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

indietyp · 2026-01-20T20:44:52Z

🌟 What is the purpose of this PR?

This PR adds a size estimation analysis to the MIR framework that can statically determine the size of values flowing through a program. The analysis helps predict memory usage and performance characteristics of queries.

🔍 What does this change?

Implements a two-phase size estimation analysis:
- Static analysis: Estimates sizes purely from type information
- Dynamic analysis: Uses dataflow to track how sizes propagate through the program
Adds support for tracking parameter-dependent sizes using affine equations
Introduces range types for representing bounded and unbounded size estimates
Updates the MIR builder guide to document new type syntax for lists and unknown types

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?

Comprehensive unit tests for all components of the size estimation system
Integration tests for multi-function analysis and recursive functions
Snapshot tests to verify analysis results

cursor · 2026-01-20T20:44:59Z

PR Summary

Introduces a full MIR size estimation system and supporting infrastructure.

Add size_estimation analysis: static type-based sizing and dynamic forward dataflow; models sizes as Footprint { units, cardinality } with Estimate::{Constant, Affine} over parameter coefficients
New lattice/range/unit primitives: InformationUnit, Cardinal, InformationRange, Cardinality, AffineEquation, and BodyFootprint{Semilattice}; snapshot and unit tests
Extend dataflow framework: metadata hooks (initialize_metadata, should_process_block, should_propagate_between), adjusted trait bounds, and fixpoint driver changes
Generalize lattice traits (JoinSemiLattice<T, Rhs=T>, HasBottom, HasTop) and implement numeric semiring bottoms/tops
Add GlobalAnalysisPass and wire SizeEstimationAnalysis into analysis::mod
MIR builder/docs: support ? (unknown) and [List T] types; export ArgSlice
Core utilities: small_vec_from_elem, optimized Vec clone-into; IdVec iterator and cross-allocator Eq/Ord
Add tests and snapshots under tests/ui/pass/size-estimation/

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indietyp · 2026-01-20T20:45:47Z

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augmentcode · 2026-01-20T20:49:34Z

🤖 Augment PR Summary

Summary: Adds a MIR “size estimation” analysis to predict value footprints (information units + cardinality), including parameter-dependent sizing via affine equations.

Changes:

Introduces size_estimation analysis pass with two phases: static (type-based) sizing and dynamic (dataflow) propagation for intrinsically-sized/unknown types.
Adds core abstractions: InformationUnit/Range, Cardinal/Cardinality, Footprint, Estimate (constant vs affine), and AffineEquation.
Implements affine substitution across apply calls, and handles recursion via call-graph SCC processing + bounded fixpoint iteration.
Extends the dataflow framework with optional per-analysis metadata hooks to bound worklist propagation/iteration, and relaxes/adjusts Domain/Lattice allocator bounds accordingly.
Updates lattice traits to support RHS-typed joins and defaulted HasBottom/HasTop type params; adds bottom/top impls for numeric carriers.
Improves allocator-aware cloning for Vec and adds a smallvec helper for repeated elements.
Extends MIR builder type syntax to support unknown (?) and intrinsic list types ([List T]).
Adds extensive unit + snapshot tests validating analysis behavior (control-flow joins, projections, calls, and recursion).

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libs/@local/hashql/mir/src/pass/analysis/size_estimation/mod.rs

libs/@local/hashql/mir/src/pass/analysis/size_estimation/dynamic.rs

codecov · 2026-01-20T20:51:16Z

Codecov Report

❌ Patch coverage is 91.73935% with 161 lines in your changes missing coverage. Please review.
✅ Project coverage is 60.83%. Comparing base (d7b72f2) to head (9594e78).

Files with missing lines	Patch %	Lines
...mir/src/pass/analysis/size_estimation/footprint.rs	83.62%	26 Missing and 11 partials ⚠️
...shql/mir/src/pass/analysis/size_estimation/unit.rs	61.11%	28 Missing ⚠️
...hashql/mir/src/pass/analysis/dataflow/framework.rs	43.58%	20 Missing and 2 partials ⚠️
...l/mir/src/pass/analysis/size_estimation/dynamic.rs	91.32%	15 Missing and 6 partials ⚠️
.../mir/src/pass/analysis/size_estimation/estimate.rs	93.77%	6 Missing and 8 partials ⚠️
...hql/mir/src/pass/analysis/size_estimation/range.rs	95.59%	13 Missing ⚠️
...ql/mir/src/pass/analysis/dataflow/lattice/impls.rs	50.00%	6 Missing ⚠️
libs/@local/hashql/core/src/heap/clone.rs	0.00%	4 Missing ⚠️
...ql/mir/src/pass/analysis/size_estimation/affine.rs	96.99%	1 Missing and 3 partials ⚠️
...ql/mir/src/pass/analysis/size_estimation/static.rs	98.41%	4 Missing ⚠️
... and 4 more

Additional details and impacted files

@@                             Coverage Diff                             @@
##           bm/be-273-hashql-interpreter-benchmarks    #8278      +/-   ##
===========================================================================
+ Coverage                                    60.22%   60.83%   +0.60%     
===========================================================================
  Files                                         1231     1240       +9     
  Lines                                       117974   119913    +1939     
  Branches                                      5133     5181      +48     
===========================================================================
+ Hits                                         71050    72948    +1898     
- Misses                                       46097    46107      +10     
- Partials                                       827      858      +31

Flag	Coverage Δ
rust.hashql-ast	`87.25% <ø> (ø)`
rust.hashql-compiletest	`46.65% <ø> (ø)`
rust.hashql-core	`81.75% <0.00%> (-0.02%)`	⬇️
rust.hashql-hir	`89.10% <ø> (ø)`
rust.hashql-mir	`89.22% <92.16%> (+1.31%)`	⬆️

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codspeed-hq · 2026-01-20T20:52:19Z

CodSpeed Performance Report

Merging this PR will not alter performance

_{Comparing bm/be-301-hashql-size-estimation-for-local-variables-and-functions (9594e78) with bm/be-273-hashql-interpreter-benchmarks (d7b72f2)}

Summary

✅ 21 untouched benchmarks
🗄️ 12 archived benchmarks run¹

12 benchmarks were run, but are now archived. If they were deleted in another branch, consider rebasing to remove them from the report. Instead if they were added back, click here to restore them. ↩

cursor

Cursor Bugbot has reviewed your changes and found 2 potential issues.

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libs/@local/hashql/mir/src/pass/analysis/size_estimation/dynamic.rs

libs/@local/hashql/mir/src/pass/analysis/size_estimation/range.rs

TimDiekmann · 2026-01-23T07:59:20Z

libs/@local/hashql/mir/src/pass/analysis/size_estimation/range.rs

We have something similar for temporal versioning. I guess we cannot easily use one or the other?

Not really no, it's just for isolation purposes. I thought about doing a trait, but thought that'd be overkill (hence the macro to just scaffold the types). Was inspired by the text-size crate.

Agreed, a trait would be overkill. I know I put quite a lot of work into the bound comparisons for temporal versioning, but I guess this became a lot easier nowadays. I guess it makes sense to move out common things like this but I always struggle to find a good location.
Let's leave it as it is.

libs/@local/hashql/mir/src/pass/analysis/size_estimation/dynamic.rs

Your organization requires reapproval when changes are made, so Graphite has dismissed approvals. See the output of git range-diff at https://github.com/hashintel/hash/actions/runs/21279903124

TimDiekmann · 2026-01-23T08:40:56Z

libs/@local/hashql/mir/src/pass/analysis/size_estimation/range.rs

Agreed, a trait would be overkill. I know I put quite a lot of work into the bound comparisons for temporal versioning, but I guess this became a lot easier nowadays. I guess it makes sense to move out common things like this but I always struggle to find a good location.
Let's leave it as it is.

github-actions · 2026-01-23T09:47:12Z

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	$$26.1 \mathrm{ms} \pm 155 \mathrm{μs}\left({\color{gray}-2.182 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.29 \mathrm{ms} \pm 14.0 \mathrm{μs}\left({\color{gray}1.97 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1001	$$11.8 \mathrm{ms} \pm 62.9 \mathrm{μs}\left({\color{gray}-4.923 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 3314	$$41.8 \mathrm{ms} \pm 266 \mathrm{μs}\left({\color{gray}-2.522 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$15.2 \mathrm{ms} \pm 106 \mathrm{μs}\left({\color{red}5.83 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 1526	$$23.6 \mathrm{ms} \pm 135 \mathrm{μs}\left({\color{gray}-1.485 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 2078	$$26.3 \mathrm{ms} \pm 136 \mathrm{μs}\left({\color{lightgreen}-38.701 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.63 \mathrm{ms} \pm 16.9 \mathrm{μs}\left({\color{lightgreen}-82.326 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 1033	$$14.1 \mathrm{ms} \pm 90.8 \mathrm{μs}\left({\color{lightgreen}-50.450 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_medium

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 102	$$3.62 \mathrm{ms} \pm 14.6 \mathrm{μs}\left({\color{gray}0.884 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.81 \mathrm{ms} \pm 12.3 \mathrm{μs}\left({\color{gray}0.104 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 51	$$3.16 \mathrm{ms} \pm 13.6 \mathrm{μs}\left({\color{gray}0.284 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 269	$$4.92 \mathrm{ms} \pm 23.4 \mathrm{μs}\left({\color{gray}-0.626 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$3.38 \mathrm{ms} \pm 13.3 \mathrm{μs}\left({\color{gray}-0.332 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 107	$$3.92 \mathrm{ms} \pm 19.7 \mathrm{μs}\left({\color{gray}-0.291 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 133	$$4.25 \mathrm{ms} \pm 25.5 \mathrm{μs}\left({\color{gray}0.838 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.24 \mathrm{ms} \pm 10.8 \mathrm{μs}\left({\color{gray}0.824 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 63	$$3.84 \mathrm{ms} \pm 30.0 \mathrm{μs}\left({\color{gray}0.154 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_none

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2	$$2.54 \mathrm{ms} \pm 11.9 \mathrm{μs}\left({\color{red}7.85 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.49 \mathrm{ms} \pm 12.1 \mathrm{μs}\left({\color{red}7.21 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1	$$2.57 \mathrm{ms} \pm 10.6 \mathrm{μs}\left({\color{red}7.29 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 8	$$2.77 \mathrm{ms} \pm 13.2 \mathrm{μs}\left({\color{red}5.95 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.68 \mathrm{ms} \pm 11.6 \mathrm{μs}\left({\color{red}7.09 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 3	$$2.85 \mathrm{ms} \pm 12.0 \mathrm{μs}\left({\color{red}5.70 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_small

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 52	$$2.89 \mathrm{ms} \pm 14.2 \mathrm{μs}\left({\color{gray}4.80 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.63 \mathrm{ms} \pm 13.9 \mathrm{μs}\left({\color{red}9.06 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 25	$$2.81 \mathrm{ms} \pm 14.6 \mathrm{μs}\left({\color{red}9.97 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 94	$$3.25 \mathrm{ms} \pm 18.3 \mathrm{μs}\left({\color{red}6.50 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$2.83 \mathrm{ms} \pm 11.7 \mathrm{μs}\left({\color{red}7.12 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 26	$$3.04 \mathrm{ms} \pm 15.9 \mathrm{μs}\left({\color{red}5.78 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 66	$$3.13 \mathrm{ms} \pm 14.2 \mathrm{μs}\left({\color{gray}3.85 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.80 \mathrm{ms} \pm 11.9 \mathrm{μs}\left({\color{red}6.79 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 29	$$3.02 \mathrm{ms} \pm 14.6 \mathrm{μs}\left({\color{red}5.78 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_complete

Function	Value	Mean	Flame graphs
entity_by_id;one_depth	1 entities	$$38.7 \mathrm{ms} \pm 155 \mathrm{μs}\left({\color{gray}-0.057 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	10 entities	$$77.4 \mathrm{ms} \pm 307 \mathrm{μs}\left({\color{gray}1.93 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	25 entities	$$44.2 \mathrm{ms} \pm 211 \mathrm{μs}\left({\color{gray}1.99 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	5 entities	$$45.5 \mathrm{ms} \pm 174 \mathrm{μs}\left({\color{gray}-0.607 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	50 entities	$$53.5 \mathrm{ms} \pm 239 \mathrm{μs}\left({\color{gray}1.37 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	1 entities	$$40.2 \mathrm{ms} \pm 148 \mathrm{μs}\left({\color{gray}2.17 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	10 entities	$$425 \mathrm{ms} \pm 736 \mathrm{μs}\left({\color{gray}2.91 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	25 entities	$$97.2 \mathrm{ms} \pm 412 \mathrm{μs}\left({\color{gray}4.64 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	5 entities	$$85.7 \mathrm{ms} \pm 310 \mathrm{μs}\left({\color{gray}0.893 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	50 entities	$$284 \mathrm{ms} \pm 763 \mathrm{μs}\left({\color{lightgreen}-8.891 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	1 entities	$$14.9 \mathrm{ms} \pm 90.1 \mathrm{μs}\left({\color{red}5.60 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	10 entities	$$14.8 \mathrm{ms} \pm 70.7 \mathrm{μs}\left({\color{gray}1.32 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	25 entities	$$15.0 \mathrm{ms} \pm 62.9 \mathrm{μs}\left({\color{gray}0.406 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	5 entities	$$14.7 \mathrm{ms} \pm 70.8 \mathrm{μs}\left({\color{gray}2.55 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	50 entities	$$18.0 \mathrm{ms} \pm 102 \mathrm{μs}\left({\color{gray}0.623 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_linkless

Function	Value	Mean	Flame graphs
entity_by_id	1 entities	$$14.7 \mathrm{ms} \pm 64.8 \mathrm{μs}\left({\color{gray}-0.301 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10 entities	$$14.9 \mathrm{ms} \pm 53.5 \mathrm{μs}\left({\color{gray}1.46 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	100 entities	$$14.9 \mathrm{ms} \pm 81.9 \mathrm{μs}\left({\color{gray}2.61 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	1000 entities	$$15.4 \mathrm{ms} \pm 78.6 \mathrm{μs}\left({\color{gray}0.922 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10000 entities	$$22.6 \mathrm{ms} \pm 159 \mathrm{μs}\left({\color{gray}2.15 \mathrm{\%}}\right) $$	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`	$$29.7 \mathrm{ms} \pm 293 \mathrm{μs}\left({\color{gray}2.90 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/book/v/1`	$$30.4 \mathrm{ms} \pm 278 \mathrm{μs}\left({\color{gray}1.86 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/building/v/1`	$$30.2 \mathrm{ms} \pm 275 \mathrm{μs}\left({\color{gray}2.29 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/organization/v/1`	$$29.8 \mathrm{ms} \pm 297 \mathrm{μs}\left({\color{gray}3.78 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/page/v/2`	$$29.8 \mathrm{ms} \pm 281 \mathrm{μs}\left({\color{gray}-1.113 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/person/v/1`	$$30.2 \mathrm{ms} \pm 259 \mathrm{μs}\left({\color{gray}3.74 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/playlist/v/1`	$$29.8 \mathrm{ms} \pm 258 \mathrm{μs}\left({\color{gray}0.040 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/song/v/1`	$$30.1 \mathrm{ms} \pm 289 \mathrm{μs}\left({\color{red}5.71 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: `https://blockprotocol.org/@alice/types/entity-type/uk-address/v/1`	$$29.6 \mathrm{ms} \pm 303 \mathrm{μs}\left({\color{gray}-0.194 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity_type

Function	Value	Mean	Flame graphs
get_entity_type_by_id	Account ID: `bf5a9ef5-dc3b-43cf-a291-6210c0321eba`	$$8.17 \mathrm{ms} \pm 35.9 \mathrm{μs}\left({\color{gray}2.83 \mathrm{\%}}\right) $$	Flame Graph

representative_read_multiple_entities

Function	Value	Mean	Flame graphs
entity_by_property	traversal_paths=0	0	$$47.4 \mathrm{ms} \pm 229 \mathrm{μs}\left({\color{gray}2.86 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$94.0 \mathrm{ms} \pm 334 \mathrm{μs}\left({\color{gray}-0.122 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$53.4 \mathrm{ms} \pm 275 \mathrm{μs}\left({\color{gray}-0.398 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$62.2 \mathrm{ms} \pm 244 \mathrm{μs}\left({\color{gray}0.866 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$69.2 \mathrm{ms} \pm 373 \mathrm{μs}\left({\color{gray}-2.278 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$75.2 \mathrm{ms} \pm 395 \mathrm{μs}\left({\color{gray}-1.984 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=0	0	$$50.7 \mathrm{ms} \pm 307 \mathrm{μs}\left({\color{gray}1.54 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$78.2 \mathrm{ms} \pm 363 \mathrm{μs}\left({\color{gray}2.10 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$57.8 \mathrm{ms} \pm 262 \mathrm{μs}\left({\color{gray}1.61 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$64.6 \mathrm{ms} \pm 247 \mathrm{μs}\left({\color{gray}-0.572 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$67.0 \mathrm{ms} \pm 366 \mathrm{μs}\left({\color{gray}-1.636 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$67.5 \mathrm{ms} \pm 326 \mathrm{μs}\left({\color{gray}2.53 \mathrm{\%}}\right) $$

scenarios

Function	Value	Mean	Flame graphs
full_test	query-limited	$$137 \mathrm{ms} \pm 641 \mathrm{μs}\left({\color{gray}1.05 \mathrm{\%}}\right) $$	Flame Graph
full_test	query-unlimited	$$137 \mathrm{ms} \pm 462 \mathrm{μs}\left({\color{gray}-0.219 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-limited	$$39.1 \mathrm{ms} \pm 172 \mathrm{μs}\left({\color{lightgreen}-62.386 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-unlimited	$$584 \mathrm{ms} \pm 1.31 \mathrm{ms}\left({\color{gray}-4.962 \mathrm{\%}}\right) $$	Flame Graph

indietyp added 25 commits January 15, 2026 16:10

fix: lockfile

0d2323f

feat: static size estimation

4fc773a

feat: checkpoint

06494b4

feat: checkpoint

1afc8d1

feat: checkpoint

363dc2b

feat: footprint analysis

74a0b2e

feat: dynamic analysis

2137aa9

feat: dynamic analysis calls

3d04b9a

feat: size estimation

af4647d

feat: non recursive size estimation

fd63dde

feat: size estimation cleanup

469ec99

feat: checkpoint

397e8c3

feat: fixpoint iteration

12bc43d

feat: bounded data analysis

6d35502

feat: bounded data analysis

116e2f0

fix: review

02f306e

fix: apply suggestions from code review

5797045

chore: docs

f6b3b72

chore: rename struct

79b02d9

feat: tests v1

5461769

feat: tests v1

b229135

feat: snapshot testing

f2aeaf0

feat: snapshot testing

66f81e5

feat: tests

c500928

feat: dynamic test

1da444f

graphite-app bot assigned indietyp Jan 20, 2026

github-actions bot added area/infra Relates to version control, CI, CD or IaC (area) area/libs Relates to first-party libraries/crates/packages (area) type/eng > backend Owned by the @backend team labels Jan 20, 2026

This was referenced Jan 20, 2026

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

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

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indietyp mentioned this pull request Jan 20, 2026

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

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libs/@local/hashql/mir/src/pass/analysis/size_estimation/mod.rs Outdated Show resolved Hide resolved

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indietyp added 3 commits January 21, 2026 09:24

feat: suggestions from code review

f3c3ed6

chore: range reimpl

784a4dc

feat: saturating add

bc0a7a3

vercel bot temporarily deployed to Preview – petrinaut January 21, 2026 09:11 Inactive

chore: max iteration count fix

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fix: docs

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TimDiekmann previously approved these changes Jan 23, 2026

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Update dynamic.rs

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TimDiekmann approved these changes Jan 23, 2026

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

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

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indietyp commented Jan 20, 2026

🌟 What is the purpose of this PR?

🔍 What does this change?

Pre-Merge Checklist 🚀

🚢 Has this modified a publishable library?

📜 Does this require a change to the docs?

🕸️ Does this require a change to the Turbo Graph?

🛡 What tests cover this?

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github-actions bot commented Jan 23, 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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