I get 700 million requests per second over 12 threads due to the sharding of money over accounts. Here I prioritise throughput of transacitons per second over balance checks (a consistent view of the data).
I am also experimenting with left-right concurrency control which trades memory usage for performance, it basically keeps two copies of data, one that is currently being read and written to and another inactive copy that is not active. You switch the data structures around periodically to "make changes visible" to that thread.
You can get embarassingly parallel performance if you split your data by thread and aggregate periodically.
If you need a consistent view of your entire set of data, that is a slow path with sharding.
In my experiments with multithreaded software I simulate a bank where many bankaccounts are randomly withdrawn from and deposited to. https://github.com/samsquire/multiversion-concurrency-contro...
I get 700 million requests per second over 12 threads due to the sharding of money over accounts. Here I prioritise throughput of transacitons per second over balance checks (a consistent view of the data).I am also experimenting with left-right concurrency control which trades memory usage for performance, it basically keeps two copies of data, one that is currently being read and written to and another inactive copy that is not active. You switch the data structures around periodically to "make changes visible" to that thread.