Concurrency

← Previous Chapter: Part 5: Asynchronous Messaging & Kafka KRaft | Series Hub: System Design Masterclass | Next Chapter: Part 7: Idempotency Key Architecture & Financial API Design → Prerequisite: Read Part 5: Asynchronous Messaging, Kafka KRaft & Event-Driven Systems to understand event streams before coordinating state across concurrent distributed workers. Answer-first: Distributed mutual exclusion in high-throughput Go microservices requires monotonic fencing tokens verified by the underlying storage engine to prevent race conditions during unexpected network partitions or garbage collection pauses. While Redis Redlock provides high-throughput probabilistic locking, Etcd Raft leases guarantee CP linearizability, sustaining zero double-spend anomalies across mission-critical financial microservices. ...

Golang Goroutine Pool Patterns: errgroup & Backpressure Answer-first: Golang goroutine pool patterns using golang.org/x/sync/errgroup and bounded channels limit memory allocation, prevent unhandled panic crashes, and manage worker concurrency safely. Preventing goroutine leaks in high-concurrency worker pools using errgroup. Writing resilient worker pools that propagate context cancellation to all active goroutines. Every Go engineer eventually writes the same mistake: a loop that launches goroutines unconditionally. In a demo with 10 items, this works beautifully. In production with 50,000 incoming webhook events, it spawns 50,000 goroutines simultaneously, exhausts memory, and triggers the OOM killer. Kubernetes restarts the pod. The on-call engineer gets paged at 3 AM. ...