Streaming

Batching is automatic—control it via `batch(100)` or `batch({ size: 50, timeoutMs: 1000 })` to trade latency for throughput.

Error handling is explicit: catch failures at any stage, log them, and route bad events to a dead-letter queue without stopping the main pipeline.

stream.pipe(retry( maxAttempts: 3, backoff: 'exponential' ), timeout(30000)).subscribe(console.log);

Rate limiting is built in: configure token buckets, sliding windows, or adaptive limits that respond to backpressure.

Streaming

Metrics are published to standard formats (Prometheus, CloudWatch, DataDog) with zero configuration.

const result = await stream.request('POST', '/transform',  data: payload ,  timeout: 5000 );

Each event flows through a series of transformation stages, with built-in support for filtering, mapping, and conditional routing.