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: 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. ...