Optimize queue performance: eliminate defer accumulation, improve sorting complexity, cache keys

[Copilot]

Multiple performance bottlenecks identified in queue processing: defer accumulation in loops, O(n log n) sorts on every job addition, repeated string operations for key generation, and a race condition in concurrent slice access.

Changes

Memory and Concurrency

• Moved context cancellation outside loops in Run() and Retry() - defer cancel() inside loops accumulates deferred functions indefinitely
• Added mutex protection for finishedJob slice accessed by concurrent goroutines in Retry()

Sorting Optimization

• AddJob: O(n log n) → O(log n) - binary search for insertion point instead of full sort

  // Before: sort.SliceStable(q.jobs, ...) on every add
  // After: sort.Search() to find position, insert directly
  insertIdx := sort.Search(len(q.jobs), func(i int) bool {
      return q.jobs[i].Priority < job.Priority
  })

• BulkAddJob: O(n log n) → O(n+m) - merge sorted slices instead of re-sorting everything

  // Sort new jobs once, merge with existing sorted slice
  q.jobs = mergeSortedJobs(q.jobs, newJobs)

Key Caching

• Pre-compute queue key in constructor - eliminates repeated strings.ToLower() and concatenation on every access

  type Queue struct {
      // ...
      cachedKey string // computed once at initialization
  }

Impact

High-frequency operations (job additions, key lookups) are now logarithmic or constant time instead of linearithmic. Eliminates memory accumulation issue in long-running queues.

Warning

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• vuln.go.dev
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Original prompt

Identify and suggest improvements to slow or inefficient code

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