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1/3 I should, at some point, make a small tutorial about matching engines. You can write a really really simple one in few lines of code. ZMQ-input -> matching (2 red black trees per symbol) -> ZMQ-outputhttps://twitter.com/IvanOnTech/status/1213877073992933376 …
2/3 Then how you make it resilient, super-scalable, micro-second fast, ... it's all about art.
I spent quite sometime in developing @bitfinex 's one, via many iterations and experiments.
Now it's a beautiful beast, capable of incredible performance and powering several products!
3/3 Many exchanges rely on traditional databases to handle matching. This is why during peak market activity they won't perform well and throughput is heavily limitated. Of course it requires to develop custom fault-tolerance, resilience, ... but it's simply the only correct way.
That would be amazing 
Would love to read it be day
A single symbol process can handle up to 350k orders/sec. We're improving towards 1M per process. Also having native sharding support let us scale horizontally in a cluster, giving almost infinite matching power.
Please do. 
Imo optimize for cache locality. I.e. flat array data structure, linear search. The orderbooks aren’t big enough for trees to pay of. But maybe you have measured otherwise. I tend to come back tohttps://github.com/charles-cooper/itch-order-book …
What wire protocol do you prefer for internal event bus - JSON, Apache Arrow, proprietary? Lots of good options but parsing overhead could affect nano second latency!
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