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

vercel · 2026-03-02T20:59:37Z

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augmentcode · 2026-03-02T20:59:41Z

This pull request is too large for Augment to review. The PR exceeds the maximum size limit of 100000 tokens (approximately 400000 characters) for automated code review. Please consider breaking this PR into smaller, more focused changes.

indietyp · 2026-03-02T20:59:51Z

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cursor · 2026-03-02T21:00:53Z

PR Summary

Medium Risk
Refactors execution-planning analysis and solver inputs to incorporate new per-block path transfer premiums, which can change backend assignment decisions and requires correctness across multiple interacting passes. Core bitset/ID APIs also change signatures/constness, so downstream compilation breakage is possible if any call sites were missed.

Overview
Replaces traversal handling in MIR execution planning with a pure analysis that records which vertex/entity paths each basic block touches, and uses those paths to add per-block transfer premiums on non-origin backends.

The placement solver is rewired to operate on a new BasicBlockCostVec (block-level cost = statement sum + transfer load), with supporting infrastructure like BlockPartitionedVec for per-block storage and updated island/fusion/liveness analyses to track vertex-path bitsets. Separately, hashql-core moves Id/bitset traits toward const usage, adjusts FiniteBitSet::insert_range to take an explicit domain size, and adds FiniteBitSet::negate plus related test updates.

^{Written by Cursor Bugbot for commit 7014842. This will update automatically on new commits. Configure here.}

codecov · 2026-03-02T21:03:26Z

Codecov Report

❌ Patch coverage is 96.09756% with 88 lines in your changes missing coverage. Please review.
✅ Project coverage is 63.15%. Comparing base (3d9cc9d) to head (7014842).

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 ⚠️
...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/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 ⚠️
...cal/hashql/mir/src/pass/execution/cost/analysis.rs	99.62%	1 Missing ⚠️
.../mir/src/pass/execution/placement/solve/csp/mod.rs	83.33%	0 Missing and 1 partial ⚠️

Additional details and impacted files

@@                                       Coverage Diff                                       @@
##           bm/be-434-hashql-entity-path-resolution-and-storage-mapping    #8498      +/-   ##
===============================================================================================
+ Coverage                                                        63.03%   63.15%   +0.11%     
===============================================================================================
  Files                                                             1303     1313      +10     
  Lines                                                           132791   133550     +759     
  Branches                                                          5507     5498       -9     
===============================================================================================
+ Hits                                                             83709    84342     +633     
- Misses                                                           48170    48297     +127     
+ Partials                                                           912      911       -1

Flag	Coverage Δ
rust.hashql-compiletest	`29.69% <ø> (ø)`
rust.hashql-mir	`92.34% <96.40%> (-0.30%)`	⬇️

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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 (7014842) with bm/be-434-hashql-entity-path-resolution-and-storage-mapping (3d9cc9d)}

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

cursor

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

^{Bugbot Autofix is OFF. To automatically fix reported issues with cloud agents, enable autofix in 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

indietyp added 26 commits February 28, 2026 18:48

feat: move to shared infrastructure

bf75feb

feat: traversal path

955105a

feat: move analysis traversal pass (WIP)

081026f

feat: checkpoint

4674f1a

feat: checkpoint

85e8720

feat: checkpoint

d56a68a

feat: checkpoint

0aa82fc

feat: rework tests

a833653

feat: define lattice over the entity

0928db7

feat: checkpoint

5e4c96b

feat: define lattice over the entity

7316bb2

feat: terminator placement

ef3fe64

feat: terminator placement

7e5b485

chore: tests

6911298

feat: re-enable unused lints

3959707

feat: checkpoint

2b99fb1

fix: remove the old traversals intermediary

4b4b7b6

fix: remove the old traversals intermediary

2f269da

chore: remove old version

57989b1

feat: sketch out the cost vec

62507d6

feat: checkpoitn

30b7204

feat: checkpoint

b289d79

feat: checkpoint

23b9a00

feat: checkpoint

353b214

fix: doc cleanup

d62764b

fix: suggestions from code review

d4a8fb3

indietyp mentioned this pull request Mar 2, 2026

BE-434: HashQL: Refactor entity storage path resolution with centralized schema mapping #8496

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github-actions bot added 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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vercel bot deployed to Preview – petrinaut March 2, 2026 21:02 View deployment

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fix: suggestions from code review

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vercel bot deployed to Preview – hash March 2, 2026 21:31 View deployment

graphite-app bot requested review from a team March 2, 2026 21:39

fix: ordering

7014842

vercel bot temporarily deployed to Preview – petrinaut March 3, 2026 09:55 Inactive

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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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