https://blog.turboawesome.win/tags/concurrency/ Recent content in Concurrency on Bits, Trades & Systems Hugo en-us Wed, 04 Oct 2023 09:35:00 +0000 https://blog.turboawesome.win/2023/10/gos-race-detector-in-ci-catching-data-races-before-they-catch-you/ Wed, 04 Oct 2023 09:35:00 +0000 https://blog.turboawesome.win/2023/10/gos-race-detector-in-ci-catching-data-races-before-they-catch-you/ Data races are among the hardest bugs to find and reproduce. Go's built-in race detector finds them automatically — if you run it. Here's how to integrate it into CI effectively and what to do when it fires. https://blog.turboawesome.win/2020/08/gos-scheduler-gomaxprocs-work-stealing-and-why-it-matters/ Wed, 12 Aug 2020 10:27:00 +0000 https://blog.turboawesome.win/2020/08/gos-scheduler-gomaxprocs-work-stealing-and-why-it-matters/ Go's M:N scheduler is one of the reasons goroutines are cheap enough to use liberally. Understanding how it works changes how you reason about concurrency bugs and performance anomalies. https://blog.turboawesome.win/2020/07/building-a-high-throughput-event-pipeline-in-go-without-losing-your-mind/ Wed, 01 Jul 2020 14:30:00 +0000 https://blog.turboawesome.win/2020/07/building-a-high-throughput-event-pipeline-in-go-without-losing-your-mind/ Channels are Go's concurrency primitive, but a naive pipeline of goroutines and channels won't survive high throughput. The patterns that scale: batching, backpressure, worker pools, and knowing when to abandon channels entirely. https://blog.turboawesome.win/2020/02/context-propagation-in-go-deadlines-cancellation-and-tracing/ Wed, 19 Feb 2020 11:14:00 +0000 https://blog.turboawesome.win/2020/02/context-propagation-in-go-deadlines-cancellation-and-tracing/ Go's context package is one of the most important idioms in the language. Used well it enables clean cancellation and distributed tracing. Used poorly it becomes a magic bag of values that creates invisible dependencies. https://blog.turboawesome.win/2019/08/channels-vs-mutexes-when-to-use-which-in-go/ Wed, 07 Aug 2019 10:22:00 +0000 https://blog.turboawesome.win/2019/08/channels-vs-mutexes-when-to-use-which-in-go/ Go's concurrency slogan is 'share memory by communicating.' It's good advice but incomplete. Some problems are better solved with a mutex than a channel. A decision framework with concrete examples. https://blog.turboawesome.win/2019/04/goroutines-are-not-threads-the-mental-model-shift/ Mon, 22 Apr 2019 13:07:00 +0000 https://blog.turboawesome.win/2019/04/goroutines-are-not-threads-the-mental-model-shift/ Coming from Java, I initially modelled goroutines as lightweight threads. That model is mostly right but subtly wrong in ways that matter — understanding the Go scheduler changes how you think about goroutine lifecycle and blocking. https://blog.turboawesome.win/2019/02/go-for-the-seasoned-java-developer-what-feels-familiar-what-doesnt/ Wed, 20 Feb 2019 11:33:00 +0000 https://blog.turboawesome.win/2019/02/go-for-the-seasoned-java-developer-what-feels-familiar-what-doesnt/ After seven years of Java, switching to Go as a primary language. The frictions, the surprises, and what I'd tell my past self. https://blog.turboawesome.win/2018/05/project-loom-preview-virtual-threads-and-what-they-mean-for-server-code/ Thu, 24 May 2018 09:38:00 +0000 https://blog.turboawesome.win/2018/05/project-loom-preview-virtual-threads-and-what-they-mean-for-server-code/ Project Loom's virtual threads promise to fix Java's thread-per-request scalability problem by making millions of lightweight threads practical. Here's what the early design looked like and why it matters for server-side Java. https://blog.turboawesome.win/2017/08/persistent-data-structures-are-not-just-for-functional-purists/ Wed, 16 Aug 2017 13:07:00 +0000 https://blog.turboawesome.win/2017/08/persistent-data-structures-are-not-just-for-functional-purists/ Clojure's persistent data structures — immutable, structurally shared — seemed academic until we used them in a concurrent risk system. The concurrency model they enable is genuinely simpler than the lock-based alternative. https://blog.turboawesome.win/2016/11/threading-models-in-java-which-one-does-your-system-actually-need/ Wed, 09 Nov 2016 10:22:00 +0000 https://blog.turboawesome.win/2016/11/threading-models-in-java-which-one-does-your-system-actually-need/ Thread-per-request, event-loop, work-stealing, single-threaded with message passing — Java supports all of them, but each optimises for different things. Choosing the wrong model means fighting the runtime rather than working with it. https://blog.turboawesome.win/2015/08/scala-akka-actors-for-trading-workflows-promises-and-pitfalls/ Wed, 19 Aug 2015 11:28:00 +0000 https://blog.turboawesome.win/2015/08/scala-akka-actors-for-trading-workflows-promises-and-pitfalls/ We evaluated Akka actors for a workflow orchestration layer in the trading system. The actor model's isolation and message-passing properties were genuinely useful — and its failure modes were genuinely painful. https://blog.turboawesome.win/2015/03/building-a-trade-blotter-that-doesnt-lie-under-load/ Wed, 04 Mar 2015 10:12:00 +0000 https://blog.turboawesome.win/2015/03/building-a-trade-blotter-that-doesnt-lie-under-load/ A trade blotter shows traders their current positions and recent executions. When the system is under load, naive implementations show stale, inconsistent, or missing data. Here's the design that kept our blotter honest. https://blog.turboawesome.win/2014/05/busy-spinning-vs-blocking-thread-strategies-for-ultra-low-latency/ Wed, 14 May 2014 10:31:00 +0000 https://blog.turboawesome.win/2014/05/busy-spinning-vs-blocking-thread-strategies-for-ultra-low-latency/ Blocking a thread hands control to the OS scheduler and costs you microseconds on wake-up. Busy spinning wastes a CPU core. The right choice depends on your latency target and hardware budget. https://blog.turboawesome.win/2013/05/comparing-arrayblockingqueue-to-the-disruptor-numbers-dont-lie/ Wed, 22 May 2013 13:39:00 +0000 https://blog.turboawesome.win/2013/05/comparing-arrayblockingqueue-to-the-disruptor-numbers-dont-lie/ A direct benchmark comparison between Java's ArrayBlockingQueue and the LMAX Disruptor at various producer/consumer configurations. What the numbers show and why. https://blog.turboawesome.win/2013/02/the-lmax-disruptor-how-a-ring-buffer-changed-my-mental-model-of-queues/ Thu, 28 Feb 2013 09:55:00 +0000 https://blog.turboawesome.win/2013/02/the-lmax-disruptor-how-a-ring-buffer-changed-my-mental-model-of-queues/ The Disruptor pattern replaced our internal queue infrastructure and cut p99 latency in half. Here's the mental model shift required to understand why it works. https://blog.turboawesome.win/2013/01/introduction-to-lock-free-programming-in-java/ Thu, 17 Jan 2013 10:55:00 +0000 https://blog.turboawesome.win/2013/01/introduction-to-lock-free-programming-in-java/ Lock-free programming replaces mutex-based synchronisation with atomic CPU instructions. The performance gains are real; the correctness requirements are unforgiving. https://blog.turboawesome.win/2012/05/building-a-price-feed-aggregator-in-java-first-attempt/ Wed, 09 May 2012 14:22:00 +0000 https://blog.turboawesome.win/2012/05/building-a-price-feed-aggregator-in-java-first-attempt/ My first serious Java performance project: aggregating price feeds from multiple venues into a single best-bid-offer view. What I built, what was wrong with it, and what I learned rewriting it.