Implement concurrent operations and batch methods using goroutines#20
Implement concurrent operations and batch methods using goroutines#20
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📋 Version Check: This PR contains changes. Consider creating a new version tag after merging. Semantic Versioning Guide:
To create a release after merging: git tag v0.1.0
git push origin v0.1.0 |
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Pull request overview
This PR implements concurrent batch operations and parallelizes existing set operations (Union, Intersection, PopCount) using goroutines to improve performance on multi-core systems. The implementation adds new AddBatch and ContainsBatch methods that automatically switch between sequential and parallel execution based on data size, and enhances existing operations with parallel execution paths for large filters.
Key Changes:
- Added concurrent batch methods (
AddBatch,ContainsBatch) with automatic sequential/parallel switching based on workload size - Parallelized set operations (Union, Intersection, PopCount) for large filters exceeding
ParallelThreshold(4096 cache lines) - Introduced comprehensive test coverage and performance benchmarks comparing sequential vs parallel implementations
Reviewed changes
Copilot reviewed 3 out of 3 changed files in this pull request and generated 16 comments.
| File | Description |
|---|---|
| bloomfilter.go | Adds batch methods with goroutine-based parallelization, parallelizes Union/Intersection/PopCount operations, introduces ParallelThreshold constant |
| bloomfilter_parallel_test.go | Provides functional tests for batch operations and parallel set operations with both small and large datasets |
| benchmark_comparison_test.go | Adds comparative benchmarks between sequential and parallel implementations to measure performance improvements |
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| func BenchmarkSequentialAdd(b *testing.B) { | ||
| bf := NewCacheOptimizedBloomFilter(1000000, 0.01) | ||
| data := make([][]byte, 10000) | ||
| for i := 0; i < 10000; i++ { | ||
| data[i] = []byte(fmt.Sprintf("bench-%d", rand.Int())) | ||
| } | ||
| b.ResetTimer() | ||
| for i := 0; i < b.N; i++ { | ||
| for _, item := range data { | ||
| bf.Add(item) | ||
| } | ||
| } |
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Same issue as BenchmarkAddBatch: the bloom filter is reused across all benchmark iterations, leading to an increasingly full filter. This makes the benchmark results less accurate and comparable. Consider creating a fresh filter for each iteration.
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📋 Version Check: This PR contains changes. Consider creating a new version tag after merging. Semantic Versioning Guide:
To create a release after merging: git tag v0.1.0
git push origin v0.1.0 |
|
📋 Version Check: This PR contains changes. Consider creating a new version tag after merging. Semantic Versioning Guide:
To create a release after merging: git tag v0.1.0
git push origin v0.1.0 |
|
📋 Version Check: This PR contains changes. Consider creating a new version tag after merging. Semantic Versioning Guide:
To create a release after merging: git tag v0.1.0
git push origin v0.1.0 |
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