BE-514: HashQL: implement iterative adjustment passes in placement solver

[indietyp]

🌟 What is the purpose of this PR?

This PR enhances the placement solver in the MIR dataflow analysis framework by implementing iterative adjustment passes that alternate direction until convergence. Instead of a simple two-pass approach (forward then backward), the solver now continues refining assignments in alternating directions until no further improvements are found, converging to a better local minimum.

🔍 What does this change?

• Adds a reverse() method to the Direction enum to toggle between Forward and Backward directions
• Replaces the single backward refinement pass with iterative adjustment passes that alternate direction until convergence
• Modifies adjust_trivial() and adjust_cyclic() methods to return boolean flags indicating whether assignments changed
• Introduces a new run_adjustment() method that processes regions in the specified direction and tracks changes
• Updates documentation to reflect the new iterative convergence approach rather than the previous two-pass strategy

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?

• Existing placement solver tests continue to validate the functionality
• Updated test in backward_pass_keeps_assignment_when_csp_fails() to handle the new return signature

❓ How to test this?

1. Checkout the branch
2. Run the existing MIR placement solver tests
3. Confirm that placement optimization still works correctly with the new iterative approach

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Project	Deployment	Actions	Updated (UTC)
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[indietyp]

Warning

This pull request is not mergeable via GitHub because a downstack PR is open. Once all requirements are satisfied, merge this PR as a stack on Graphite.
Learn more

• BE-514: HashQL: implement iterative adjustment passes in placement solver #8635 👈 (View in Graphite)
• BE-500, BE-501: HashQL: Unify mixed parameter resolution in data dependency analysis #8607
• BE-494: HashQL: Loop-breaker selection for recursive inlining #8600
• BE-482: HashQL: Remove logical not from MIR and fix postgres boolean lowering #8595
• BE-474: HashQL: Take into account terminator target eligibility in execution backend placement #8587
• BE-455: HashQL: Introduce evaluation orchestrator #8586
• BE-306: HashQL: PostgreSQL translation #8526
• BE-457: HashQL: MIR execution pipeline extensions for postgres compilation #8525
• BE-456: HashQL: Entity type model enrichment and graph store query extensions #8524
• BE-429: HashQL: Add island dependency graph with data requirement resolution #8501
• BE-428: HashQL: Simplify traversal tracking to path recording #8498
• BE-434: HashQL: Refactor entity storage path resolution with centralized schema mapping #8496
• BE-427: Add hashql-macros crate with procedural macros for ID types and symbols #8495
• main

This stack of pull requests is managed by Graphite. Learn more about stacking.

[indietyp]

remind me to add a test case for the reason this fails before merging:

3-node chain A → B → C:

 - Unary costs: u_A(0)=3, u_A(1)=0; u_B(0)=u_B(1)=0; u_C(0)=0
 - Edge costs: A–B mismatch cost 2, B–C mismatch cost 1
 - Start from labeling 000

Backward sweep (sinks first): C stays 0, B stays 0 (given A=0, C=0), A switches to 1 (given B=0). Final: 1,0,0.

But changing only B from 0 to 1 lowers total cost — so the result after one sweep is not a 1-opt local minimum.

[codecov]

Codecov Report

❌ Patch coverage is 97.36842% with 1 line in your changes missing coverage. Please review.
✅ Project coverage is 71.72%. Comparing base (77eb677) to head (60563b3).

Files with missing lines	Patch %	Lines
...shql/mir/src/pass/execution/placement/solve/mod.rs	96.87%	1 Missing ⚠️

Additional details and impacted files

@@                                       Coverage Diff                                       @@
##           bm/be-500-hashql-forward-substitution-unified-param-resolution    #8635   +/-   ##
===============================================================================================
  Coverage                                                           71.71%   71.72%           
===============================================================================================
  Files                                                                1047     1047           
  Lines                                                              104282   104301   +19     
  Branches                                                             4665     4666    +1     
===============================================================================================
+ Hits                                                                74791    74810   +19     
  Misses                                                              28716    28716           
  Partials                                                              775      775           

Flag	Coverage Δ
apps.hash-ai-worker-ts	1.40% <ø> (ø)
apps.hash-api	0.00% <ø> (ø)
local.hash-graph-sdk	9.63% <ø> (ø)
local.hash-isomorphic-utils	0.00% <ø> (ø)
rust.hash-graph-api	2.52% <ø> (ø)
rust.hashql-compiletest	28.31% <ø> (ø)
rust.hashql-mir	91.69% <97.36%> (+<0.01%)	⬆️

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[codspeed-hq]

Merging this PR will not alter performance

✅ 24 untouched benchmarks
⏩ 56 skipped benchmarks¹

---

_{Comparing bm/be-514-hashql-solver-iterate-forwardbackward-passes-to-convergence (60563b3) with bm/be-500-hashql-forward-substitution-unified-param-resolution (77eb677)}

Footnotes

1. 56 benchmarks were skipped, so the baseline results were used instead. If they were deleted from the codebase, click here and archive them to remove them from the performance reports. ↩

[github-actions]

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	$$28.5 \mathrm{ms} \pm 181 \mathrm{μs}\left({\color{gray}1.65 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.56 \mathrm{ms} \pm 26.0 \mathrm{μs}\left({\color{gray}2.66 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1001	$$14.0 \mathrm{ms} \pm 88.3 \mathrm{μs}\left({\color{red}11.2 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 3314	$$44.4 \mathrm{ms} \pm 341 \mathrm{μs}\left({\color{gray}2.69 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$15.0 \mathrm{ms} \pm 127 \mathrm{μs}\left({\color{gray}1.55 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 1526	$$25.0 \mathrm{ms} \pm 199 \mathrm{μs}\left({\color{gray}1.62 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 2078	$$29.1 \mathrm{ms} \pm 173 \mathrm{μs}\left({\color{gray}0.975 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.88 \mathrm{ms} \pm 23.1 \mathrm{μs}\left({\color{gray}1.83 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 1033	$$14.9 \mathrm{ms} \pm 102 \mathrm{μs}\left({\color{gray}3.62 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_medium

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 102	$$3.87 \mathrm{ms} \pm 21.3 \mathrm{μs}\left({\color{gray}1.15 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.13 \mathrm{ms} \pm 23.8 \mathrm{μs}\left({\color{gray}3.96 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 51	$$3.42 \mathrm{ms} \pm 19.4 \mathrm{μs}\left({\color{gray}-0.015 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 269	$$5.35 \mathrm{ms} \pm 47.7 \mathrm{μs}\left({\color{gray}2.68 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$3.62 \mathrm{ms} \pm 21.2 \mathrm{μs}\left({\color{gray}1.69 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 107	$$4.31 \mathrm{ms} \pm 24.1 \mathrm{μs}\left({\color{gray}2.90 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 133	$$4.48 \mathrm{ms} \pm 27.8 \mathrm{μs}\left({\color{gray}0.231 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.47 \mathrm{ms} \pm 19.3 \mathrm{μs}\left({\color{gray}-0.330 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 63	$$4.16 \mathrm{ms} \pm 29.0 \mathrm{μs}\left({\color{gray}1.76 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_none

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2	$$2.69 \mathrm{ms} \pm 15.1 \mathrm{μs}\left({\color{gray}2.54 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.56 \mathrm{ms} \pm 14.7 \mathrm{μs}\left({\color{gray}1.52 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1	$$2.67 \mathrm{ms} \pm 12.9 \mathrm{μs}\left({\color{gray}3.43 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 8	$$2.95 \mathrm{ms} \pm 15.2 \mathrm{μs}\left({\color{gray}2.35 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.78 \mathrm{ms} \pm 17.2 \mathrm{μs}\left({\color{gray}3.64 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 3	$$2.95 \mathrm{ms} \pm 16.4 \mathrm{μs}\left({\color{gray}3.89 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_small

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 52	$$3.12 \mathrm{ms} \pm 20.4 \mathrm{μs}\left({\color{gray}2.00 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.74 \mathrm{ms} \pm 13.1 \mathrm{μs}\left({\color{gray}-2.139 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 25	$$3.09 \mathrm{ms} \pm 15.8 \mathrm{μs}\left({\color{gray}1.71 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 94	$$3.45 \mathrm{ms} \pm 15.7 \mathrm{μs}\left({\color{gray}-1.284 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$2.98 \mathrm{ms} \pm 15.8 \mathrm{μs}\left({\color{gray}-1.542 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 26	$$3.29 \mathrm{ms} \pm 18.0 \mathrm{μs}\left({\color{gray}-1.296 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 66	$$3.47 \mathrm{ms} \pm 24.0 \mathrm{μs}\left({\color{gray}2.17 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.05 \mathrm{ms} \pm 18.0 \mathrm{μs}\left({\color{gray}1.54 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 29	$$3.38 \mathrm{ms} \pm 18.3 \mathrm{μs}\left({\color{gray}1.39 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_complete

Function	Value	Mean	Flame graphs
entity_by_id;one_depth	1 entities	$$55.2 \mathrm{ms} \pm 306 \mathrm{μs}\left({\color{gray}-2.261 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	10 entities	$$47.0 \mathrm{ms} \pm 217 \mathrm{μs}\left({\color{gray}-0.856 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	25 entities	$$52.4 \mathrm{ms} \pm 483 \mathrm{μs}\left({\color{gray}-0.509 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	5 entities	$$45.7 \mathrm{ms} \pm 255 \mathrm{μs}\left({\color{gray}-1.137 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	50 entities	$$64.3 \mathrm{ms} \pm 392 \mathrm{μs}\left({\color{gray}0.409 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	1 entities	$$62.7 \mathrm{ms} \pm 330 \mathrm{μs}\left({\color{gray}-1.695 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	10 entities	$$56.9 \mathrm{ms} \pm 345 \mathrm{μs}\left({\color{gray}-1.657 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	25 entities	$$104 \mathrm{ms} \pm 569 \mathrm{μs}\left({\color{gray}-2.098 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	5 entities	$$47.2 \mathrm{ms} \pm 204 \mathrm{μs}\left({\color{gray}-1.783 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	50 entities	$$298 \mathrm{ms} \pm 705 \mathrm{μs}\left({\color{red}7.32 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	1 entities	$$19.6 \mathrm{ms} \pm 117 \mathrm{μs}\left({\color{gray}-1.954 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	10 entities	$$20.2 \mathrm{ms} \pm 90.3 \mathrm{μs}\left({\color{gray}-2.922 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	25 entities	$$20.4 \mathrm{ms} \pm 108 \mathrm{μs}\left({\color{gray}-1.612 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	5 entities	$$19.5 \mathrm{ms} \pm 109 \mathrm{μs}\left({\color{gray}-3.150 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	50 entities	$$25.1 \mathrm{ms} \pm 132 \mathrm{μs}\left({\color{gray}-0.192 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_linkless

Function	Value	Mean	Flame graphs
entity_by_id	1 entities	$$19.8 \mathrm{ms} \pm 125 \mathrm{μs}\left({\color{gray}1.13 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10 entities	$$20.1 \mathrm{ms} \pm 139 \mathrm{μs}\left({\color{gray}0.510 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	100 entities	$$20.1 \mathrm{ms} \pm 112 \mathrm{μs}\left({\color{gray}1.25 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	1000 entities	$$20.8 \mathrm{ms} \pm 124 \mathrm{μs}\left({\color{gray}1.24 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10000 entities	$$27.3 \mathrm{ms} \pm 226 \mathrm{μs}\left({\color{gray}2.61 \mathrm{\%}}\right) $$	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	$$36.5 \mathrm{ms} \pm 376 \mathrm{μs}\left({\color{gray}2.92 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/book/v/1	$$35.0 \mathrm{ms} \pm 262 \mathrm{μs}\left({\color{gray}2.23 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/building/v/1	$$36.8 \mathrm{ms} \pm 338 \mathrm{μs}\left({\color{gray}2.52 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/organization/v/1	$$34.8 \mathrm{ms} \pm 312 \mathrm{μs}\left({\color{gray}-2.050 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/page/v/2	$$36.9 \mathrm{ms} \pm 300 \mathrm{μs}\left({\color{gray}2.69 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/person/v/1	$$34.5 \mathrm{ms} \pm 332 \mathrm{μs}\left({\color{gray}-2.947 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/playlist/v/1	$$35.4 \mathrm{ms} \pm 291 \mathrm{μs}\left({\color{gray}-1.917 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/song/v/1	$$37.2 \mathrm{ms} \pm 287 \mathrm{μs}\left({\color{red}5.30 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/uk-address/v/1	$$35.2 \mathrm{ms} \pm 299 \mathrm{μs}\left({\color{gray}2.03 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity_type

Function	Value	Mean	Flame graphs
get_entity_type_by_id	Account ID: bf5a9ef5-dc3b-43cf-a291-6210c0321eba	$$8.67 \mathrm{ms} \pm 41.8 \mathrm{μs}\left({\color{gray}-1.015 \mathrm{\%}}\right) $$	Flame Graph

representative_read_multiple_entities

Function	Value	Mean	Flame graphs
entity_by_property	traversal_paths=0	0	$$94.7 \mathrm{ms} \pm 503 \mathrm{μs}\left({\color{gray}-1.604 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$149 \mathrm{ms} \pm 964 \mathrm{μs}\left({\color{gray}-1.478 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$101 \mathrm{ms} \pm 604 \mathrm{μs}\left({\color{gray}-1.090 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$112 \mathrm{ms} \pm 766 \mathrm{μs}\left({\color{gray}-1.633 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$119 \mathrm{ms} \pm 666 \mathrm{μs}\left({\color{gray}-1.622 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$129 \mathrm{ms} \pm 760 \mathrm{μs}\left({\color{gray}-1.414 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=0	0	$$103 \mathrm{ms} \pm 588 \mathrm{μs}\left({\color{gray}-1.147 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$134 \mathrm{ms} \pm 771 \mathrm{μs}\left({\color{gray}-2.130 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$111 \mathrm{ms} \pm 572 \mathrm{μs}\left({\color{gray}-0.013 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$120 \mathrm{ms} \pm 641 \mathrm{μs}\left({\color{gray}-0.442 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$122 \mathrm{ms} \pm 687 \mathrm{μs}\left({\color{gray}-1.495 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$123 \mathrm{ms} \pm 655 \mathrm{μs}\left({\color{gray}-0.669 \mathrm{\%}}\right) $$

scenarios

Function	Value	Mean	Flame graphs
full_test	query-limited	$$153 \mathrm{ms} \pm 2.23 \mathrm{ms}\left({\color{lightgreen}-16.984 \mathrm{\%}}\right) $$	Flame Graph
full_test	query-unlimited	$$150 \mathrm{ms} \pm 667 \mathrm{μs}\left({\color{lightgreen}-23.785 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-limited	$$41.9 \mathrm{ms} \pm 239 \mathrm{μs}\left({\color{lightgreen}-61.350 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-unlimited	$$565 \mathrm{ms} \pm 958 \mathrm{μs}\left({\color{lightgreen}-6.968 \mathrm{\%}}\right) $$	Flame Graph