API Performance
API Performance:
โ Caching The idea of caching is simple.
Store frequently accessed data in a cache so that you can access it faster when needed.
If thereโs a cache miss, fetch the data from the database.
Itโs quite effective actually but cache invalidation and deciding on the caching strategy can be challenging.
โ Scale-out with Load Balancing If one server instance isnโt enough, you can think of scaling your API to multiple instances.
So - whereโs the catch?
You need to find a way to distribute requests between these multiple instances.
Enter Load Balancer.
It not only helps with performance but also makes your application more reliable.
However, load balancers work best when your application is stateless and easy to scale horizontally.
โ Async Processing Sometimes, you canโt solve multiple problems together.
The best way is to park them for later.
With async processing, you can let the clients know that their requests are registered and under process.
Then, you process the requests one by one and communicate the results to the client later on.
This allows your application server to take a breather and give its best performance.
But of course, async processing may not be possible for every requirement.
โ Pagination If your API returns a large number of records, you need to explore Pagination.
Basically, you limit the number of records per request.
This improves the response time of your API for the consumer.
โ Connection Pooling
An API often needs to connect to the database to fetch some data.
Creating a new connection for each request can degrade performance.
Itโs a good idea to use connection pooling to set up a pool of database connections that can be reused across requests.
This is a subtle aspect but in highly concurrent systems, connection pooling can have a dramatic impact on performance.
