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Golang Realtime GC in Theory and Practice | Pusher blog
How the Golang concurrent GC achieves low latencies in real-time systems: a visualization of the algorithm and an empirical comparison with other languages. Each day, Pusher sends billions of messages in real-time: source to destination in less than 100ms. How do we achieve this? A key factor is Go’s low-latency garbage collector. Garbage collectors are a bane of real-time systems because they pau
Low latency, large working set, and GHC’s garbage collector: pick two of three | Pusher blog
Low latency, large working set, and GHC’s garbage collector: pick two of three Pusher makes it easy for developers to reliably deliver data at scale. A few features we are working on required a rewrite of our internal message bus, but we knew anything we build has to perform as reliably as the existing platform. Our chosen language for this job was Haskell, compiled with GHC. Haskell, unusually \[
Memory profiling in Haskell | Pusher blog
At Pusher we are currently writing a high performance system in Haskell and blogging about it along the way. In our first blog post we explained some tools and tips that we have used for improving CPU time of our program. We’ve received requests for part two, so here it is. In this post we’re \[…\] At Pusher we are currently writing a high performance system in Haskell and blogging about it along
Top tips and tools for optimising Haskell | Pusher blog
(This blog post discusses CPU time profiling, but not space (memory) profiling. We’ll come back to that in a Part 2.) We use a range of different programming languages at Pusher and are always looking for ways to squeeze every bit of performance from them. Recently, I’ve been on a project to explore what other \[…\] (This blog post discusses CPU time profiling, but not space (memory) profiling. We
Porting the Pusher integration tests to Haskell | Pusher blog
Those of you who use Pusher will know that the APIs we expose are small and straightforward to use. But this hides the complexity of what is going on behind the scenes. The sheer volume of messages (around 5 billion messages a day and approaching 1.25 trillion since we started counting) has necessitated a large, \[…\] Those of you who use Pusher will know that the APIs we expose are small and stra
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Fuzz testing distributed systems with QuickCheck | Pusher blog
Making Elm Lang Realtime with PusherJS | Pusher blog