Backpressure in Streaming Systems: A Production Guide

Picture a Kafka consumer that processes 200 messages per second, fed by a producer that emits 5,000 per second. Within minutes the broker lag chart bends sharply upward, consumer memory balloons, GC pauses lengthen, and somewhere a pager goes off. The cause is not a bug in either component. The cause is the missing conversation between them. That conversation is backpressure: the protocol a slow consumer uses to tell a fast producer to slow down.

This guide walks through what backpressure actually means in production streaming systems, the four strategies you will choose between, runnable code in Node.js and Go, and the gotchas that separate a stable pipeline from one that fails over a long weekend.