perf: improve precompute performance by optimizing updates #1977
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triceo
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Christopher-Chianelli and
Copilot
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Pull request overview
This PR optimizes the performance of precompute operations by eliminating intermediate data structures in the update path. The optimization removes the updateQueue from RecordAndReplayPropagator and delegates deduplication directly to StaticPropagationQueue, which already handles duplicate updates efficiently via tuple state checking.
Key Changes
- Removed the intermediate
updateQueuefield and its associated processing overhead - Modified
update()method to directly propagate updates to the tuple-level queue instead of collecting them at the object level - Simplified
propagateUpdates()to directly delegate to the propagation queue
Reviewed changes
Copilot reviewed 2 out of 2 changed files in this pull request and generated 1 comment.
| File | Description |
|---|---|
RecordAndReplayPropagator.java |
Removed updateQueue field and refactored update handling to bypass object-level queuing and deduplication, directly calling propagationQueue.update() for each tuple |
PropagationQueue.java |
Added Javadoc clarifying that duplicate updates of the same item are deduplicated |
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core/src/main/java/ai/timefold/solver/core/impl/bavet/common/PropagationQueue.java
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…ropagationQueue.java Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
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Christopher-Chianelli
approved these changes
Dec 12, 2025
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In my benchmarks, I see speedups of 20-25 %.
This should significantly improve the viability of
precompute.Looking at the insert/retract logic, we probably over-designed this. The de-duplication is very heavy, for something that barely ever happens. But retracts and inserts also barely ever happen themselves, so this code doesn't show up in profiling at all and therefore I'm not touching it.