← Building Concurrent Ai Pipelines In Go

Phase

Concurrency Fundamentals

Goroutines, WaitGroups, races, mutexes, channels, worker pools, context, and graceful shutdown — the foundations every concurrent AI pipeline needs.

9 published · 0 upcoming

  1. Part 1Published

    Part 1 - Why Concurrency Matters: Building the Wrong System First

    Why Sequential AI Pipelines Stop Scaling

  2. Part 2Published

    Part 2 - Goroutines and WaitGroups: Fixing One Bug, Finding Two More

    We add goroutines to the pipeline and hit three problems in a row: a silently discarded goroutine, a loop-capture trap, and a real data race — ending with a working but unsafe concurrent system.

  3. Part 3Published

    Part 3 - Race Conditions and Mutexes: Locking the Right Thing

    Part 2 introduced goroutines and left a data race. Part 3 fixes it with sync.Mutex, then shows what happens when you lock in the wrong place. It also introduces sync.RWMutex for the case that appears in every real AI pipeline: many concurrent reads, rare writes.

  4. Part 4Published

    Part 4 - Deadlocks: When Goroutines Wait Forever

    Deadlocks are the silent failures of concurrent systems — no panic, no stack trace, just a process that stops making progress. We create three intentionally, read the runtime messages Go gives us, and build rules that prevent them.

  5. Part 5Published

    Part 5 - Channels: Removing the Lock Entirely

    We replace the mutex with a channel. No shared memory, no lock, same speed — and a look at why "share memory by communicating" is more than a slogan.

  6. Part 6Published

    Part 6 - Buffered Channels and select

    Unbuffered channels force synchronisation between sender and receiver. Buffered channels decouple them. And select — introduced here in the collector — lets you wait on multiple channels at once and proceed with whichever fires first.

  7. Part 7Published

    Part 7 - Worker Pools: Decoupling Concurrency from Input Size

    One goroutine per article does not scale to production traffic. We build a fixed-size worker pool and measure exactly how worker count trades off against throughput.

  8. Part 8Published

    Part 8 - Context and Timeouts: Every Call Needs a Deadline

    Every external call in a concurrent AI pipeline needs a deadline. We add context.WithTimeout to the worker pool and see, with real numbers, what happens when we do — and when we forget.

  9. Part 9Published

    Part 9 - Cancellation and Graceful Shutdown: Stopping Safely

    A pipeline that can not be stopped cleanly is not production-ready. We add OS signal handling, propagate cancellation through the worker pool, and build a ShutdownReport that accounts for every article — even under an abrupt stop.