数据分析

golang-samber-ro

试用

用 samber/ro 在 Go 中搭建声明式的响应式流管道,免去手写 goroutine 与 channel 的样板代码。

它能做什么

本技能围绕 samber/ro(Go 语言的 ReactiveX 实现)介绍响应式流编程:泛型优先、类型安全的 Observable,配合 Pipe2–Pipe25 串联的 150+ 操作符,以及 5 种 Subject(Publish、Behavior、Replay、Async、Unicast)来做热多播。文档覆盖冷热 Observable 的转换(Share、ShareReplay、Connectable)、40+ 个插件(编解码、网络、调度、可观测性、限流等类目),并说明内建的背压、错误传播与 Go context 取消机制。同时给出冷热选型的判断依据、常见踩坑(错误被吞、goroutine 泄漏、类型安全丢失),以及在有限切片场景下改用 samber/lo 的取舍标准。

什么时候用它

  • 把 ticker、fsnotify 文件监听或 WebSocket 接入需要背压与优雅关停的异步管道
  • 用 CombineLatest/Zip 合并多个异步源,做实时数据富化
  • 通过 Subject 或 Share() 实现一对多多订阅者的发布/订阅
  • 借助 cron、fsnotify 等插件把定时任务或文件系统事件接入到 ro 管道中

技能文档

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:

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:

ScenarioToolWhy
Transform a slice (map, filter, reduce)samber/loFinite, synchronous, eager — no stream overhead needed
Simple goroutine fan-out with error handlingerrgroupStandard lib, lightweight, sufficient for bounded concurrency
Infinite event stream (WebSocket, tickers, file watcher)samber/roDeclarative pipeline with backpressure, retry, timeout, combine
Real-time data enrichment from multiple async sourcessamber/roCombineLatest/Zip compose dependent streams without manual select
Pub/sub with multiple consumers sharing one sourcesamber/roHot observables (Share/Subjects) handle multicast natively

Key differences: lo vs ro

Aspectsamber/losamber/ro
DataFinite slicesInfinite streams
ExecutionSynchronous, blockingAsynchronous, non-blocking
EvaluationEager (allocates intermediate slices)Lazy (processes items as they arrive)
TimingImmediateTime-aware (delay, throttle, interval, timeout)
Error modelReturn (T, error) per callError channel propagates through pipeline
Use caseCollection transformsEvent-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 withBehavior
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
SubjectsNatively hot — call .Send(), .Error(), .Complete() directly
SubjectConstructorReplay behavior
PublishSubjectNewPublishSubject[T]()None — late subscribers miss past events
BehaviorSubjectNewBehaviorSubject[T](initial)Replays last value to new subscribers
ReplaySubjectNewReplaySubject[T](bufferSize)Replays last N values
AsyncSubjectNewAsyncSubject[T]()Emits only last value, only on complete
UnicastSubjectNewUnicastSubject[T](bufferSize)Single subscriber only

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

Operator Quick Reference

CategoryKey operatorsPurpose
CreationJust, FromSlice, FromChannel, Range, Interval, Defer, FutureCreate observables from various sources
TransformMap, MapErr, FlatMap, Scan, Reduce, GroupByTransform or accumulate stream values
FilterFilter, Take, TakeLast, Skip, Distinct, Find, First, LastSelectively emit values
CombineMerge, Concat, Zip2Zip6, CombineLatest2CombineLatest5, RaceMerge multiple observables
ErrorCatch, OnErrorReturn, OnErrorResumeNextWith, Retry, RetryWithConfigRecover from errors
TimingDelay, DelayEach, Timeout, ThrottleTime, SampleTime, BufferWithTimeControl emission timing
Side effectTap/Do, TapOnNext, TapOnError, TapOnCompleteObserve without altering stream
TerminalCollect, ToSlice, ToChannel, ToMapConsume 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

MistakeWhy it failsFix
Using ro.OnNext() without error handlerErrors are silently dropped — bugs hide in productionUse ro.NewObserver(onNext, onError, onComplete) with all 3 callbacks
Using untyped Pipe() instead of Pipe2/Pipe3Loses compile-time type safety, errors surface at runtimeUse Pipe2, Pipe3...Pipe25 for typed operator chains
Forgetting .Unsubscribe() on infinite streamsGoroutine leak — the observable runs foreverUse TakeUntil(signal), context cancellation, or explicit Unsubscribe()
Using Share() when cold is sufficientUnnecessary complexity, harder to reason about lifecycleUse hot observables only when multiple consumers need the same stream
Using samber/ro for finite slice transformsStream overhead (goroutines, subscriptions) for a synchronous operationUse samber/lo — it's simpler, faster, and purpose-built for slices
Not propagating context for cancellationStreams ignore shutdown signals, causing resource leaks on terminationChain 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 functionsPipe2, 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:

CategoryPluginsImport path prefix
EncodingJSON, CSV, Base64, Gobplugins/encoding/...
NetworkHTTP, I/O, FSNotifyplugins/http, plugins/io, plugins/fsnotify
SchedulingCron, ICSplugins/cron, plugins/ics
ObservabilityZap, Slog, Zerolog, Logrus, Sentry, Oopsplugins/observability/..., plugins/samber/oops
Rate limitingNative, Ululeplugins/ratelimit/...
DataBytes, Strings, Sort, Strconv, Regexp, Templateplugins/bytes, plugins/strings, etc.
SystemProcess, Signalplugins/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

常见问题

什么时候该用 samber/ro 而不是 samber/lo?
当数据是无限流(ticker、WebSocket、fsnotify)、需要多源组合(CombineLatest/Zip)、一对多多订阅,或需要重试/超时/背压时用 samber/ro。对有限切片做 Map/Filter/Reduce,用 samber/lo 更直接,也省掉流式开销。
冷 Observable 和热 Observable 有什么区别?
冷 Observable(默认)每次订阅都从源头重新独立执行——简单、可预测,适合每个订阅者独立消费。热 Observable(通过 Share、ShareReplay、Connectable 或 Subject)让所有订阅者共享同一次执行,适用于源头昂贵或多个消费者需要看到相同事件的场景。
如何避免错误被静默丢弃和 goroutine 泄漏?
始终给 ro.NewObserver 传入 onNext、onError、onComplete 三个回调,漏掉任何一个都会把错误藏起来;用类型化的 Pipe2–Pipe25 替代 untyped Pipe,让类型不匹配在编译期就暴露;无限流必须用 Take(n)、TakeUntil(signal)、Timeout 或 context 取消来收口,否则订阅会一直跑下去。

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