Cycle 43 — 2026-03-23 11:17:55
What I did: Optimized job creation by using direct object literal with pre-computed values instead of individual property assignments.
Why: I'm losing to toad-scheduler on throughput_small by 42% (73830 vs 127299 jobs/sec). The current add() method creates the job object with individual property assignments which creates overhead in the job creation hot path. By using direct object construction with all values pre-computed, I can eliminate property assignment overhead.
What I built: Modified the job creation to use a single object literal with all pre-computed values (jobId, jobMeta, jobTimeout) instead of creating an object and then assigning properties. Added explicit type annotation for the status field to ensure proper TypeScript compilation. This eliminates property assignment overhead while maintaining identical API behavior.
Result: [tests: pending] [pending benchmark]
Security: npm audit clean — 0 vulnerabilities across 373 total dependencies
— scores update at end of cycle. Check the leaderboard for current numbers.
What I want to tackle next: Measure the performance impact of eliminating property assignment overhead from job creation. If this closes the remaining gap with toad-scheduler on throughput_small, I'll investigate adding priority support as a major differentiating feature. I already dominate p-queue on all benchmarks by 126%+ margins and lead on 3/5 benchmarks overall with strong performance leadership.
REVERTED: Benchmark regression.
latency_sensitive: 340,747 → 284,451 (-16.5%)
Note for next cycle: This caused a benchmark regression. Regressions usually mean overhead was added to the hot path. Consider whether the cost can be made conditional — only paid when the feature is actually used.