golang-samber-ro

golang-samber-ro

Persona: You are a Go engineer who reaches for reactive streams when data flows asynchronously or infinitely. You use samber/ro to build declarative pipelines instead of manual goroutine/channel wiring, but you know when a simple slice + samber/lo is enough.

Thinking mode: Use ultrathink when designing advanced reactive pipelines or choosing between cold/hot observables, subjects, and combining operators. Wrong architecture leads to resource leaks or missed events.

samber/ro — Reactive Streams for Go

Go implementation of ReactiveX. Generics-first, type-safe, composable pipelines for asynchronous data streams with automatic backpressure, error propagation, context integration, and resource cleanup. 150+ operators, 5 subject types, 40+ plugins.

Official Resources:

• github.com/samber/ro

• ro.samber.dev

• pkg.go.dev/github.com/samber/ro

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/ro (Streams vs Slices)

Go channels + goroutines become unwieldy for complex async pipelines: manual channel closures, verbose goroutine lifecycle, error propagation across nested selects, and no composable operators. samber/ro solves this with declarative, chainable stream operators.

When to use which tool:

Scenario Tool Why

Transform a slice (map, filter, reduce) samber/lo Finite, synchronous, eager — no stream overhead needed

Simple goroutine fan-out with error handling errgroup Standard lib, lightweight, sufficient for bounded concurrency

Infinite event stream (WebSocket, tickers, file watcher) samber/ro Declarative pipeline with backpressure, retry, timeout, combine

Real-time data enrichment from multiple async sources samber/ro CombineLatest/Zip compose dependent streams without manual select

Pub/sub with multiple consumers sharing one source samber/ro Hot observables (Share/Subjects) handle multicast natively

Key differences: lo vs ro

Aspect samber/lo samber/ro

Data Finite slices Infinite streams

Execution Synchronous, blocking Asynchronous, non-blocking

Evaluation Eager (allocates intermediate slices) Lazy (processes items as they arrive)

Timing Immediate Time-aware (delay, throttle, interval, timeout)

Error model Return (T, error) per call Error channel propagates through pipeline

Use case Collection transforms Event-driven, real-time, async pipelines

Installation

go get github.com/samber/ro

Core Concepts

Four building blocks:

• Observable — a data source that emits values over time. Cold by default: each subscriber triggers independent execution from scratch

• Observer — a consumer with three callbacks: onNext(T), onError(error), onComplete()

• Operator — a function that transforms an observable into another observable, chained via Pipe

• Subscription — the connection between observable and observer. Call .Wait() to block or .Unsubscribe() to cancel

observable := ro.Pipe2(
    ro.RangeWithInterval(0, 5, 1*time.Second),
    ro.Filter(func(x int) bool { return x%2 == 0 }),
    ro.Map(func(x int) string { return fmt.Sprintf("even-%d", x) }),
)

observable.Subscribe(ro.NewObserver(
    func(s string) { fmt.Println(s) },      // onNext
    func(err error) { log.Println(err) },    // onError
    func() { fmt.Println("Done!") },         // onComplete
))
// Output: "even-0", "even-2", "even-4", "Done!"

// Or collect synchronously:
values, err := ro.Collect(observable)

Cold vs Hot Observables

Cold (default): each .Subscribe() starts a new independent execution. Safe and predictable — use by default.

Hot: multiple subscribers share a single execution. Use when the source is expensive (WebSocket, DB poll) or subscribers must see the same events.

Convert with Behavior

Share() Cold → hot with reference counting. Last unsubscribe tears down

ShareReplay(n) Same as Share + buffers last N values for late subscribers

Connectable() Cold → hot, but waits for explicit .Connect() call

Subjects Natively hot — call .Send(), .Error(), .Complete() directly

Subject Constructor Replay behavior

PublishSubject NewPublishSubject[T]() None — late subscribers miss past events

BehaviorSubject NewBehaviorSubject[T](initial) Replays last value to new subscribers

ReplaySubject NewReplaySubject[T](bufferSize) Replays last N values

AsyncSubject NewAsyncSubject[T]() Emits only last value, only on complete

UnicastSubject NewUnicastSubject[T](bufferSize) Single subscriber only

For subject details and hot observable patterns, see Subjects Guide.

Operator Quick Reference

Category Key operators Purpose

Creation Just, FromSlice, FromChannel, Range, Interval, Defer, Future Create observables from various sources

Transform Map, MapErr, FlatMap, Scan, Reduce, GroupBy Transform or accumulate stream values

Filter Filter, Take, TakeLast, Skip, Distinct, Find, First, Last Selectively emit values

Combine Merge, Concat, Zip2–Zip6, CombineLatest2–CombineLatest5, Race Merge multiple observables

Error Catch, OnErrorReturn, OnErrorResumeNextWith, Retry, RetryWithConfig Recover from errors

Timing Delay, DelayEach, Timeout, ThrottleTime, SampleTime, BufferWithTime Control emission timing

Side effect Tap/Do, TapOnNext, TapOnError, TapOnComplete Observe without altering stream

Terminal Collect, ToSlice, ToChannel, ToMap Consume stream into Go types

Use typed Pipe2, Pipe3 ... Pipe25 for compile-time type safety across operator chains. The untyped Pipe uses any and loses type checking.

For the complete operator catalog (150+ operators with signatures), see Operators Guide.

Common Mistakes

Mistake Why it fails Fix

Using ro.OnNext() without error handler Errors are silently dropped — bugs hide in production Use ro.NewObserver(onNext, onError, onComplete) with all 3 callbacks

Using untyped Pipe() instead of Pipe2/Pipe3 Loses compile-time type safety, errors surface at runtime Use Pipe2, Pipe3...Pipe25 for typed operator chains

Forgetting .Unsubscribe() on infinite streams Goroutine leak — the observable runs forever Use TakeUntil(signal), context cancellation, or explicit Unsubscribe()

Using Share() when cold is sufficient Unnecessary complexity, harder to reason about lifecycle Use hot observables only when multiple consumers need the same stream

Using samber/ro for finite slice transforms Stream overhead (goroutines, subscriptions) for a synchronous operation Use samber/lo — it's simpler, faster, and purpose-built for slices

Not propagating context for cancellation Streams ignore shutdown signals, causing resource leaks on termination Chain ContextWithTimeout or ThrowOnContextCancel in the pipeline

Best Practices

• Always handle all three events — use NewObserver(onNext, onError, onComplete), not just OnNext. Unhandled errors cause silent data loss

• Use Collect() for synchronous consumption — when the stream is finite and you need []T, Collect blocks until complete and returns the slice + error

• Prefer typed Pipe functions — Pipe2, Pipe3...Pipe25 catch type mismatches at compile time. Reserve untyped Pipe for dynamic operator chains

• Bound infinite streams — use Take(n), TakeUntil(signal), Timeout(d), or context cancellation. Unbounded streams leak goroutines

• Use Tap/Do for observability — log, trace, or meter emissions without altering the stream. Chain TapOnError for error monitoring

• Prefer samber/lo for simple transforms — if the data is a finite slice and you need Map/Filter/Reduce, use lo. Reach for ro when data arrives over time, from multiple sources, or needs retry/timeout/backpressure

Plugin Ecosystem

40+ plugins extend ro with domain-specific operators:

Category Plugins Import path prefix

Encoding JSON, CSV, Base64, Gob plugins/encoding/...

Network HTTP, I/O, FSNotify plugins/http, plugins/io, plugins/fsnotify

Scheduling Cron, ICS plugins/cron, plugins/ics

Observability Zap, Slog, Zerolog, Logrus, Sentry, Oops plugins/observability/..., plugins/samber/oops

Rate limiting Native, Ulule plugins/ratelimit/...

Data Bytes, Strings, Sort, Strconv, Regexp, Template plugins/bytes, plugins/strings, etc.

System Process, Signal plugins/proc, plugins/signal

For the full plugin catalog with import paths and usage examples, see Plugin Ecosystem.

For real-world reactive patterns (retry+timeout, WebSocket fan-out, graceful shutdown, stream combination), see Patterns.

If you encounter a bug or unexpected behavior in samber/ro, open an issue at github.com/samber/ro/issues.

Cross-References

• → See samber/cc-skills-golang@golang-samber-lo skill for finite slice transforms (Map, Filter, Reduce, GroupBy) — use lo when data is already in a slice

• → See samber/cc-skills-golang@golang-samber-mo skill for monadic types (Option, Result, Either) that compose with ro pipelines

• → See samber/cc-skills-golang@golang-samber-hot skill for in-memory caching (also available as an ro plugin)

• → See samber/cc-skills-golang@golang-concurrency skill for goroutine/channel patterns when reactive streams are overkill

• → See samber/cc-skills-golang@golang-observability skill for monitoring reactive pipelines in production

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