Adds performance benchmarks for core collection functions to enable
tracking of performance regressions and optimization opportunities.
Benchmarks added:
- Map: 1000 element transformation
- Reduce: 1000 element sum
- Partition: 1000 element split
- Unique/UniqueInPlace: Comparison with many duplicates
- ParallelMap: Multiple worker counts (1, 2, 4, 8)
- MapVsParallelMap: Direct comparison (10k elements)
Key findings from benchmarks:
- Map: 1363 ns/op, 1 alloc (excellent)
- Reduce: 335 ns/op, 0 allocs (excellent)
- Partition: 3411 ns/op, 2 allocs (good - both slices)
- ParallelMap overhead: ~240x slower for simple operations
- ParallelMap is best for CPU-intensive operations (>1ms per element)
Use cases clarified:
- Regular Map for simple/fast operations
- ParallelMap for expensive operations with 100+ elements
- Optimal workers: 1-4 for most workloads
All tests pass ✅
Coverage maintained ✅🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Add tests to verify default worker behavior in ParallelMap and
ParallelFilter.
Add internal test to cover unexported Result marker methods for
coverage.
- Add `Chunk` to split slices into groups of size n.
- Add `ContainsBy` for predicate-based containment checks.
- Add `UniqueBy` to deduplicate slices by key selector.
- Add `ParallelMap` for concurrent mapping with context and error
handling.
- Add `maps.Keys` and `maps.Values` helpers for extracting map
keys/values.
- Update README and docs for new features.
- Refactor `Contains` to use `slices.Contains`.
