BE-261: HashQL: Implement Administrative Reduction pass for MIR by indietyp · Pull Request #8227 · hashintel/hash

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

Introduces a global MIR optimization that eliminates unnecessary calls by inlining simple bodies.

New AdministrativeReduction GlobalTransformPass: identifies TrivialThunk and ForwardingClosure bodies and inlines them; handles indirect calls via closure/function-pointer tracking; processes bodies in call-graph postorder
Call graph reworked to implement core graph traits and expose successors; adds filtered analysis modes and display; integrates with traversal
Core graph: adds DepthFirstForestPostOrder iterator and exposes it via Traverse
Pass infra: adds GlobalTransformPass trait; improves Changed with bit-or ops; minor MIR APIs (Place::SYNTHETIC, FieldIndex constants, Apply::ArgVec, builder helpers closure/call)
Allocator/heap/id utilities: uninitialized slice allocation, DenseBitSet::first_unset, IdSlice::{as_raw,as_raw_mut}, IdVec extend/from-iter; accompanying tests
Compiletest suite and extensive MIR UI snapshots for AR; wires new suite into runner

^{Written by Cursor Bugbot for commit a322d20. 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

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 59.24%. Comparing base (ad273fc) to head (c4139ea).

Additional details and impacted files

@@                               Coverage Diff                                @@
##           bm/be-262-hashql-use-heap-to-store-interners    #8227      +/-   ##
================================================================================
+ Coverage                                         59.20%   59.24%   +0.03%     
================================================================================
  Files                                              1191     1191              
  Lines                                            113524   113321     -203     
  Branches                                           4986     4981       -5     
================================================================================
- Hits                                              67215    67134      -81     
+ Misses                                            45531    45411     -120     
+ Partials                                            778      776       -2

Flag	Coverage Δ
apps.hash-ai-worker-ts	`1.40% <ø> (ø)`
apps.hash-api	`0.00% <ø> (ø)`
rust.hash-graph-api	`2.89% <ø> (ø)`

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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

Merging this PR will not alter performance

✅ 14 untouched benchmarks
🗄️ 15 archived benchmarks run¹

_{Comparing bm/be-261-hashql-mir-administrative-reduction-pass (a322d20) with main (7c64ae2)}

15 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. ↩

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

graphite-app · 2026-01-16T14:43:15Z

Merge activity

Jan 16, 2:43 PM UTC: Graphite rebased this pull request, because this pull request is set to merge when ready.

github-actions · 2026-01-16T15:54:28Z

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	$$25.9 \mathrm{ms} \pm 271 \mathrm{μs}\left({\color{lightgreen}-8.536 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.23 \mathrm{ms} \pm 14.9 \mathrm{μs}\left({\color{gray}-0.436 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1001	$$11.7 \mathrm{ms} \pm 74.6 \mathrm{μs}\left({\color{gray}-1.604 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 3314	$$41.7 \mathrm{ms} \pm 282 \mathrm{μs}\left({\color{gray}-0.277 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$14.0 \mathrm{ms} \pm 82.9 \mathrm{μs}\left({\color{gray}-0.044 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 1526	$$23.2 \mathrm{ms} \pm 151 \mathrm{μs}\left({\color{gray}0.117 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 2078	$$25.9 \mathrm{ms} \pm 164 \mathrm{μs}\left({\color{lightgreen}-39.280 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.62 \mathrm{ms} \pm 17.2 \mathrm{μs}\left({\color{lightgreen}-81.774 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 1033	$$10.9 \mathrm{ms} \pm 70.1 \mathrm{μs}\left({\color{lightgreen}-63.960 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_medium

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 102	$$3.64 \mathrm{ms} \pm 17.5 \mathrm{μs}\left({\color{gray}0.355 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.87 \mathrm{ms} \pm 14.5 \mathrm{μs}\left({\color{gray}0.768 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 51	$$3.20 \mathrm{ms} \pm 14.0 \mathrm{μs}\left({\color{gray}0.282 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 269	$$4.95 \mathrm{ms} \pm 20.5 \mathrm{μs}\left({\color{gray}-0.273 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$3.42 \mathrm{ms} \pm 13.9 \mathrm{μs}\left({\color{gray}0.214 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 107	$$3.95 \mathrm{ms} \pm 16.9 \mathrm{μs}\left({\color{gray}0.511 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 133	$$4.22 \mathrm{ms} \pm 19.3 \mathrm{μs}\left({\color{gray}-0.731 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.30 \mathrm{ms} \pm 15.8 \mathrm{μs}\left({\color{gray}1.81 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 63	$$3.91 \mathrm{ms} \pm 27.3 \mathrm{μs}\left({\color{gray}1.28 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_none

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2	$$2.57 \mathrm{ms} \pm 9.66 \mathrm{μs}\left({\color{red}7.38 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.50 \mathrm{ms} \pm 8.96 \mathrm{μs}\left({\color{red}6.55 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1	$$2.60 \mathrm{ms} \pm 10.1 \mathrm{μs}\left({\color{red}7.78 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 8	$$2.79 \mathrm{ms} \pm 12.6 \mathrm{μs}\left({\color{red}6.27 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.69 \mathrm{ms} \pm 13.2 \mathrm{μs}\left({\color{red}6.82 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 3	$$2.89 \mathrm{ms} \pm 12.3 \mathrm{μs}\left({\color{red}7.50 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_small

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 52	$$2.93 \mathrm{ms} \pm 14.5 \mathrm{μs}\left({\color{red}7.03 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.61 \mathrm{ms} \pm 11.6 \mathrm{μs}\left({\color{red}8.05 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 25	$$2.78 \mathrm{ms} \pm 13.3 \mathrm{μs}\left({\color{red}7.43 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 94	$$3.26 \mathrm{ms} \pm 14.3 \mathrm{μs}\left({\color{red}5.96 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$2.84 \mathrm{ms} \pm 15.6 \mathrm{μs}\left({\color{red}6.42 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 26	$$3.04 \mathrm{ms} \pm 14.2 \mathrm{μs}\left({\color{red}5.82 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 66	$$3.15 \mathrm{ms} \pm 13.8 \mathrm{μs}\left({\color{red}5.16 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.80 \mathrm{ms} \pm 12.0 \mathrm{μs}\left({\color{red}6.55 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 29	$$3.04 \mathrm{ms} \pm 13.7 \mathrm{μs}\left({\color{red}6.52 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_complete

Function	Value	Mean	Flame graphs
entity_by_id;one_depth	1 entities	$$39.4 \mathrm{ms} \pm 166 \mathrm{μs}\left({\color{gray}1.30 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	10 entities	$$75.9 \mathrm{ms} \pm 325 \mathrm{μs}\left({\color{gray}0.711 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	25 entities	$$44.0 \mathrm{ms} \pm 156 \mathrm{μs}\left({\color{gray}1.40 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	5 entities	$$45.7 \mathrm{ms} \pm 164 \mathrm{μs}\left({\color{gray}0.572 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	50 entities	$$54.2 \mathrm{ms} \pm 251 \mathrm{μs}\left({\color{gray}1.68 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	1 entities	$$40.6 \mathrm{ms} \pm 150 \mathrm{μs}\left({\color{gray}-0.588 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	10 entities	$$413 \mathrm{ms} \pm 715 \mathrm{μs}\left({\color{gray}-0.889 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	25 entities	$$93.9 \mathrm{ms} \pm 428 \mathrm{μs}\left({\color{red}6.37 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	5 entities	$$84.4 \mathrm{ms} \pm 255 \mathrm{μs}\left({\color{gray}0.923 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	50 entities	$$306 \mathrm{ms} \pm 1.01 \mathrm{ms}\left({\color{red}9.46 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	1 entities	$$14.6 \mathrm{ms} \pm 66.1 \mathrm{μs}\left({\color{gray}0.029 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	10 entities	$$15.0 \mathrm{ms} \pm 63.9 \mathrm{μs}\left({\color{gray}1.58 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	25 entities	$$15.2 \mathrm{ms} \pm 66.2 \mathrm{μs}\left({\color{gray}-0.202 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	5 entities	$$15.0 \mathrm{ms} \pm 63.6 \mathrm{μs}\left({\color{gray}1.30 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	50 entities	$$17.8 \mathrm{ms} \pm 75.7 \mathrm{μs}\left({\color{gray}3.51 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_linkless

Function	Value	Mean	Flame graphs
entity_by_id	1 entities	$$14.7 \mathrm{ms} \pm 65.7 \mathrm{μs}\left({\color{gray}-1.715 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10 entities	$$14.7 \mathrm{ms} \pm 54.6 \mathrm{μs}\left({\color{gray}0.343 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	100 entities	$$14.7 \mathrm{ms} \pm 70.8 \mathrm{μs}\left({\color{gray}-0.876 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	1000 entities	$$15.5 \mathrm{ms} \pm 81.5 \mathrm{μs}\left({\color{gray}0.246 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10000 entities	$$22.4 \mathrm{ms} \pm 155 \mathrm{μs}\left({\color{gray}0.879 \mathrm{\%}}\right) $$	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`	$$30.1 \mathrm{ms} \pm 256 \mathrm{μs}\left({\color{gray}-0.054 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/book/v/1`	$$29.8 \mathrm{ms} \pm 317 \mathrm{μs}\left({\color{gray}2.85 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/building/v/1`	$$30.0 \mathrm{ms} \pm 308 \mathrm{μs}\left({\color{gray}3.00 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/organization/v/1`	$$29.6 \mathrm{ms} \pm 326 \mathrm{μs}\left({\color{gray}1.33 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/page/v/2`	$$29.5 \mathrm{ms} \pm 320 \mathrm{μs}\left({\color{gray}0.376 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/person/v/1`	$$30.6 \mathrm{ms} \pm 316 \mathrm{μs}\left({\color{gray}3.87 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/playlist/v/1`	$$30.0 \mathrm{ms} \pm 266 \mathrm{μs}\left({\color{gray}2.50 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/song/v/1`	$$29.2 \mathrm{ms} \pm 293 \mathrm{μs}\left({\color{gray}-0.786 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/uk-address/v/1`	$$29.1 \mathrm{ms} \pm 273 \mathrm{μs}\left({\color{gray}0.614 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity_type

Function	Value	Mean	Flame graphs
get_entity_type_by_id	Account ID: `bf5a9ef5-dc3b-43cf-a291-6210c0321eba`	$$8.19 \mathrm{ms} \pm 33.7 \mathrm{μs}\left({\color{gray}1.42 \mathrm{\%}}\right) $$	Flame Graph

representative_read_multiple_entities

Function	Value	Mean	Flame graphs
entity_by_property	traversal_paths=0	0	$$46.5 \mathrm{ms} \pm 240 \mathrm{μs}\left({\color{gray}0.882 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$94.6 \mathrm{ms} \pm 347 \mathrm{μs}\left({\color{gray}0.894 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$52.7 \mathrm{ms} \pm 279 \mathrm{μs}\left({\color{gray}1.50 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$60.4 \mathrm{ms} \pm 319 \mathrm{μs}\left({\color{gray}0.142 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$68.6 \mathrm{ms} \pm 283 \mathrm{μs}\left({\color{gray}0.919 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$75.1 \mathrm{ms} \pm 406 \mathrm{μs}\left({\color{gray}0.869 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=0	0	$$49.6 \mathrm{ms} \pm 195 \mathrm{μs}\left({\color{gray}0.447 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$76.9 \mathrm{ms} \pm 317 \mathrm{μs}\left({\color{gray}0.338 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$56.5 \mathrm{ms} \pm 250 \mathrm{μs}\left({\color{gray}0.383 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$64.0 \mathrm{ms} \pm 282 \mathrm{μs}\left({\color{gray}-0.174 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$66.5 \mathrm{ms} \pm 393 \mathrm{μs}\left({\color{gray}0.307 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$66.5 \mathrm{ms} \pm 316 \mathrm{μs}\left({\color{gray}-0.279 \mathrm{\%}}\right) $$

scenarios

Function	Value	Mean	Flame graphs
full_test	query-limited	$$134 \mathrm{ms} \pm 402 \mathrm{μs}\left({\color{gray}2.12 \mathrm{\%}}\right) $$	Flame Graph
full_test	query-unlimited	$$138 \mathrm{ms} \pm 500 \mathrm{μs}\left({\color{gray}4.07 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-limited	$$39.7 \mathrm{ms} \pm 145 \mathrm{μs}\left({\color{lightgreen}-61.938 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-unlimited	$$579 \mathrm{ms} \pm 829 \mathrm{μs}\left({\color{lightgreen}-7.403 \mathrm{\%}}\right) $$	Flame Graph

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

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indietyp added 14 commits January 16, 2026 14:42

chore: docs

fad2053

feat: borrow issue

156dc91

feat: split ar into multiple files

f3af02a

chore: cleanup

4dddbe1

feat: reorganization

c953d12

fix: maximal

cf4ec22

chore: docs

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chore: checkpoint

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chore: checkpoint

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chore: checkpoint

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feat: tests

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feat: test sync

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chore: CI

0d6e704

fix: add const assertion

a322d20

indietyp force-pushed the bm/be-261-hashql-mir-administrative-reduction-pass branch from 10798e5 to a322d20 Compare January 16, 2026 14:42

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Merged via the queue into main with commit fd458f2 Jan 16, 2026
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indietyp deleted the bm/be-261-hashql-mir-administrative-reduction-pass branch January 16, 2026 15:21

This was referenced Jan 20, 2026

BE-301: HashQL: Implement size estimation analysis for MIR #8278

Open

BE-315: HashQL: Add GraphReadFilter source variant to MIR #8323

Open

BE-317: HashQL: Implement TraversalExtraction pass #8331

Open

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indietyp commented Dec 29, 2025

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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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2 participants

cursor bot commented Dec 29, 2025 •

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