← 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

    From Sequential Scripts to Concurrent AI Pipelines in Go — Part 1

    Why Sequential AI Pipelines Stop Scaling

  2. Part 2Published

    From Sequential Scripts to Concurrent AI Pipelines in Go — Part 2

    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

    From Sequential Scripts to Concurrent AI Pipelines in Go — Part 3

    We fix the data race from Part 2 with a mutex — then prove, with real numbers, that locking in the wrong place quietly destroys every gain concurrency gave us.

  4. Part 4Published

    From Sequential Scripts to Concurrent AI Pipelines in Go — Part 4

    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

    From Sequential Scripts to Concurrent AI Pipelines in Go — Part 5

    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

    From Sequential Scripts to Concurrent AI Pipelines in Go — Part 6

    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

    From Sequential Scripts to Concurrent AI Pipelines in Go — Part 7

    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

    From Sequential Scripts to Concurrent AI Pipelines in Go — Part 8

    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

    From Sequential Scripts to Concurrent AI Pipelines in Go — Part 9

    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.