 266 \mathrm{μs}\left({\color{gray}0.729 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/song/v/1`	$$30.5 \mathrm{ms} \pm 261 \mathrm{μs}\left({\color{gray}4.55 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/uk-address/v/1`	$$30.5 \mathrm{ms} \pm 238 \mathrm{μs}\left({\color{gray}2.95 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity_type

Function	Value	Mean	Flame graphs
get_entity_type_by_id	Account ID: `bf5a9ef5-dc3b-43cf-a291-6210c0321eba`	$$8.18 \mathrm{ms} \pm 34.0 \mathrm{μs}\left({\color{gray}1.99 \mathrm{\%}}\right) $$	Flame Graph

representative_read_multiple_entities

Function	Value	Mean	Flame graphs
entity_by_property	traversal_paths=0	0	$$47.9 \mathrm{ms} \pm 226 \mathrm{μs}\left({\color{red}5.72 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$95.6 \mathrm{ms} \pm 385 \mathrm{μs}\left({\color{gray}2.93 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$52.8 \mathrm{ms} \pm 299 \mathrm{μs}\left({\color{gray}3.94 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$60.3 \mathrm{ms} \pm 297 \mathrm{μs}\left({\color{gray}2.29 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$70.5 \mathrm{ms} \pm 509 \mathrm{μs}\left({\color{gray}4.40 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$75.7 \mathrm{ms} \pm 374 \mathrm{μs}\left({\color{gray}2.55 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=0	0	$$50.7 \mathrm{ms} \pm 278 \mathrm{μs}\left({\color{gray}4.58 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$76.8 \mathrm{ms} \pm 401 \mathrm{μs}\left({\color{gray}1.89 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$58.6 \mathrm{ms} \pm 345 \mathrm{μs}\left({\color{red}5.34 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$65.4 \mathrm{ms} \pm 374 \mathrm{μs}\left({\color{gray}3.77 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$67.1 \mathrm{ms} \pm 354 \mathrm{μs}\left({\color{gray}2.51 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$67.0 \mathrm{ms} \pm 306 \mathrm{μs}\left({\color{gray}2.85 \mathrm{\%}}\right) $$

scenarios

Function	Value	Mean	Flame graphs
full_test	query-limited	$$131 \mathrm{ms} \pm 493 \mathrm{μs}\left({\color{gray}2.69 \mathrm{\%}}\right) $$	Flame Graph
full_test	query-unlimited	$$129 \mathrm{ms} \pm 502 \mathrm{μs}\left({\color{gray}-0.697 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-limited	$$41.0 \mathrm{ms} \pm 204 \mathrm{μs}\left({\color{lightgreen}-60.579 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-unlimited	$$568 \mathrm{ms} \pm 1.20 \mathrm{ms}\left({\color{lightgreen}-6.786 \mathrm{\%}}\right) $$	Flame Graph

vercel · 2026-01-15T13:31:50Z

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indietyp added 3 commits January 3, 2026 21:11

feat: tests

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

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indietyp added 2 commits January 3, 2026 22:20

chore: remove tracing dep

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BE-257: HashQL: Implement MIR interpreter with runtime value representation #8245

Open

BE-272: HashQL: Add numeric addition and subtraction operators to MIR #8250

Open

BE-273: HashQL: Profile and benchmark MIR interpreter #8251

Open

indietyp force-pushed the bm/be-269-hashql-move-out-most-of-preinline-into-a-canonicalization branch from 0a15246 to d68987f Compare January 15, 2026 13:31

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BE-271: HashQL: Implement PostInline pass #8240

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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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CodSpeed Performance Report

Merging this PR will degrade performance by 23.97%

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github-actions bot commented Jan 4, 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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BE-271: HashQL: Implement `PostInline` pass #8240

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

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