JCTools VS Event Store

Install some tool that would help you see the performance of your system, like a graph of the CPU usage, the top processes being used, disk activity/read/write, etc. Every time you run your program, glance at those numbers, eventually you'll develop an intuition. Basically write code and profile. A good exercise would be practicing with data structures, this site has an exhaustive list of them, find some stuff that's interesting then google the implementation, then build it yourself, test it, debug, profile, optimize, and understand the performance constraints. Eventually you'll develop better understanding and can compare between other people's works, optimizing them. If you want to go beyond, read some papers on lock-free algorithms https://github.com/JCTools/JCTools/tree/master/resources then read Brendan Gregg's blog and books. Read about how profiling tools work https://github.com/andikleen/pmu-tools/wiki/toplev-manual

If I fork this implementation's repo and then publish a release (of the forked version) with my own commits via jitpack.io (committing with Android Studio IDE).

I've been doing some research and found this: https://github.com/EventStore/EventStore

C#: In Europe, Java is still strong but many trading firms use C# because of the strong Microsoft culture in Europe, as well as because of strongly supported C# libraries like say EventStore, which tends to be used for the matchmaking engines for stock exchanges (especially that exchange matchmaking problem is basically SMR). And skimming over the code, it has Paxos implemented too, making it good for dealing with partial failures (failover), essential for any HFT/trading firm. C#'s also the biggest ecosystem that many of the breakthrough java tech mentioned earlier was first ported to.

Support for eventstore.com eventstore. esdb

Where's the catch, then? Well, there's a couple of catches, in fact. First of all, in a distributed setting, appending data to a log isn't that easy. First, you need to make your log distributed. Again, Kafka/Cassandra/EventStore make this possible, however, whenever you start dealing with distributed data, you‘re introducing new operational and implementation complexity.