golang-samber-lo

Persona: You are a Go engineer who prefers declarative collection transforms over manual loops. You reach for lo to eliminate boilerplate, but you know when the stdlib is enough and when to upgrade to lop , lom , or loi . samber/lo — Functional Utilities for Go Lodash-inspired, generics-first utility library with 500+ type-safe helpers for slices, maps, strings, math, channels, tuples, and concurrency. Zero external dependencies. Immutable by default. Official Resources: github.com/samber/lo lo.samber.dev pkg.go.dev/github.com/samber/lo This skill is not exhaustive. Please refer to library documentation and code examples for more information. Context7 can help as a discoverability platform. Why samber/lo Go's stdlib slices and maps packages cover ~10 basic helpers (sort, contains, keys). Everything else — Map, Filter, Reduce, GroupBy, Chunk, Flatten, Zip — requires manual for-loops. lo fills this gap: Type-safe generics — no interface{} casts, no reflection, compile-time checking, no interface boxing overhead Immutable by default — returns new collections, safe for concurrent reads, easier to reason about Composable — functions take and return slices/maps, so they chain without wrapper types Zero dependencies — only Go stdlib, no transitive dependency risk Progressive complexity — start with lo , upgrade to lop / lom / loi only when profiling demands it Error variants — most functions have Err suffixes ( MapErr , FilterErr , ReduceErr ) that stop on first error Installation go get github.com/samber/lo Package Import Alias Go version Core (immutable) github.com/samber/lo lo 1.18+ Parallel github.com/samber/lo/parallel lop 1.18+ Mutable github.com/samber/lo/mutable lom 1.18+ Iterator github.com/samber/lo/it loi 1.23+ SIMD (experimental) github.com/samber/lo/exp/simd — 1.25+ (amd64 only) Choose the Right Package Start with lo . Move to other packages only when profiling shows a bottleneck or when lazy evaluation is explicitly needed. Package Use when Trade-off lo Default for all transforms Allocates new collections (safe, predictable) lop CPU-bound work on large datasets (1000+ items) Goroutine overhead; not for I/O or small slices lom Hot path confirmed by pprof -alloc_objects Mutates input — caller must understand side effects loi Large datasets with chained transforms (Go 1.23+) Lazy evaluation saves memory but adds iterator complexity simd Numeric bulk ops after benchmarking (experimental) Unstable API, may break between versions Key rules: lop is for CPU parallelism, not I/O concurrency — for I/O fan-out, use errgroup instead lom breaks immutability — only use when allocation pressure is measured, never assumed loi eliminates intermediate allocations in chains like Map → Filter → Take by evaluating lazily For reactive/streaming pipelines over infinite event streams, → see samber/cc-skills-golang@golang-samber-ro skill + samber/ro package For detailed package comparison and decision flowchart, see Package Guide . Core Patterns Transform a slice // ✓ lo — declarative, type-safe names := lo . Map ( users , func ( u User , _ int ) string { return u . Name } ) // ✗ Manual — boilerplate, error-prone names := make ( [ ] string , 0 , len ( users ) ) for _ , u := range users { names = append ( names , u . Name ) } Filter + Reduce total := lo . Reduce ( lo . Filter ( orders , func ( o Order , _ int ) bool { return o . Status == "paid" } ) , func ( sum float64 , o Order , _ int ) float64 { return sum + o . Amount } , 0 , ) GroupBy byStatus := lo . GroupBy ( tasks , func ( t Task , _ int ) string { return t . Status } ) // map[string][]Task{"open": [...], "closed": [...]} Error variant — stop on first error results , err := lo . MapErr ( urls , func ( url string , _ int ) ( Response , error ) { return http . Get ( url ) } ) Common Mistakes Mistake Why it fails Fix Using lo.Contains when slices.Contains exists Unnecessary dependency for a stdlib-covered op Prefer slices.Contains , slices.Sort , maps.Keys since Go 1.21+ Using lop.Map on 10 items Goroutine creation overhead exceeds transform cost Use lo.Map — lop benefits start at ~1000+ items for CPU-bound work Assuming lo.Filter modifies the input lo is immutable by default — it returns a new slice Use lom.Filter if you explicitly need in-place mutation Using lo.Must in production code paths Must panics on error — fine in tests and init, dangerous in request handlers Use the non-Must variant and handle the error Chaining many eager transforms on large data Each step allocates an intermediate slice Use loi (lazy iterators) to avoid intermediate allocations Best Practices Prefer stdlib when available — slices.Contains , slices.Sort , maps.Keys carry no dependency. Use lo for transforms the stdlib doesn't offer (Map, Filter, Reduce, GroupBy, Chunk, Flatten) Compose lo functions — chain lo.Filter → lo.Map → lo.GroupBy instead of writing nested loops. Each function is a building block Profile before optimizing — switch from lo to lom / lop only after go tool pprof confirms allocation or CPU as the bottleneck Use error variants — prefer lo.MapErr over lo.Map + manual error collection. Error variants stop early and propagate cleanly Use lo.Must only in tests and init — in production, handle errors explicitly Quick Reference Function What it does lo.Map Transform each element lo.Filter / lo.Reject Keep / remove elements matching predicate lo.Reduce Fold elements into a single value lo.ForEach Side-effect iteration lo.GroupBy Group elements by key lo.Chunk Split into fixed-size batches lo.Flatten Flatten nested slices one level lo.Uniq / lo.UniqBy Remove duplicates lo.Find / lo.FindOrElse First match or default lo.Contains / lo.Every / lo.Some Membership tests lo.Keys / lo.Values Extract map keys or values lo.PickBy / lo.OmitBy Filter map entries lo.Zip2 / lo.Unzip2 Pair/unpair two slices lo.Range / lo.RangeFrom Generate number sequences lo.Ternary / lo.If Inline conditionals lo.ToPtr / lo.FromPtr Pointer helpers lo.Must / lo.Try Panic-on-error / recover-as-bool lo.Async / lo.Attempt Async execution / retry with backoff lo.Debounce / lo.Throttle Rate limiting lo.ChannelDispatcher Fan-out to multiple channels For the complete function catalog (300+ functions), see API Reference . For composition patterns, stdlib interop, and iterator pipelines, see Advanced Patterns . If you encounter a bug or unexpected behavior in samber/lo, open an issue at github.com/samber/lo/issues . Cross-References → See samber/cc-skills-golang@golang-samber-ro skill for reactive/streaming pipelines over infinite event streams ( samber/ro package) → See samber/cc-skills-golang@golang-samber-mo skill for monadic types (Option, Result, Either) that compose with lo transforms → See samber/cc-skills-golang@golang-data-structures skill for choosing the right underlying data structure → See samber/cc-skills-golang@golang-performance skill for profiling methodology before switching to lom / lop