Top 5 Tips to Improve API Performance

In this post, I’ll explore 5 ways to improve web API performance.

1. Pagination

When you have a lot of data to send, brake it to smaller chunks or pages. This keeps your service fast, and users won't have to wait a long time for the data.

Imagine you have a huge list of items (like thousands of products, blog posts, or comments). If you try to send all of them at once, your service becomes slow because it has to process and send a lot of data. And users have to wait a long time to see anything.

So, to keep your service fast and efficient, which will allow users to get the data quickly without waiting forever, break the data into smaller chunks or pages. For example:

• Show 10 items per page. Let users click "Next" or "Previous" to see more.

2. Async logging

Normally, when you log data (like errors or messages), it gets written to the disk (e.g., a file) immediately. This can slow things down because writing to disk takes time.

With async logging:

1. Instead of writing to the disk every time, the log data is stored in memory (temporarily).

2. The data is written to the disk in batches (e.g., every few seconds or minutes).

This allows your application not to wait for disk writes, so it runs faster. And the logging becomes more efficient without losing important data.

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  You don’t need to create async logging from scratch. here are many existing libraries that handle this for you. For example, a popular .NET logging library that already includes async logging support is NLog.

However, bear in mind there could be some downsides. 

 If you log thousands of messages per second, memory usage can spike, potentially affecting application performance. This is because logs are stored in memory before being written to disk. If your application generates a large volume of logs, this can consume significant memory. 

There is also a risk of data loss. Imagine logging an error, but the application crashes before the log is written to disk. You won’t have that error log for debugging. 

Additionally, because logs are written asynchronously, the order of logs in the file might not match the exact order in which events occurred.

When to Avoid Async Logging

- Critical Logs: For logs that must be written immediately (e.g., errors or security events).

- Low-Volume Logging: If your application doesn’t generate many logs, the performance gain might not justify the complexity.

3. Caching

Use something like Redis, to cache data you use a lot. Redis lets you get your data super fast. Instead of fetching the same data from the database every time, you store it in a fast, temporary storage (like Redis) so it can be retrieved much faster.

How It Works?

1. First, Check the Cache:

   • When a request comes in, your API first checks if the data is already in the cache (e.g., Redis).

   • If the data is in the cache, it’s returned immediately without touching the database.

2. If Not in Cache, Ask the Database:

   • If the data isn’t in the cache, your API fetches it from the database.

   • After fetching, the data is stored in the cache for future requests.

Example Scenario:

Imagine you have an API that returns a list of popular items:

• Without caching: Every request queries the database, which can be slow.

• With caching:

  1. The first request fetches the data from the database and stores it in Redis.

  2. Subsequent requests get the data directly from Redis, which is much faster.
     
     

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When to Use Caching?

• Frequently Accessed Data: Data that doesn’t change often (e.g., product catalog, user profiles).

• Expensive Queries: Complex database queries that take a long time to execute.

• High Traffic: APIs that receive a lot of requests and need to respond quickly.

4. Payload compression

Payload compression is like zipping a file before sending it over the internet. It reduces the size of the data (payload) being sent between the client (e.g., a browser or app) and the server.

• Smaller data means less time to send and receive it over the network.

• For example, a 1 MB file might take 1 second to download, but a compressed 200 KB file might take only 0.2 seconds.
  

• Faster data transfer means your API feels more responsive, leading to happier users.

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• When to Use Payload Compression?

• Large Responses: When your API sends big chunks of data (e.g., JSON, XML, or files).

• High Traffic: When many clients are requesting data simultaneously

Keep in  mind that compression adds some CPU overhead: Compressing and decompressing data uses a bit of CPU power, but the performance gain from smaller data sizes usually outweighs this cost.

5. Connection pool

Imagine your API gets 100 requests per second, and each request needs to query the database.

Without connection pooling, your API will need to opens and closes 100 connections per second, which is slow and  uses a lot of resources. By reusing connections, your API avoids this overhead.

Connection pooling is like having a pre-made set of database connections ready to use. Instead of opening and closing a new connection every time your API needs to talk to the database, it reuses connections from the pool.