BE-428: HashQL: Simplify traversal tracking to path recording by indietyp · Pull Request #8498 · hashintel/hash

indietyp · 2026-03-02T20:59:32Z

🌟 What is the purpose of this PR?

Replaces the traversal extraction transform pass (which rewrote MIR bodies by splicing load statements and creating new locals) with a pure analysis pass that records which entity paths each block accesses as bitsets. Introduces per-block path transfer cost charging so the placement solver can account for data locality when assigning blocks to backends.

🔍 What does this change?

Traversal analysis (traversal/analysis/): walks the body, resolves vertex projections via EntityPath::resolve(), records accessed paths as TraversalPathBitSet. No MIR mutation.
Per-block cost analysis (cost/analysis.rs): new BasicBlockCostAnalysis combines per-statement costs with a path transfer premium. For each block, charges a transfer premium on targets that are not the natural origin for the accessed paths (e.g., Interpreter pays a premium for Vectors because Embedding is the origin). Premiums are charged once per block, not per statement.
BasicBlockCostVec: the placement solver now operates on precomputed per-block costs. PlacementSolverContext takes blocks: &BasicBlockCostVec instead of separate statement costs and assignment arrays.
BlockPartitionedVec<T>: generic offset-table-backed per-block storage, extracted from StatementCostVec internals.
TraversalLattice: lattice structure carrying VertexType for dataflow analysis over path bitsets.
EntityPath::origin(), EntityPath::estimate_size(): per-path origin backend and transfer size estimation.
TransferCostConfig: variable-size parameters (properties, embeddings, provenance sizes, per-target cost multiplier).
FiniteBitSet::negate() in hashql-core.
Removed pass/transform/traversal_extraction/ (the MIR transform and its tests).

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

⚠️ Known issues

Path transfer premiums use pure over-approximation: every non-origin backend pays the full premium with no discounting for co-location. This can over-penalize non-origin assignment when multiple blocks access the same path on the same backend (cross-block dedup is not implemented). This is intentional: separable costs are a prerequisite for the PBQP solver planned in BE-436.

The ideal cost model is non-separable: the first block on a backend pays the full data fetch cost, subsequent co-labeled blocks pay zero ("shared-fetch" / pay-once-per-group). This maps to submodular clique costs in the optimization literature. Two frameworks formalize this:

Jegelka et al. 2014 (arXiv:1402.0240, Cooperative Graph Cuts): the "label cost" special case is mathematically identical to shared-fetch. Algorithms include polymatroidal flow (exact for separable surrogates) and semigradient methods. However, all are binary-only (k=2 labels).
Kolmogorov et al. 2010 (arXiv:1006.1990, Sum of Submodular Functions): general framework via submodular flow with capacity scaling. Also binary-only.

The multi-label extension via alpha-expansion (Boykov, Veksler, Zabih 2001) reduces k-label to a sequence of binary cooperative cuts (each solvable exactly), but only guarantees a local minimum. Our TransMatrix is not metric, so no global approximation factor holds. For k=3 backends (growing to 6-7), the overhead of iterative cooperative cut calls on ~100-node graphs is not justified when PBQP R1/R2 reductions on the separable over-approximation give exact solutions in microseconds.

Pure over-approximation does not distort the relative ranking between non-origin backends (the premium cancels in pairwise comparisons) but inflates the absolute cost of non-origin assignment, biasing toward origin backends. Intra-block dedup (implemented here) is the first mitigation; cross-block dedup remains an open problem for k > 2 on cyclic graphs.
:

🛡 What tests cover this?

Unit tests for BasicBlockCostAnalysis covering: no vertex access, single origin/non-origin paths, multiple path accumulation, composite path expansion, restricted target domains, cross-block independence
Unit tests for TraversalAnalysisVisitor covering path resolution from MIR projections
Unit tests for EntityPathBitSet covering composite swallowing, lattice operations, normalization
Existing solver tests updated to use BasicBlockCostVec; new path_premiums_influence_placement and provenance_variants_produce_different_premiums integration tests
End-to-end mixed_postgres_embedding_interpreter test verifying three-backend splitting

❓ How to test this?

cargo nextest run --package hashql-mir
cargo test --package hashql-mir --doc

feat: checkpoint (II) feat: checkpoint (III) feat: snapshot vec feat: add dedicated filter feat: checkpoint feat: filter implementation feat: filter implementation (mostly) done chore: environment capture note chore: always postgres bigint feat: target clone feat: simplify lookup feat: move storage up feat: eval entity path chore: checkpoint chore: checkpoint chore: find entrypoint feat: eval context feat: eval cleanup chore: cleanup feat: track index feat: wire up filter feat: add error reporting chore: checkpoint

indietyp · 2026-03-02T20:59:51Z

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cursor · 2026-03-02T21:00:53Z

PR Summary

High Risk
High risk because it refactors the MIR execution planning pipeline (liveness/traversal tracking, cost aggregation, and CSP solver inputs), which can change backend placement decisions and performance characteristics. It also expands use of unstable const traits/functions in core ID/bitset primitives, which may impact compilation on supported toolchains.

Overview
Replaces traversal extraction (a MIR-rewriting transform) with a pure traversal analysis that records accessed vertex EntityPaths as TraversalPathBitSets, and threads this through TraversalLivenessAnalysis and IslandPlacement.

Updates execution planning to compute per-basic-block costs (BasicBlockCostAnalysis/BasicBlockCostVec) by combining per-statement costs with a path transfer premium on non-origin backends, and changes the placement solver/CSP to consume these block costs instead of raw statement sums.

Refactors cost storage via a generic BlockPartitionedVec, adjusts PostInline construction and benches/tests accordingly, and extends core utilities (new FiniteBitSet::negate, const-ified Id/bitset traits and range helpers, plus tuple lattice combinators and InformationRange::midpoint).

^{Written by Cursor Bugbot for commit e6439e0. This will update automatically on new commits. Configure here.}

codecov · 2026-03-02T21:03:26Z

Codecov Report

❌ Patch coverage is 95.70552% with 98 lines in your changes missing coverage. Please review.
✅ Project coverage is 62.76%. Comparing base (bb10349) to head (e6439e0).
⚠️ Report is 20 commits behind head on main.

Files with missing lines	Patch %	Lines
.../hashql/mir/src/pass/execution/traversal/entity.rs	79.51%	41 Missing and 1 partial ⚠️
...cal/hashql/mir/src/pass/execution/traversal/mod.rs	81.01%	15 Missing ⚠️
libs/@local/hashql/core/src/id/mod.rs	0.00%	9 Missing ⚠️
...ql/mir/src/pass/analysis/dataflow/lattice/impls.rs	90.10%	7 Missing and 2 partials ⚠️
...ibs/@local/hashql/mir/src/pass/execution/vertex.rs	68.18%	6 Missing and 1 partial ⚠️
...s/@local/hashql/mir/src/pass/execution/cost/mod.rs	75.00%	5 Missing ⚠️
libs/@local/hashql/core/src/id/bit_vec/mod.rs	85.00%	3 Missing ⚠️
libs/@local/hashql/mir/src/body/place.rs	0.00%	3 Missing ⚠️
libs/@local/hashql/mir/src/pass/execution/mod.rs	92.00%	1 Missing and 1 partial ⚠️
...hql/mir/src/pass/analysis/dataflow/liveness/mod.rs	96.96%	0 Missing and 1 partial ⚠️
... and 2 more

Additional details and impacted files

@@            Coverage Diff             @@
##             main    #8498      +/-   ##
==========================================
+ Coverage   62.65%   62.76%   +0.11%     
==========================================
  Files        1312     1316       +4     
  Lines      133689   134383     +694     
  Branches     5510     5499      -11     
==========================================
+ Hits        83757    84343     +586     
- Misses      49018    49129     +111     
+ Partials      914      911       -3

Flag	Coverage Δ
apps.hash-ai-worker-ts	`1.40% <ø> (ø)`
apps.hash-api	`0.00% <ø> (ø)`
local.hash-graph-sdk	`7.73% <ø> (ø)`
local.hash-isomorphic-utils	`0.00% <ø> (ø)`
rust.hash-graph-api	`2.64% <ø> (ø)`
rust.hashql-ast	`87.23% <ø> (ø)`
rust.hashql-compiletest	`29.69% <ø> (ø)`
rust.hashql-core	`82.35% <91.60%> (+0.06%)`	⬆️
rust.hashql-eval	`69.13% <ø> (ø)`
rust.hashql-hir	`89.06% <ø> (ø)`
rust.hashql-mir	`92.34% <95.97%> (-0.26%)`	⬇️
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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codspeed-hq · 2026-03-02T21:05:41Z

Merging this PR will not alter performance

✅ 80 untouched benchmarks

_{Comparing bm/be-428-hashql-simplify-traversal-tracking-to-path-recording (e6439e0) with bm/be-434-hashql-entity-path-resolution-and-storage-mapping (3d9cc9d)¹}

No successful run was found on bm/be-434-hashql-entity-path-resolution-and-storage-mapping (885a006) during the generation of this report, so f28cd5a was used instead as the comparison base. There might be some changes unrelated to this pull request in this report. ↩

cursor

Cursor Bugbot has reviewed your changes and found 1 potential issue.

Autofix Details

Bugbot Autofix prepared a fix for the issue found in the latest run.

✅ Fixed: Stale comment and unnecessary reverse after removing traversal costs
- Removed the unnecessary reverse-order iteration and stale traversal-cost comment so target statement placement now reflects its order-independent behavior.

Or push these changes by commenting:

@cursor push 58ca5bc4a4

Preview (58ca5bc4a4)

diff --git a/libs/@local/hashql/mir/src/pass/execution/mod.rs b/libs/@local/hashql/mir/src/pass/execution/mod.rs
--- a/libs/@local/hashql/mir/src/pass/execution/mod.rs
+++ b/libs/@local/hashql/mir/src/pass/execution/mod.rs
@@ -72,10 +72,7 @@
 
         let mut statement_costs: TargetArray<_> = TargetArray::from_fn(|_| None);
 
-        let mut targets = TargetId::all();
-        targets.reverse(); // We reverse the order, so that earlier targets (aka the interpreter) can have access to traversal costs
-
-        for target in targets {
+        for target in TargetId::all() {
             let mut statement = TargetPlacementStatement::new_in(target, &self.scratch);
             let statement_cost =
                 statement.statement_placement_in(context, body, vertex, &self.scratch);

_{This Bugbot Autofix run was free. To enable autofix for future PRs, go to the Cursor dashboard.}

libs/@local/hashql/mir/src/pass/execution/mod.rs

github-actions · 2026-03-03T11:10:23Z

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	$$26.9 \mathrm{ms} \pm 140 \mathrm{μs}\left({\color{gray}-0.945 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.50 \mathrm{ms} \pm 17.3 \mathrm{μs}\left({\color{gray}0.049 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1001	$$12.3 \mathrm{ms} \pm 73.6 \mathrm{μs}\left({\color{gray}0.551 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 3314	$$43.3 \mathrm{ms} \pm 341 \mathrm{μs}\left({\color{gray}-0.084 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$14.7 \mathrm{ms} \pm 122 \mathrm{μs}\left({\color{gray}-0.422 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 1526	$$24.1 \mathrm{ms} \pm 158 \mathrm{μs}\left({\color{gray}1.05 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 2078	$$28.1 \mathrm{ms} \pm 162 \mathrm{μs}\left({\color{gray}-0.028 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.81 \mathrm{ms} \pm 17.1 \mathrm{μs}\left({\color{gray}0.470 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 1033	$$13.3 \mathrm{ms} \pm 99.7 \mathrm{μs}\left({\color{gray}0.067 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_medium

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 102	$$3.92 \mathrm{ms} \pm 20.1 \mathrm{μs}\left({\color{gray}-0.020 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.11 \mathrm{ms} \pm 16.1 \mathrm{μs}\left({\color{gray}-0.359 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 51	$$3.47 \mathrm{ms} \pm 16.9 \mathrm{μs}\left({\color{gray}0.510 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 269	$$5.39 \mathrm{ms} \pm 26.4 \mathrm{μs}\left({\color{gray}1.07 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$3.67 \mathrm{ms} \pm 17.3 \mathrm{μs}\left({\color{gray}-1.036 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 107	$$4.29 \mathrm{ms} \pm 30.7 \mathrm{μs}\left({\color{gray}0.330 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 133	$$4.60 \mathrm{ms} \pm 31.4 \mathrm{μs}\left({\color{gray}-0.069 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.54 \mathrm{ms} \pm 17.4 \mathrm{μs}\left({\color{gray}-0.694 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 63	$$4.24 \mathrm{ms} \pm 27.4 \mathrm{μs}\left({\color{gray}0.869 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_none

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2	$$2.83 \mathrm{ms} \pm 15.3 \mathrm{μs}\left({\color{gray}0.211 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.78 \mathrm{ms} \pm 16.6 \mathrm{μs}\left({\color{gray}0.368 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1	$$2.89 \mathrm{ms} \pm 14.9 \mathrm{μs}\left({\color{gray}-0.478 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 8	$$3.16 \mathrm{ms} \pm 14.7 \mathrm{μs}\left({\color{gray}-0.137 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.97 \mathrm{ms} \pm 14.6 \mathrm{μs}\left({\color{gray}-0.344 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 3	$$3.25 \mathrm{ms} \pm 17.0 \mathrm{μs}\left({\color{gray}-0.251 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_small

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 52	$$3.17 \mathrm{ms} \pm 14.3 \mathrm{μs}\left({\color{gray}0.083 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.90 \mathrm{ms} \pm 16.9 \mathrm{μs}\left({\color{gray}1.48 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 25	$$3.07 \mathrm{ms} \pm 17.4 \mathrm{μs}\left({\color{gray}0.321 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 94	$$3.60 \mathrm{ms} \pm 20.0 \mathrm{μs}\left({\color{gray}-0.048 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$3.20 \mathrm{ms} \pm 20.5 \mathrm{μs}\left({\color{gray}1.84 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 26	$$3.40 \mathrm{ms} \pm 19.7 \mathrm{μs}\left({\color{gray}0.396 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 66	$$3.55 \mathrm{ms} \pm 18.0 \mathrm{μs}\left({\color{gray}-0.345 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.17 \mathrm{ms} \pm 15.3 \mathrm{μs}\left({\color{gray}1.49 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 29	$$3.44 \mathrm{ms} \pm 21.0 \mathrm{μs}\left({\color{gray}-0.104 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_complete

Function	Value	Mean	Flame graphs
entity_by_id;one_depth	1 entities	$$41.0 \mathrm{ms} \pm 186 \mathrm{μs}\left({\color{gray}-1.006 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	10 entities	$$78.1 \mathrm{ms} \pm 345 \mathrm{μs}\left({\color{gray}-0.504 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	25 entities	$$45.3 \mathrm{ms} \pm 215 \mathrm{μs}\left({\color{gray}0.539 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	5 entities	$$48.0 \mathrm{ms} \pm 235 \mathrm{μs}\left({\color{gray}-0.026 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	50 entities	$$55.9 \mathrm{ms} \pm 311 \mathrm{μs}\left({\color{gray}-1.490 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	1 entities	$$43.2 \mathrm{ms} \pm 165 \mathrm{μs}\left({\color{gray}-0.840 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	10 entities	$$420 \mathrm{ms} \pm 897 \mathrm{μs}\left({\color{gray}0.147 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	25 entities	$$97.2 \mathrm{ms} \pm 608 \mathrm{μs}\left({\color{red}5.36 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	5 entities	$$87.3 \mathrm{ms} \pm 457 \mathrm{μs}\left({\color{gray}-0.372 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	50 entities	$$283 \mathrm{ms} \pm 1.09 \mathrm{ms}\left({\color{gray}-0.016 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	1 entities	$$15.8 \mathrm{ms} \pm 114 \mathrm{μs}\left({\color{gray}0.821 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	10 entities	$$15.9 \mathrm{ms} \pm 84.7 \mathrm{μs}\left({\color{gray}0.630 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	25 entities	$$16.0 \mathrm{ms} \pm 92.7 \mathrm{μs}\left({\color{gray}0.107 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	5 entities	$$15.8 \mathrm{ms} \pm 91.1 \mathrm{μs}\left({\color{gray}-1.118 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	50 entities	$$18.4 \mathrm{ms} \pm 163 \mathrm{μs}\left({\color{gray}-2.646 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_linkless

Function	Value	Mean	Flame graphs
entity_by_id	1 entities	$$15.7 \mathrm{ms} \pm 80.7 \mathrm{μs}\left({\color{gray}1.14 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10 entities	$$15.6 \mathrm{ms} \pm 75.8 \mathrm{μs}\left({\color{gray}-1.454 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	100 entities	$$15.7 \mathrm{ms} \pm 67.7 \mathrm{μs}\left({\color{gray}-0.040 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	1000 entities	$$16.2 \mathrm{ms} \pm 73.6 \mathrm{μs}\left({\color{gray}-0.394 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10000 entities	$$23.4 \mathrm{ms} \pm 154 \mathrm{μs}\left({\color{gray}-0.025 \mathrm{\%}}\right) $$	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`	$$29.9 \mathrm{ms} \pm 283 \mathrm{μs}\left({\color{gray}-0.347 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/book/v/1`	$$30.1 \mathrm{ms} \pm 326 \mathrm{μs}\left({\color{gray}-2.627 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/building/v/1`	$$31.7 \mathrm{ms} \pm 309 \mathrm{μs}\left({\color{red}7.14 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/organization/v/1`	$$31.1 \mathrm{ms} \pm 247 \mathrm{μs}\left({\color{gray}2.38 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/page/v/2`	$$31.3 \mathrm{ms} \pm 288 \mathrm{μs}\left({\color{gray}2.51 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/person/v/1`	$$30.3 \mathrm{ms} \pm 313 \mathrm{μs}\left({\color{gray}-0.936 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/playlist/v/1`	$$30.3 \mathrm{ms} \pm 266 \mathrm{μs}\left({\color{gray}-1.430 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/song/v/1`	$$30.9 \mathrm{ms} \pm 284 \mathrm{μs}\left({\color{gray}1.03 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/uk-address/v/1`	$$30.7 \mathrm{ms} \pm 380 \mathrm{μs}\left({\color{gray}1.60 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity_type

Function	Value	Mean	Flame graphs
get_entity_type_by_id	Account ID: `bf5a9ef5-dc3b-43cf-a291-6210c0321eba`	$$8.68 \mathrm{ms} \pm 44.2 \mathrm{μs}\left({\color{gray}0.823 \mathrm{\%}}\right) $$	Flame Graph

representative_read_multiple_entities

Function	Value	Mean	Flame graphs
entity_by_property	traversal_paths=0	0	$$88.5 \mathrm{ms} \pm 397 \mathrm{μs}\left({\color{gray}0.532 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$141 \mathrm{ms} \pm 542 \mathrm{μs}\left({\color{gray}0.345 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$95.5 \mathrm{ms} \pm 437 \mathrm{μs}\left({\color{gray}0.475 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$104 \mathrm{ms} \pm 467 \mathrm{μs}\left({\color{gray}0.077 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$113 \mathrm{ms} \pm 462 \mathrm{μs}\left({\color{gray}0.205 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$120 \mathrm{ms} \pm 496 \mathrm{μs}\left({\color{gray}0.239 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=0	0	$$86.2 \mathrm{ms} \pm 429 \mathrm{μs}\left({\color{gray}-0.170 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$114 \mathrm{ms} \pm 480 \mathrm{μs}\left({\color{gray}0.067 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$92.9 \mathrm{ms} \pm 392 \mathrm{μs}\left({\color{gray}-0.583 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$102 \mathrm{ms} \pm 385 \mathrm{μs}\left({\color{gray}-0.157 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$104 \mathrm{ms} \pm 457 \mathrm{μs}\left({\color{gray}0.514 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$104 \mathrm{ms} \pm 475 \mathrm{μs}\left({\color{gray}0.272 \mathrm{\%}}\right) $$

scenarios

Function	Value	Mean	Flame graphs
full_test	query-limited	$$132 \mathrm{ms} \pm 488 \mathrm{μs}\left({\color{gray}-4.210 \mathrm{\%}}\right) $$	Flame Graph
full_test	query-unlimited	$$134 \mathrm{ms} \pm 573 \mathrm{μs}\left({\color{gray}-0.659 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-limited	$$106 \mathrm{ms} \pm 793 \mathrm{μs}\left({\color{red}7.44 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-unlimited	$$611 \mathrm{ms} \pm 3.59 \mathrm{ms}\left({\color{red}5.71 \mathrm{\%}}\right) $$	Flame Graph

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libs/@local/hashql/mir/src/pass/execution/cost/mod.rs

indietyp added 30 commits February 28, 2026 17:15

feat: convert EntityPath to bitset

73482a0

fix: suggestions from code review

3362463

fix: suggestions from code review

885a006

feat: move to shared infrastructure

2400df5

feat: traversal path

8d76664

feat: move analysis traversal pass (WIP)

d9208e6

feat: checkpoint

87b184f

feat: checkpoint

4fdcf63

feat: checkpoint

efb0b85

feat: checkpoint

8e95f62

feat: rework tests

4b6a619

feat: define lattice over the entity

410f955

feat: checkpoint

960d725

feat: define lattice over the entity

7aa802a

feat: terminator placement

1642934

feat: terminator placement

1d5b5fd

chore: tests

575cf3b

feat: re-enable unused lints

21e1b5c

feat: checkpoint

b15d420

fix: remove the old traversals intermediary

dac3186

fix: remove the old traversals intermediary

d3fc684

chore: remove old version

eb83e70

feat: sketch out the cost vec

ef8e439

feat: checkpoitn

4789492

feat: checkpoint

bcd450e

feat: checkpoint

34e44d3

feat: checkpoint

cc32a50

fix: doc cleanup

de3ba9b

fix: suggestions from code review

f67d624

github-actions bot added area/libs Relates to first-party libraries/crates/packages (area) type/eng > backend Owned by the @backend team area/tests New or updated tests labels Mar 2, 2026

graphite-app bot assigned indietyp Mar 2, 2026

indietyp mentioned this pull request Mar 2, 2026

BE-427: Add hashql-macros crate with procedural macros for ID types and symbols #8495

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fix: ordering

e6439e0

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indietyp force-pushed the bm/be-434-hashql-entity-path-resolution-and-storage-mapping branch from 3d9cc9d to 885a006 Compare March 8, 2026 17:03

indietyp force-pushed the bm/be-428-hashql-simplify-traversal-tracking-to-path-recording branch from 7014842 to e6439e0 Compare March 8, 2026 17:03

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Base automatically changed from bm/be-434-hashql-entity-path-resolution-and-storage-mapping to main March 10, 2026 20:41

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Merging this PR will not alter performance

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github-actions bot commented Mar 3, 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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