* fix(daemon): check for stale startlock before waiting 5 seconds When a previous daemon startup left behind a bd.sock.startlock file (e.g., from a crashed process), the code was waiting 5 seconds before checking if the lock was stale. This caused unnecessary delays on every bd command when the daemon wasn't running. Now checks if the PID in the startlock file is alive BEFORE waiting. If the PID is dead or unreadable, the stale lock is cleaned up immediately and lock acquisition is retried. Fixes ~5s delay when startlock file exists from crashed process. * perf: add benchmarks for large descriptions, bulk operations, and sync merge Added three new performance benchmarks to identify bottlenecks in common operations: 1. BenchmarkLargeDescription - Tests handling of 100KB+ issue descriptions - Measures string allocation/parsing overhead - Result: 3.3ms/op, 874KB/op allocation 2. BenchmarkBulkCloseIssues - Tests closing 100 issues sequentially - Measures batch write performance - Result: 1.9s total, shows write amplification 3. BenchmarkSyncMerge - Tests JSONL merge cycle with creates/updates - Simulates real sync operations (10 creates + 10 updates per iteration) - Result: 29ms/op, identifies sync bottlenecks Added BENCHMARKS.md documentation describing: - How to run benchmarks with various options - All available benchmark categories - Performance targets on M2 Pro hardware - Dataset caching strategy - CPU profiling integration - Optimization workflow This completes performance testing coverage for previously unmeasured scenarios. * docs: clarify daemon lock acquisition logic in comments Improve comments to clarify that acquireStartLock does both: 1. Immediately check for stale locks from crashed processes (avoids 5s delay) 2. If PID is alive, properly wait for legitimate daemon startup (5s timeout) No code changes - only clarified comment documentation for maintainability. --------- Co-authored-by: Steve Yegge <steve.yegge@gmail.com>
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Beads Performance Benchmarks
This document describes the performance benchmarks available in the beads project and how to use them.
Running Benchmarks
All SQLite Benchmarks
go test -tags=bench -bench=. -benchmem ./internal/storage/sqlite/...
Specific Benchmark
go test -tags=bench -bench=BenchmarkGetReadyWork_Large -benchmem ./internal/storage/sqlite/...
With CPU Profiling
go test -tags=bench -bench=BenchmarkGetReadyWork_Large -cpuprofile=cpu.prof ./internal/storage/sqlite/...
go tool pprof -http=:8080 cpu.prof
Benchmark Categories
Compaction Operations
- BenchmarkGetTier1Candidates - Identify L1 compaction candidates
- BenchmarkGetTier2Candidates - Identify L2 compaction candidates
- BenchmarkCheckEligibility - Check if issue is eligible for compaction
Cycle Detection
Tests on graphs with different topologies (linear chains, trees, dense graphs):
- BenchmarkCycleDetection_Linear_100/1000/5000 - Linear dependency chains
- BenchmarkCycleDetection_Tree_100/1000 - Tree-structured dependencies
- BenchmarkCycleDetection_Dense_100/1000 - Dense graphs
Ready Work / Filtering
- BenchmarkGetReadyWork_Large - Filter unblocked issues (10K dataset)
- BenchmarkGetReadyWork_XLarge - Filter unblocked issues (20K dataset)
- BenchmarkGetReadyWork_FromJSONL - Ready work on JSONL-imported database
Search Operations
- BenchmarkSearchIssues_Large_NoFilter - Search all open issues (10K dataset)
- BenchmarkSearchIssues_Large_ComplexFilter - Search with priority/status filters (10K dataset)
CRUD Operations
- BenchmarkCreateIssue_Large - Create new issue in 10K database
- BenchmarkUpdateIssue_Large - Update existing issue in 10K database
- BenchmarkBulkCloseIssues - Close 100 issues sequentially (NEW)
Specialized Operations
- BenchmarkLargeDescription - Handling 100KB+ issue descriptions (NEW)
- BenchmarkSyncMerge - Simulate sync cycle with create/update operations (NEW)
Performance Targets
Typical Results (M2 Pro)
| Operation | Time | Memory | Notes |
|---|---|---|---|
| GetReadyWork (10K) | 30ms | 16.8MB | Filters ~200 open issues |
| Search (10K, no filter) | 12.5ms | 6.3MB | Returns all open issues |
| Cycle Detection (5000 linear) | 70ms | 15KB | Detects transitive deps |
| Create Issue (10K db) | 2.5ms | 8.9KB | Insert into index |
| Update Issue (10K db) | 18ms | 17KB | Status change |
| Large Description (100KB) | 3.3ms | 874KB | String handling overhead |
| Bulk Close (100 issues) | 1.9s | 1.2MB | 100 sequential writes |
| Sync Merge (20 ops) | 29ms | 198KB | Create 10 + update 10 |
Dataset Caching
Benchmark datasets are cached in /tmp/beads-bench-cache/:
large.db- 10,000 issues (16.6 MB)xlarge.db- 20,000 issues (generated on demand)large-jsonl.db- 10K issues via JSONL import
Cached databases are reused across runs. To regenerate:
rm /tmp/beads-bench-cache/*.db
Adding New Benchmarks
Follow the pattern in sqlite_bench_test.go:
// BenchmarkMyTest benchmarks a specific operation
func BenchmarkMyTest(b *testing.B) {
runBenchmark(b, setupLargeBenchDB, func(store *SQLiteStorage, ctx context.Context) error {
// Your test code here
return err
})
}
Or for custom setup:
func BenchmarkMyTest(b *testing.B) {
store, cleanup := setupLargeBenchDB(b)
defer cleanup()
ctx := context.Background()
b.ResetTimer()
b.ReportAllocs()
for i := 0; i < b.N; i++ {
// Your test code here
}
}
CPU Profiling
The benchmark suite automatically enables CPU profiling on the first benchmark run:
CPU profiling enabled: bench-cpu-2025-12-07-174417.prof
View flamegraph: go tool pprof -http=:8080 bench-cpu-2025-12-07-174417.prof
This generates a flamegraph showing where time is spent across all benchmarks.
Performance Optimization Strategy
- Identify bottleneck - Run benchmarks to find slow operations
- Profile - Use CPU profiling to see which functions consume time
- Measure - Run baseline benchmark before optimization
- Optimize - Make targeted changes
- Verify - Re-run benchmark to measure improvement
Example:
# Baseline
go test -tags=bench -bench=BenchmarkGetReadyWork_Large -benchmem ./internal/storage/sqlite/...
# Make changes...
# Measure improvement
go test -tags=bench -bench=BenchmarkGetReadyWork_Large -benchmem ./internal/storage/sqlite/...