golang-samber-ro

name	golang-samber-ro
description	Reactive streams and event-driven programming in Golang using samber/ro — ReactiveX implementation with 150+ type-safe operators, cold/hot observables, 5 subject types (Publish, Behavior, Replay, Async, Unicast), declarative pipelines via Pipe, 40+ plugins (HTTP, cron, fsnotify, JSON, logging), automatic backpressure, error propagation, and Go context integration. Apply when using or adopting samber/ro, when the codebase imports github.com/samber/ro, or when building asynchronous event-driven pipelines, real-time data processing, streams, or reactive architectures in Go. Not for finite slice transforms (-> See golang-samber-lo skill).
user-invocable	true
license	MIT
compatibility	Designed for Claude Code or similar AI coding agents, and for projects using Golang.
metadata	{"author":"samber","version":"1.0.4","openclaw":{"emoji":"👁️","homepage":"https://github.com/samber/cc-skills-golang","requires":{"bins":["go"]},"install":[],"skill-library-version":"0.3.0"}}
allowed-tools	Read Edit Write Glob Grep Bash(go:*) Bash(golangci-lint:*) Bash(git:*) Agent mcp__context7__resolve-library-id mcp__context7__query-docs AskUserQuestion

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:

1. Observable — a data source that emits values over time. Cold by default: each subscriber triggers independent execution from scratch
2. Observer — a consumer with three callbacks: onNext(T), onError(error), onComplete()
3. Operator — a function that transforms an observable into another observable, chained via Pipe
4. 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

1. Always handle all three events — use NewObserver(onNext, onError, onComplete), not just OnNext. Unhandled errors cause silent data loss
2. Use Collect() for synchronous consumption — when the stream is finite and you need []T, Collect blocks until complete and returns the slice + error
3. Prefer typed Pipe functions — Pipe2, Pipe3...Pipe25 catch type mismatches at compile time. Reserve untyped Pipe for dynamic operator chains
4. Bound infinite streams — use Take(n), TakeUntil(signal), Timeout(d), or context cancellation. Unbounded streams leak goroutines
5. Use Tap/Do for observability — log, trace, or meter emissions without altering the stream. Chain TapOnError for error monitoring
6. 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