I had a system where the main running cost was MySQL. It turned out that I needed to provision a lot of MySQL capacity because there was one table that had a high rate of selects and updates.
The hot table did not use many MySQL features and could easily be handled by a key-value store with highly tuned data structures. That's a place where a "microservice" which has it's own address space, if not machine, makes it possible to scale parts of the system that need to be scaled without scaling the rest.
Still, based on what you're describing it was MySQL in your case that was the bottleneck. In that case you scale database, either by sharding or creating separate ones for different usecases. It is irrelevant to whether you use microservices or not.
Simple. Most of the database is "large" in terms of data (say 50M rows) but that database gets maybe 10,000 updates a day. The read load is well-controlled with caching.
One table is small in terms of data but involves an interactive service that might generate 50 updates/sec at peak times.
With the "hot" service implemented on top of a key-value store, the database is the ultimate commodity, I have many choices such as in-memory with logging, off-heap storage, distributed key-value stores, etc.
The service is not "in front" of MySQL, but is on the side of it so far as the app is concerned.
I had a system where the main running cost was MySQL. It turned out that I needed to provision a lot of MySQL capacity because there was one table that had a high rate of selects and updates.
The hot table did not use many MySQL features and could easily be handled by a key-value store with highly tuned data structures. That's a place where a "microservice" which has it's own address space, if not machine, makes it possible to scale parts of the system that need to be scaled without scaling the rest.