BE-524: HashQL: Remove island schedule module

[indietyp]

🌟 What is the purpose of this PR?

The island::schedule module, which provided topological scheduling with parallelism level assignment for execution islands, has been removed. This includes the IslandSchedule and ScheduledIsland types, the schedule and schedule_in methods on IslandGraph, and all associated tests. The scheduling functionality is no longer needed or has been superseded by an alternative approach.

🔍 What does this change?

• Removes the schedule submodule from island/mod.rs
• Deletes island/schedule/mod.rs, which contained the IslandSchedule and ScheduledIsland types along with the Kahn's algorithm-based topological level assignment implementation
• Deletes island/schedule/tests.rs, which contained tests for level ordering, contiguity, and full node coverage of the schedule
• Removes the public re-exports of IslandSchedule and ScheduledIsland from pass/execution/mod.rs

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?

The tests that previously covered the scheduling logic have been removed alongside the implementation.

❓ How to test this?

1. Checkout the branch
2. Run cargo test in libs/@local/hashql/mir
3. Confirm that the build succeeds and no references to IslandSchedule or ScheduledIsland remain

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[cursor]

PR Summary

Low Risk
Dead-code removal with no remaining references; island graph construction and placement are untouched.

Overview
Removes the island::schedule submodule and its public API from HashQL MIR execution placement.

Deleted: IslandSchedule, ScheduledIsland, and IslandGraph::schedule / schedule_in (Kahn-style topological ordering with per-island parallelism levels). The schedule module declaration and re-exports in pass/execution/mod.rs are dropped; island/graph and island discovery via IslandPlacement are unchanged.

^{Reviewed by Cursor Bugbot for commit 02b718a. Bugbot is set up for automated code reviews on this repo. Configure here.}

[indietyp]

• BE-537: Remove old filter API backend and wire up HashQL in the API #8827
• BE-513: HashQL: Rework dynamic aggregate size estimation #8697
• BE-512: HashQL: Allow cross-backend transitions for SwitchInt terminators #8696
• BE-524: HashQL: Remove island schedule module #8694 👈 (View in Graphite)
• BE-525: HashQL: Remove implicit type-widening from Integer operations, and make comparison size aware #8693
• BE-515: HashQL: Remove OFFSET 0 materialization fence from generated query #8650
• BE-514: HashQL: implement iterative adjustment passes in placement solver #8635
• 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-523: HashQL: Suspendable interpreter with structured values and input system #8675
• BE-522: HashQL: Expand stdlib type system with temporal types #8674
• 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

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[augmentcode]

🤖 Augment PR Summary

Summary: Removes the deprecated/unused HashQL MIR island scheduling implementation and its public surface area.

Changes:

• Drops the pass::execution::island::schedule submodule from island/mod.rs
• Deletes the scheduling implementation (IslandGraph::schedule/schedule_in) and the related types (IslandSchedule, ScheduledIsland)
• Removes the schedule unit tests that validated ordering/level semantics
• Removes the re-exports of IslandSchedule/ScheduledIsland from pass::execution

Technical Notes: This PR is primarily an API/code deletion; downstream code must no longer rely on the removed scheduling helpers and should use the newer/superseding execution approach instead.

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[augmentcode]

Review completed. No suggestions at this time.

Comment augment review to trigger a new review at any time.

[codspeed-hq]

Merging this PR will not alter performance

✅ 24 untouched benchmarks
⏩ 56 skipped benchmarks¹

---

_{Comparing bm/be-524-hashql-remove-island-dag (02b718a) with main (eeea5cb)}

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. ↩

The base branch was changed.

[graphite-app]

Merge activity

• Jun 1, 12:59 PM UTC: Graphite rebased this pull request, because this pull request is set to merge when ready.

[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	$$25.8 \mathrm{ms} \pm 171 \mathrm{μs}\left({\color{gray}0.753 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.50 \mathrm{ms} \pm 22.0 \mathrm{μs}\left({\color{gray}3.48 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1001	$$12.4 \mathrm{ms} \pm 101 \mathrm{μs}\left({\color{gray}2.26 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 3314	$$42.9 \mathrm{ms} \pm 330 \mathrm{μs}\left({\color{gray}3.40 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$16.5 \mathrm{ms} \pm 167 \mathrm{μs}\left({\color{red}16.7 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 1526	$$23.9 \mathrm{ms} \pm 161 \mathrm{μs}\left({\color{gray}2.63 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 2078	$$26.0 \mathrm{ms} \pm 169 \mathrm{μs}\left({\color{gray}-0.751 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.72 \mathrm{ms} \pm 14.7 \mathrm{μs}\left({\color{gray}1.38 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 1033	$$13.0 \mathrm{ms} \pm 96.6 \mathrm{μs}\left({\color{gray}-1.288 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_medium

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 102	$$3.76 \mathrm{ms} \pm 27.1 \mathrm{μs}\left({\color{gray}2.77 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$3.01 \mathrm{ms} \pm 13.4 \mathrm{μs}\left({\color{gray}0.751 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 51	$$3.54 \mathrm{ms} \pm 37.4 \mathrm{μs}\left({\color{red}7.21 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 269	$$5.08 \mathrm{ms} \pm 27.5 \mathrm{μs}\left({\color{gray}0.779 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$3.55 \mathrm{ms} \pm 17.7 \mathrm{μs}\left({\color{gray}1.50 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 107	$$4.10 \mathrm{ms} \pm 24.5 \mathrm{μs}\left({\color{gray}2.11 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 133	$$4.35 \mathrm{ms} \pm 35.3 \mathrm{μs}\left({\color{gray}0.749 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$3.54 \mathrm{ms} \pm 32.0 \mathrm{μs}\left({\color{gray}3.06 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 63	$$4.02 \mathrm{ms} \pm 21.7 \mathrm{μs}\left({\color{gray}0.756 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_none

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 2	$$2.61 \mathrm{ms} \pm 13.8 \mathrm{μs}\left({\color{gray}2.51 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.51 \mathrm{ms} \pm 14.0 \mathrm{μs}\left({\color{gray}0.958 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 1	$$2.57 \mathrm{ms} \pm 9.64 \mathrm{μs}\left({\color{gray}0.724 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 8	$$2.85 \mathrm{ms} \pm 13.2 \mathrm{μs}\left({\color{gray}0.268 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.67 \mathrm{ms} \pm 13.8 \mathrm{μs}\left({\color{gray}3.24 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 3	$$2.84 \mathrm{ms} \pm 18.7 \mathrm{μs}\left({\color{gray}2.47 \mathrm{\%}}\right) $$	Flame Graph

policy_resolution_small

Function	Value	Mean	Flame graphs
resolve_policies_for_actor	user: empty, selectivity: high, policies: 52	$$3.01 \mathrm{ms} \pm 15.0 \mathrm{μs}\left({\color{gray}0.907 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: low, policies: 1	$$2.76 \mathrm{ms} \pm 13.6 \mathrm{μs}\left({\color{gray}1.37 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: empty, selectivity: medium, policies: 25	$$3.01 \mathrm{ms} \pm 18.2 \mathrm{μs}\left({\color{gray}2.53 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: high, policies: 94	$$3.39 \mathrm{ms} \pm 18.0 \mathrm{μs}\left({\color{gray}1.81 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: low, policies: 1	$$2.97 \mathrm{ms} \pm 15.9 \mathrm{μs}\left({\color{gray}0.624 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: seeded, selectivity: medium, policies: 26	$$3.26 \mathrm{ms} \pm 16.0 \mathrm{μs}\left({\color{gray}-0.600 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: high, policies: 66	$$3.28 \mathrm{ms} \pm 16.7 \mathrm{μs}\left({\color{gray}-0.021 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: low, policies: 1	$$2.95 \mathrm{ms} \pm 14.7 \mathrm{μs}\left({\color{gray}1.06 \mathrm{\%}}\right) $$	Flame Graph
resolve_policies_for_actor	user: system, selectivity: medium, policies: 29	$$3.28 \mathrm{ms} \pm 15.9 \mathrm{μs}\left({\color{gray}1.27 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_complete

Function	Value	Mean	Flame graphs
entity_by_id;one_depth	1 entities	$$58.1 \mathrm{ms} \pm 439 \mathrm{μs}\left({\color{red}8.13 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	10 entities	$$49.3 \mathrm{ms} \pm 388 \mathrm{μs}\left({\color{red}8.21 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	25 entities	$$53.0 \mathrm{ms} \pm 375 \mathrm{μs}\left({\color{red}5.74 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	5 entities	$$46.9 \mathrm{ms} \pm 335 \mathrm{μs}\left({\color{red}7.60 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;one_depth	50 entities	$$65.3 \mathrm{ms} \pm 420 \mathrm{μs}\left({\color{red}6.49 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	1 entities	$$62.5 \mathrm{ms} \pm 408 \mathrm{μs}\left({\color{gray}3.09 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	10 entities	$$61.5 \mathrm{ms} \pm 301 \mathrm{μs}\left({\color{red}9.72 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	25 entities	$$108 \mathrm{ms} \pm 575 \mathrm{μs}\left({\color{red}5.78 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	5 entities	$$50.9 \mathrm{ms} \pm 417 \mathrm{μs}\left({\color{red}10.8 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;two_depth	50 entities	$$299 \mathrm{ms} \pm 872 \mathrm{μs}\left({\color{gray}4.38 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	1 entities	$$21.3 \mathrm{ms} \pm 122 \mathrm{μs}\left({\color{red}9.56 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	10 entities	$$22.4 \mathrm{ms} \pm 111 \mathrm{μs}\left({\color{red}11.6 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	25 entities	$$21.1 \mathrm{ms} \pm 109 \mathrm{μs}\left({\color{red}5.23 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	5 entities	$$21.1 \mathrm{ms} \pm 135 \mathrm{μs}\left({\color{red}10.3 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id;zero_depth	50 entities	$$25.5 \mathrm{ms} \pm 142 \mathrm{μs}\left({\color{gray}1.09 \mathrm{\%}}\right) $$	Flame Graph

read_scaling_linkless

Function	Value	Mean	Flame graphs
entity_by_id	1 entities	$$20.0 \mathrm{ms} \pm 140 \mathrm{μs}\left({\color{gray}4.67 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10 entities	$$19.8 \mathrm{ms} \pm 160 \mathrm{μs}\left({\color{gray}3.88 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	100 entities	$$19.9 \mathrm{ms} \pm 174 \mathrm{μs}\left({\color{gray}4.51 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	1000 entities	$$20.5 \mathrm{ms} \pm 120 \mathrm{μs}\left({\color{gray}3.35 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	10000 entities	$$26.7 \mathrm{ms} \pm 220 \mathrm{μs}\left({\color{gray}2.27 \mathrm{\%}}\right) $$	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	$$33.9 \mathrm{ms} \pm 249 \mathrm{μs}\left({\color{gray}1.41 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/book/v/1	$$34.1 \mathrm{ms} \pm 269 \mathrm{μs}\left({\color{gray}-1.088 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/building/v/1	$$34.4 \mathrm{ms} \pm 296 \mathrm{μs}\left({\color{gray}2.73 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/organization/v/1	$$34.1 \mathrm{ms} \pm 324 \mathrm{μs}\left({\color{gray}-0.945 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/page/v/2	$$34.4 \mathrm{ms} \pm 330 \mathrm{μs}\left({\color{gray}-0.133 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/person/v/1	$$34.4 \mathrm{ms} \pm 365 \mathrm{μs}\left({\color{gray}2.94 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/playlist/v/1	$$33.4 \mathrm{ms} \pm 293 \mathrm{μs}\left({\color{gray}-3.350 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/song/v/1	$$34.0 \mathrm{ms} \pm 280 \mathrm{μs}\left({\color{gray}-3.956 \mathrm{\%}}\right) $$	Flame Graph
entity_by_id	entity type ID: https://blockprotocol.org/@alice/types/entity-type/uk-address/v/1	$$34.8 \mathrm{ms} \pm 290 \mathrm{μs}\left({\color{gray}1.88 \mathrm{\%}}\right) $$	Flame Graph

representative_read_entity_type

Function	Value	Mean	Flame graphs
get_entity_type_by_id	Account ID: bf5a9ef5-dc3b-43cf-a291-6210c0321eba	$$8.46 \mathrm{ms} \pm 33.4 \mathrm{μs}\left({\color{gray}1.33 \mathrm{\%}}\right) $$	Flame Graph

representative_read_multiple_entities

Function	Value	Mean	Flame graphs
entity_by_property	traversal_paths=0	0	$$89.4 \mathrm{ms} \pm 357 \mathrm{μs}\left({\color{gray}-1.190 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$143 \mathrm{ms} \pm 584 \mathrm{μs}\left({\color{gray}-0.041 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$97.6 \mathrm{ms} \pm 501 \mathrm{μs}\left({\color{gray}1.02 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$109 \mathrm{ms} \pm 755 \mathrm{μs}\left({\color{gray}1.03 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$115 \mathrm{ms} \pm 580 \mathrm{μs}\left({\color{gray}0.771 \mathrm{\%}}\right) $$
entity_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$123 \mathrm{ms} \pm 623 \mathrm{μs}\left({\color{gray}-0.214 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=0	0	$$102 \mathrm{ms} \pm 527 \mathrm{μs}\left({\color{gray}0.248 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=255	1,resolve_depths=inherit:1;values:255;properties:255;links:127;link_dests:126;type:true	$$131 \mathrm{ms} \pm 538 \mathrm{μs}\left({\color{gray}0.344 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:0;link_dests:0;type:false	$$108 \mathrm{ms} \pm 466 \mathrm{μs}\left({\color{gray}-0.233 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:0;links:1;link_dests:0;type:true	$$119 \mathrm{ms} \pm 657 \mathrm{μs}\left({\color{gray}0.541 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:0;properties:2;links:1;link_dests:0;type:true	$$119 \mathrm{ms} \pm 670 \mathrm{μs}\left({\color{gray}0.343 \mathrm{\%}}\right) $$
link_by_source_by_property	traversal_paths=2	1,resolve_depths=inherit:0;values:2;properties:2;links:1;link_dests:0;type:true	$$119 \mathrm{ms} \pm 464 \mathrm{μs}\left({\color{gray}-0.039 \mathrm{\%}}\right) $$

scenarios

Function	Value	Mean	Flame graphs
full_test	query-limited	$$190 \mathrm{ms} \pm 885 \mathrm{μs}\left({\color{red}11.0 \mathrm{\%}}\right) $$	Flame Graph
full_test	query-unlimited	$$185 \mathrm{ms} \pm 2.34 \mathrm{ms}\left({\color{red}29.4 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-limited	$$40.1 \mathrm{ms} \pm 202 \mathrm{μs}\left({\color{gray}-1.326 \mathrm{\%}}\right) $$	Flame Graph
linked_queries	query-unlimited	$$570 \mathrm{ms} \pm 978 \mathrm{μs}\left({\color{gray}0.433 \mathrm{\%}}\right) $$	Flame Graph