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> Load the remaining customer funds into RAM, and decrement them on each API. Periodically flush the remaining balance to DB at set intervals (say every hour).

So you only ever have one app server?

Sure, this specific issues could be handled other ways, but in general global state is hard.




If you have more than one app server load the customer funds into Redis and use atomic operations to decrement it. Serving out of RAM is going to be orders of magnitude faster than updating disk regardless of how much you spend on network overhead. If you have so many customers that they blow the RAM limit on a Redis box....well, you won't, because that's billions of customers.

You can get surprisingly far on one app server (or sometimes one app server with 2 mirrors for failover). See eg. the latest TechEmpower benchmarks [1], where common Java and C++ web frameworks can serve 300k+ reqs/sec off of bare-metal hardware. The OP indicated that these API requests are basically straight queries anyway, and only need to write to update the billing information. Reads can run incredibly fast (both because they can usually be served out of cache and because they can be mirrored), so if your only bottleneck is the write, take the write out of the critical path.

In general global state is hard. Don't solve general problems, solve specific ones. Atomic counters have a well-understood and highly performant solution.

[1] https://www.techempower.com/benchmarks/#section=data-r17&hw=...


Using a database like redis isn't quite the same as just "loading it into ram".

Also, I never said that you couldn't use a single app server because of performance; as you said, you can handle quite a bit of traffic on a single box, even with slower languages.




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