Backpressure

← Previous Chapter: Part 4: Database Scaling & Sharding | Series Hub: System Design Masterclass | Next Chapter: Part 6: Distributed Locks, Mutex Invariants & Concurrency in Go → Prerequisite: Read Part 4: Database Scaling, Sharding Strategies & Distributed SQL to understand how databases decouple state before implementing asynchronous event streams. Answer-first: Asynchronous event streaming with Apache Kafka 3.9+ KRaft decouples distributed microservices by eliminating ZooKeeper coordination bottlenecks. In Go, pairing Cooperative Sticky consumer assignors with bounded channel worker pools enforces backpressure, while non-blocking exponential retry topics quarantine poison pill messages, sustaining 500,000 events per second with sub-5ms latency across cloud clusters. ...

Golang Goroutine Pool Patterns: errgroup & Backpressure Answer-first: Unbounded goroutine creation in high-throughput Go services causes severe memory spikes, GC thrashing, and Kubernetes OOM pod terminations under burst traffic. Production systems must implement bounded concurrency using errgroup.WithContext and channel-based worker pools, enforcing strict backpressure queues to process thousands of jobs predictably while guaranteeing clean context cancellation and deterministic memory bounds. Prerequisite: Working knowledge of Go channels, context propagation, synchronization primitives (sync.WaitGroup, sync.Mutex), and Go runtime scheduling mechanics. ...