BE-261: HashQL: Implement Administrative Reduction pass for MIR #8227

indietyp · 2025-12-29T21:42:20Z

🌟 What is the purpose of this PR?

Add an Administrative Reduction pass to the MIR optimizer that eliminates unnecessary function call overhead by inlining trivial thunks and forwarding closures.

🔍 What does this change?

Implements a new AdministrativeReduction global transform pass that identifies and inlines:
- Trivial thunks: Single-block functions with only trivial statements that immediately return a value
- Forwarding closures: Functions that perform trivial setup then delegate to another function
Adds a call-graph based traversal that processes functions in post-order (callees before callers)
Introduces DepthFirstForestPostOrder algorithm for complete graph traversal
Adds support for tracking closure values and resolving indirect function calls
Includes comprehensive test suite with various inlining scenarios

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

🛡 What tests cover this?

Unit tests for classification of reducible functions
Integration tests for various inlining scenarios (thunks, closures, indirect calls)
Snapshot tests to verify correct transformation behavior
Test cases for edge cases like self-recursion

cursor · 2025-12-29T21:42:27Z

PR Summary

Implements a global MIR optimization to eliminate administrative call overhead and adds supporting infrastructure.

New pass: AdministrativeReduction (pass/transform/administrative_reduction/*) implementing GlobalTransformPass, inlining trivial thunks and forwarding closures using call-graph postorder; adds compiletest suite and comprehensive UI/snapshot tests
Call graph: Extends pass/analysis/callgraph to implement DirectedGraph/Successors/Traverse, add CallKindFilter, and provide analyze(_in) helpers
Graph traversal: Adds DepthFirstForestPostOrder iterator and Traverse::depth_first_forest_post_order; fixes size_hint in postorder DFS
MIR utilities: ArgVec for Apply.arguments, builder helpers (closure, call), Place::SYNTHETIC, FieldIndex::{FN_PTR, ENV}
Pass framework: New GlobalTransformPass trait; Changed gains bit-or semantics
Allocator/heap: Add try_allocate_slice_uninit and infallible allocate_slice_uninit to bump allocators; forwarders in Heap/Scratch
ID/collections: IdSlice::{as_raw,as_raw_mut}, IdVec::extend_from_slice, Extend impl, FromIteratorIn
Bitset: DenseBitSet::first_unset with tests
Compiletest: New suite mir/pass/transform/administrative-reduction wired into registry and outputs (SVG optional)

^{Written by Cursor Bugbot for commit c680dde. This will update automatically on new commits. Configure here.}

indietyp · 2025-12-29T21:42:52Z

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codecov · 2025-12-29T21:47:15Z

Codecov Report

❌ Patch coverage is 88.23529% with 138 lines in your changes missing coverage. Please review.
✅ Project coverage is 60.36%. Comparing base (75ba45f) to head (c680dde).

Files with missing lines	Patch %	Lines
...ocal/hashql/mir/src/pass/analysis/callgraph/mod.rs	52.38%	27 Missing and 3 partials ⚠️
libs/@local/hashql/core/src/heap/bump.rs	0.00%	19 Missing ⚠️
libs/@local/hashql/core/src/heap/allocator.rs	0.00%	18 Missing ⚠️
...pass/transform/administrative_reduction/visitor.rs	89.80%	7 Missing and 9 partials ⚠️
libs/@local/hashql/core/src/id/vec.rs	0.00%	14 Missing ⚠️
...ass/transform/administrative_reduction/disjoint.rs	68.75%	8 Missing and 2 partials ⚠️
...rc/pass/transform/administrative_reduction/kind.rs	85.45%	4 Missing and 4 partials ⚠️
libs/@local/hashql/core/src/heap/mod.rs	0.00%	6 Missing ⚠️
libs/@local/hashql/core/src/heap/scratch.rs	0.00%	6 Missing ⚠️
...src/pass/transform/administrative_reduction/mod.rs	95.00%	6 Missing ⚠️
... and 2 more

Additional details and impacted files

@@                               Coverage Diff                                @@
##           bm/be-262-hashql-use-heap-to-store-interners    #8227      +/-   ##
================================================================================
+ Coverage                                         60.04%   60.36%   +0.32%     
================================================================================
  Files                                              1054     1060       +6     
  Lines                                            106576   107742    +1166     
  Branches                                           4434     4478      +44     
================================================================================
+ Hits                                              63989    65039    +1050     
- Misses                                            41865    41963      +98     
- Partials                                            722      740      +18

Flag	Coverage Δ
apps.hash-ai-worker-ts	`1.40% <ø> (ø)`
apps.hash-api	`0.00% <ø> (ø)`
local.hash-graph-sdk	`10.88% <ø> (ø)`
local.hash-isomorphic-utils	`0.00% <ø> (ø)`
rust.hash-graph-api	`2.89% <ø> (ø)`
rust.hashql-ast	`87.25% <ø> (ø)`
rust.hashql-compiletest	`46.65% <ø> (ø)`
rust.hashql-core	`81.75% <60.36%> (-0.18%)`	⬇️
rust.hashql-eval	`68.54% <ø> (ø)`
rust.hashql-hir	`89.10% <ø> (ø)`
rust.hashql-mir	`89.12% <92.76%> (+0.61%)`	⬆️
rust.hashql-syntax-jexpr	`94.05% <ø> (ø)`

Flags with carried forward coverage won't be shown. Click here to find out more.

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augmentcode · 2025-12-29T21:47:28Z

🤖 Augment PR Summary

Summary: This PR introduces an Administrative Reduction optimization pass for HashQL MIR to remove avoidable call/closure indirection by inlining trivial wrappers.

Changes:

Adds a new global MIR transform pass (AdministrativeReduction) that classifies bodies as TrivialThunk or ForwardingClosure and inlines them at call sites.
Builds and traverses a call graph in callee-before-caller order using a new full-graph DFS postorder iterator (DepthFirstForestPostOrder).
Extends call graph infrastructure to implement graph traversal traits and support filtered edge collection.
Adds MIR utilities to support the pass: closure/arg helpers, synthetic places, field indices, and local-offsetting support during inlining.
Enhances core utilities (bitsets, id slices/vectors, bump allocation) needed by the new pass.
Adds compiletest + MIR UI/snapshot tests covering thunks, forwarding closures, indirect calls, and recursion edges.

Technical Notes: The pass runs as a whole-program transform over DefIdSlice<Body>, processes functions postorder, and performs local fixpoint iteration within blocks to fully reduce newly spliced statements.

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codspeed-hq · 2025-12-29T21:49:12Z

CodSpeed Performance Report

Merging #8227 will not alter performance

_{Comparing bm/be-261-hashql-mir-administrative-reduction-pass (c680dde) with bm/be-262-hashql-use-heap-to-store-interners (75ba45f)}

Summary

✅ 29 untouched

libs/@local/hashql/core/src/heap/allocator.rs

github-actions · 2025-12-30T16:55:43Z

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	$$24.5 \mathrm{ms} \pm 141 \mathrm{μs}\left({\color{lightgreen}-36.460 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.68 \mathrm{ms} \pm 13.5 \mathrm{μs}\left({\color{gray}-2.030 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1001	$$11.6 \mathrm{ms} \pm 74.1 \mathrm{μs}\left({\color{gray}-3.254 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 3314	$$38.2 \mathrm{ms} \pm 256 \mathrm{μs}\left({\color{gray}-0.314 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$11.9 \mathrm{ms} \pm 61.0 \mathrm{μs}\left({\color{gray}-3.006 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 1526	$$20.7 \mathrm{ms} \pm 112 \mathrm{μs}\left({\color{gray}-1.433 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 2078	$$25.8 \mathrm{ms} \pm 131 \mathrm{μs}\left({\color{lightgreen}-39.531 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.00 \mathrm{ms} \pm 14.0 \mathrm{μs}\left({\color{lightgreen}-84.902 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 1033	$$10.8 \mathrm{ms} \pm 60.6 \mathrm{μs}\left({\color{lightgreen}-64.063 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_medium

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 102	$$2.99 \mathrm{ms} \pm 15.7 \mathrm{μs}\left({\color{gray}-1.506 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.34 \mathrm{ms} \pm 16.5 \mathrm{μs}\left({\color{gray}1.52 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 51	$$2.60 \mathrm{ms} \pm 12.0 \mathrm{μs}\left({\color{gray}-1.088 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 269	$$4.32 \mathrm{ms} \pm 18.6 \mathrm{μs}\left({\color{gray}1.85 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$2.82 \mathrm{ms} \pm 10.5 \mathrm{μs}\left({\color{gray}0.458 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 107	$$3.33 \mathrm{ms} \pm 17.9 \mathrm{μs}\left({\color{gray}1.45 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 133	$$3.60 \mathrm{ms} \pm 18.4 \mathrm{μs}\left({\color{gray}-0.235 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.70 \mathrm{ms} \pm 11.4 \mathrm{μs}\left({\color{gray}-0.664 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 63	$$3.18 \mathrm{ms} \pm 14.5 \mathrm{μs}\left({\color{gray}-0.912 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_none

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2	$$2.08 \mathrm{ms} \pm 11.3 \mathrm{μs}\left({\color{gray}3.96 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.04 \mathrm{ms} \pm 8.46 \mathrm{μs}\left({\color{gray}3.57 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1	$$2.12 \mathrm{ms} \pm 8.40 \mathrm{μs}\left({\color{gray}2.59 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 8	$$2.28 \mathrm{ms} \pm 10.5 \mathrm{μs}\left({\color{red}7.12 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.16 \mathrm{ms} \pm 8.79 \mathrm{μs}\left({\color{gray}3.51 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 3	$$2.37 \mathrm{ms} \pm 20.7 \mathrm{μs}\left({\color{red}7.63 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_small

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 52	$$2.31 \mathrm{ms} \pm 8.86 \mathrm{μs}\left({\color{gray}0.683 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.11 \mathrm{ms} \pm 6.81 \mathrm{μs}\left({\color{red}5.16 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 25	$$2.21 \mathrm{ms} \pm 8.07 \mathrm{μs}\left({\color{gray}4.16 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 94	$$2.61 \mathrm{ms} \pm 12.3 \mathrm{μs}\left({\color{gray}1.87 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$2.26 \mathrm{ms} \pm 9.23 \mathrm{μs}\left({\color{gray}3.71 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 26	$$2.44 \mathrm{ms} \pm 10.5 \mathrm{μs}\left({\color{gray}3.03 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 66	$$2.57 \mathrm{ms} \pm 10.5 \mathrm{μs}\left({\color{gray}4.23 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.24 \mathrm{ms} \pm 6.90 \mathrm{μs}\left({\color{gray}2.66 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 29	$$2.45 \mathrm{ms} \pm 14.4 \mathrm{μs}\left({\color{red}5.71 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_complete

Function	Value	Mean	Flame graphs
entity_by_id;one_depth	1 entities	$$34.0 \mathrm{ms} \pm 197 \mathrm{μs}\left({\color{gray}-1.523 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	10 entities	$$76.7 \mathrm{ms} \pm 705 \mathrm{μs}\left({\color{red}7.65 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	25 entities	$$41.1 \mathrm{ms} \pm 405 \mathrm{μs}\left({\color{red}11.0 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	5 entities	$$43.0 \mathrm{ms} \pm 324 \mathrm{μs}\left({\color{gray}2.37 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	50 entities	$$50.3 \mathrm{ms} \pm 456 \mathrm{μs}\left({\color{gray}3.14 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	1 entities	$$35.6 \mathrm{ms} \pm 117 \mathrm{μs}\left({\color{gray}-1.023 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	10 entities	$$429 \mathrm{ms} \pm 970 \mathrm{μs}\left({\color{gray}3.81 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	25 entities	$$85.1 \mathrm{ms} \pm 416 \mathrm{μs}\left({\color{gray}-2.288 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	5 entities	$$86.9 \mathrm{ms} \pm 508 \mathrm{μs}\left({\color{gray}-0.425 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	50 entities	$$253 \mathrm{ms} \pm 646 \mathrm{μs}\left({\color{gray}-0.869 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	1 entities	$$12.9 \mathrm{ms} \pm 63.4 \mathrm{μs}\left({\color{gray}-0.077 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	10 entities	$$13.2 \mathrm{ms} \pm 91.6 \mathrm{μs}\left({\color{gray}0.684 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	25 entities	$$14.1 \mathrm{ms} \pm 102 \mathrm{μs}\left({\color{gray}1.61 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	5 entities	$$13.3 \mathrm{ms} \pm 138 \mathrm{μs}\left({\color{gray}4.36 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	50 entities	$$15.7 \mathrm{ms} \pm 93.3 \mathrm{μs}\left({\color{lightgreen}-6.200 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_linkless

Function	Value	Mean	Flame graphs
entity_by_id	1 entities	$$13.0 \mathrm{ms} \pm 119 \mathrm{μs}\left({\color{gray}-3.005 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10 entities	$$13.0 \mathrm{ms} \pm 78.6 \mathrm{μs}\left({\color{lightgreen}-5.517 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	100 entities	$$13.0 \mathrm{ms} \pm 103 \mathrm{μs}\left({\color{gray}-4.716 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	1000 entities	$$14.1 \mathrm{ms} \pm 98.0 \mathrm{μs}\left({\color{lightgreen}-8.867 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10000 entities	$$20.6 \mathrm{ms} \pm 171 \mathrm{μs}\left({\color{lightgreen}-5.004 \mathrm{\%}}\right) $$	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`	$$28.8 \mathrm{ms} \pm 311 \mathrm{μs}\left({\color{gray}3.62 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/book/v/1`	$$28.4 \mathrm{ms} \pm 234 \mathrm{μs}\left({\color{gray}2.32 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/building/v/1`	$$27.8 \mathrm{ms} \pm 251 \mathrm{μs}\left({\color{gray}-3.946 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/organization/v/1`	$$27.1 \mathrm{ms} \pm 277 \mathrm{μs}\left({\color{gray}-4.474 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/page/v/2`	$$26.8 \mathrm{ms} \pm 306 \mathrm{μs}\left({\color{lightgreen}-6.887 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/person/v/1`	$$27.2 \mathrm{ms} \pm 266 \mathrm{μs}\left({\color{gray}-3.842 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/playlist/v/1`	$$28.3 \mathrm{ms} \pm 294 \mathrm{μs}\left({\color{gray}-0.842 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/song/v/1`	$$27.4 \mathrm{ms} \pm 328 \mathrm{μs}\left({\color{lightgreen}-5.202 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/uk-address/v/1`	$$28.2 \mathrm{ms} \pm 303 \mathrm{μs}\left({\color{gray}-1.098 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity_type

Function	Value	Mean	Flame graphs
get_entity_type_by_id	Account ID: `bf5a9ef5-dc3b-43cf-a291-6210c0321eba`	$$6.80 \mathrm{ms} \pm 61.4 \mathrm{μs}\left({\color{gray}-1.772 \mathrm{\%}}\right) $$	Flame Graph

representative_read_multiple_entities

Function	Value	Mean	Flame graphs
entity_by_property	traversal_paths=0	0	$$43.3 \mathrm{ms} \pm 261 \mathrm{μs}\left({\color{gray}-1.802 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$85.0 \mathrm{ms} \pm 376 \mathrm{μs}\left({\color{gray}-0.356 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$49.7 \mathrm{ms} \pm 244 \mathrm{μs}\left({\color{gray}-0.040 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$57.7 \mathrm{ms} \pm 486 \mathrm{μs}\left({\color{gray}1.56 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$63.9 \mathrm{ms} \pm 333 \mathrm{μs}\left({\color{gray}-1.479 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$68.3 \mathrm{ms} \pm 364 \mathrm{μs}\left({\color{gray}-2.342 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=0	0	$$47.5 \mathrm{ms} \pm 254 \mathrm{μs}\left({\color{gray}-0.615 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$70.0 \mathrm{ms} \pm 337 \mathrm{μs}\left({\color{gray}-3.431 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$53.5 \mathrm{ms} \pm 394 \mathrm{μs}\left({\color{gray}-0.940 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$58.1 \mathrm{ms} \pm 325 \mathrm{μs}\left({\color{lightgreen}-5.125 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$60.6 \mathrm{ms} \pm 319 \mathrm{μs}\left({\color{gray}-2.972 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$61.9 \mathrm{ms} \pm 350 \mathrm{μs}\left({\color{gray}0.398 \mathrm{\%}}\right) $$

scenarios

Function	Value	Mean	Flame graphs
full_test	query-limited	$$130 \mathrm{ms} \pm 460 \mathrm{μs}\left({\color{gray}4.95 \mathrm{\%}}\right) $$	Flame Graph
full_test	query-unlimited	$$126 \mathrm{ms} \pm 432 \mathrm{μs}\left({\color{gray}3.89 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-limited	$$38.7 \mathrm{ms} \pm 143 \mathrm{μs}\left({\color{red}9.82 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-unlimited	$$544 \mathrm{ms} \pm 1.06 \mathrm{ms}\left({\color{red}6.78 \mathrm{\%}}\right) $$	Flame Graph

indietyp added 18 commits December 28, 2025 16:13

feat: add first administrative reduction infra

b5b6d87

feat: add visitor for everything

71164b6

feat: first version

cc8382b

feat: forest post order

0c90a56

feat: allow for maybe unit

8e96c43

chore: docs

62faab3

chore: docs

34e60df

feat: borrow issue

5aeabf7

feat: split ar into multiple files

a7ed7cb

chore: cleanup

8bf9ad0

feat: reorganization

bfe58c8

fix: maximal

e4ceae4

chore: docs

0881b9e

chore: checkpoint

0db3490

chore: checkpoint

6f50192

chore: checkpoint

24b4bab

feat: tests

19f7ee7

feat: test sync

d7edaa4

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indietyp mentioned this pull request Dec 29, 2025

BE-260: HashQL: Switch to bump allocator with scoping support #8218

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indietyp mentioned this pull request Dec 29, 2025

BE-262: HashQL: Do not intern empty slices #8223

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graphite-app bot requested review from a team December 30, 2025 15:22

fix: add const assertion

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vercel bot temporarily deployed to Preview – petrinaut December 30, 2025 15:46 Inactive

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This was referenced Jan 8, 2026

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

BE-261: HashQL: Implement Administrative Reduction pass for MIR #8227

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BE-261: HashQL: Implement Administrative Reduction pass for MIR #8227

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indietyp commented Dec 29, 2025

🌟 What is the purpose of this PR?

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🚢 Has this modified a publishable library?

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Merging #8227 will not alter performance

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github-actions bot commented Dec 30, 2025

Benchmark results

@rust/hash-graph-benches – Integrations

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