260 lines
7.3 KiB
Markdown
260 lines
7.3 KiB
Markdown
# Reactive Patterns
|
|
|
|
Real-world patterns for building production reactive pipelines with samber/ro.
|
|
|
|
## Pattern 1: Remote Call with Retry and Timeout
|
|
|
|
Wrap a remote call (HTTP, gRPC, database) with automatic retry, exponential backoff, timeout, and fallback.
|
|
|
|
```go
|
|
result := ro.Pipe3(
|
|
fetchUser(userID), // ro.Observable[User] — wraps your remote call
|
|
ro.Timeout[User](5*time.Second),
|
|
ro.RetryWithConfig[User](ro.RetryConfig{
|
|
Max: 3,
|
|
Delay: 500 * time.Millisecond,
|
|
BackoffMultiplier: 2.0,
|
|
MaxDelay: 5 * time.Second,
|
|
}),
|
|
ro.Catch[User](func(err error) ro.Observable[User] {
|
|
log.Printf("remote call failed after retries: %v, using cache", err)
|
|
return getCachedUser(userID)
|
|
}),
|
|
)
|
|
|
|
user, err := ro.Collect(result)
|
|
```
|
|
|
|
**Why ro over plain calls:** declarative retry + timeout + fallback in 10 lines vs manual for-loops with sleep, context, and error tracking.
|
|
|
|
## Pattern 2: Continuous Event Stream (Hot Observable)
|
|
|
|
Share a single long-lived connection (WebSocket, SSE, message queue) across multiple consumers.
|
|
|
|
```go
|
|
// Cold observable wrapping any event stream source
|
|
eventStream := ro.NewObservable[TickerEvent](func(ctx context.Context, obs ro.Observer[TickerEvent]) error {
|
|
// connect to your stream source (WebSocket, NATS, Kafka, etc.)
|
|
for {
|
|
event, err := streamSource.Read(ctx)
|
|
if err != nil {
|
|
return err
|
|
}
|
|
obs.Next(event)
|
|
}
|
|
})
|
|
|
|
// Share: one connection, multiple consumers
|
|
shared := ro.Pipe1(eventStream, ro.Share[TickerEvent]())
|
|
|
|
// Consumer 1: update UI
|
|
shared.Subscribe(ro.OnNext(func(e TickerEvent) {
|
|
updateDashboard(e)
|
|
}))
|
|
|
|
// Consumer 2: record metrics
|
|
shared.Subscribe(ro.OnNext(func(e TickerEvent) {
|
|
metrics.RecordTick(e.Symbol, e.Price)
|
|
}))
|
|
|
|
// Consumer 3: alert on threshold
|
|
ro.Pipe1(shared, ro.Filter(func(e TickerEvent) bool {
|
|
return e.Price > alertThreshold
|
|
})).Subscribe(ro.OnNext(sendAlert))
|
|
```
|
|
|
|
The `rohttp` plugin provides WebSocket and HTTP streaming observables (see [Plugin Ecosystem](./plugin-ecosystem.md)).
|
|
|
|
## Pattern 3: Fan-In from Multiple Sources
|
|
|
|
Merge events from multiple independent sources, batch, and process.
|
|
|
|
```go
|
|
combined := ro.Pipe2(
|
|
ro.Merge(
|
|
apiStream,
|
|
pushStream,
|
|
cronScheduleStream,
|
|
),
|
|
ro.Distinct[Event](),
|
|
ro.BufferWithTimeOrCount[Event](100, 5*time.Second),
|
|
ro.Map(func(batch []Event) ProcessResult {
|
|
return processBatch(batch)
|
|
}),
|
|
)
|
|
```
|
|
|
|
**When to use Merge vs Concat vs Zip:**
|
|
|
|
| Operator | Behavior | Use when |
|
|
| --- | --- | --- |
|
|
| `Merge` | Interleave: emit from any source as it arrives | Independent streams, order doesn't matter |
|
|
| `Concat` | Sequential: finish first source, then start second | Ordered processing, fallback chains |
|
|
| `Zip` | Pair: wait for one value from each source | Correlated data (user + settings, request + response) |
|
|
| `CombineLatest` | Latest: re-emit combined whenever any source changes | Dependent state (price \* quantity, config + data) |
|
|
|
|
## Pattern 4: Dependent Data Combination
|
|
|
|
Combine data from multiple async sources that depend on each other.
|
|
|
|
```go
|
|
// Fetch user and their orders in parallel, combine
|
|
profile := ro.Pipe1(
|
|
ro.CombineLatest2(
|
|
fetchUser(userID),
|
|
fetchOrders(userID),
|
|
),
|
|
ro.Map(func(pair lo.Tuple2[User, []Order]) UserProfile {
|
|
return UserProfile{
|
|
User: pair.A,
|
|
Orders: pair.B,
|
|
}
|
|
}),
|
|
)
|
|
```
|
|
|
|
For independent data where you need exactly one value from each:
|
|
|
|
```go
|
|
// Zip: waits for one value from each, pairs them
|
|
configAndData := ro.Zip2(loadConfig(), loadData())
|
|
```
|
|
|
|
## Pattern 5: Running Aggregation with Scan
|
|
|
|
Maintain running state across stream values — useful for dashboards, analytics, monitoring.
|
|
|
|
```go
|
|
type Stats struct {
|
|
Count int
|
|
Sum float64
|
|
Avg float64
|
|
Max float64
|
|
}
|
|
|
|
statsStream := ro.Pipe2(
|
|
metricsStream,
|
|
ro.Scan(func(acc Stats, v float64) Stats {
|
|
acc.Count++
|
|
acc.Sum += v
|
|
acc.Avg = acc.Sum / float64(acc.Count)
|
|
if v > acc.Max {
|
|
acc.Max = v
|
|
}
|
|
return acc
|
|
}, Stats{}),
|
|
ro.SampleTime[Stats](5*time.Second), // emit stats every 5s
|
|
)
|
|
```
|
|
|
|
**Scan vs Reduce:** `Scan` emits every intermediate state (good for live dashboards). `Reduce` emits only the final accumulated value (good for batch summaries).
|
|
|
|
## Pattern 6: Error Recovery Cascade
|
|
|
|
Layer multiple error recovery strategies.
|
|
|
|
```go
|
|
resilient := ro.Pipe3(
|
|
primaryDataSource,
|
|
// Strategy 1: retry transient failures
|
|
ro.RetryWithConfig[Data](ro.RetryConfig{
|
|
Max: 2,
|
|
Delay: time.Second,
|
|
}),
|
|
// Strategy 2: fall back to secondary source
|
|
ro.Catch[Data](func(err error) ro.Observable[Data] {
|
|
log.Warn("primary failed, trying secondary", "err", err)
|
|
return secondaryDataSource
|
|
}),
|
|
// Strategy 3: return cached/default value
|
|
ro.OnErrorReturn[Data](cachedDefault),
|
|
)
|
|
```
|
|
|
|
**Order matters:** retry first (transient errors), then fallback source (persistent errors), then default value (total failure).
|
|
|
|
## Pattern 7: File System Watcher
|
|
|
|
React to file changes with debouncing.
|
|
|
|
```go
|
|
import rofsnotify "github.com/samber/ro/plugins/fsnotify"
|
|
|
|
watcher := ro.Pipe3(
|
|
rofsnotify.Watch("/etc/app/config/"),
|
|
ro.Filter(func(e fsnotify.Event) bool {
|
|
return e.Op&fsnotify.Write != 0
|
|
}),
|
|
ro.ThrottleTime[fsnotify.Event](2*time.Second), // debounce rapid saves
|
|
ro.Map(func(e fsnotify.Event) Config {
|
|
return reloadConfig(e.Name)
|
|
}),
|
|
)
|
|
|
|
watcher.Subscribe(ro.NewObserver(
|
|
func(cfg Config) { applyConfig(cfg) },
|
|
func(err error) { log.Error("config watch failed", "err", err) },
|
|
func() { log.Info("config watcher stopped") },
|
|
))
|
|
```
|
|
|
|
## Pattern 8: Graceful Shutdown
|
|
|
|
Use context or signal observable to cleanly terminate infinite streams.
|
|
|
|
```go
|
|
import rosignal "github.com/samber/ro/plugins/signal"
|
|
|
|
// Method 1: OS signal
|
|
shutdown := rosignal.Notify(syscall.SIGTERM, syscall.SIGINT)
|
|
|
|
sub := ro.Pipe1(
|
|
workStream,
|
|
ro.TakeUntil[Work, os.Signal](shutdown),
|
|
).Subscribe(ro.NewObserver(
|
|
processWork,
|
|
handleError,
|
|
func() { log.Info("gracefully stopped") },
|
|
))
|
|
|
|
sub.Wait() // blocks until SIGTERM/SIGINT
|
|
|
|
// Method 2: Context cancellation
|
|
ctx, cancel := context.WithCancel(context.Background())
|
|
|
|
sub := ro.Pipe2(
|
|
workStream,
|
|
ro.ContextReset[Work](ctx),
|
|
ro.ThrowOnContextCancel[Work](),
|
|
).Subscribe(worker)
|
|
|
|
// Later: cancel() triggers clean shutdown
|
|
```
|
|
|
|
## Pattern 9: Event-Driven Pipeline with Logging
|
|
|
|
Full production pipeline with observability at each stage.
|
|
|
|
```go
|
|
import roslog "github.com/samber/ro/plugins/observability/slog"
|
|
|
|
pipeline := ro.Pipe5(
|
|
eventSource,
|
|
ro.TapOnSubscribe[Event](func() {
|
|
slog.Info("pipeline started")
|
|
}),
|
|
ro.Filter(func(e Event) bool { return e.Valid() }),
|
|
roslog.TapOnNext[Event](logger, slog.LevelDebug), // log each event
|
|
ro.Map(enrichEvent),
|
|
ro.BufferWithTimeOrCount[EnrichedEvent](50, 10*time.Second),
|
|
ro.MapErr(func(batch []EnrichedEvent) (Result, error) {
|
|
return persistBatch(batch)
|
|
}),
|
|
ro.TapOnError[Result](func(err error) {
|
|
slog.Error("pipeline error", "err", err)
|
|
metrics.IncrCounter("pipeline.errors", 1)
|
|
}),
|
|
ro.RetryWithConfig[Result](ro.RetryConfig{Max: 3, Delay: time.Second}),
|
|
)
|
|
```
|