How to implement rate limiting in your REST APIs

How to implement caching in your REST APIs for better performance

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Caching is one of the most effective tools for performance optimization that is available to backend developers. It allows your server to respond faster and save you money on bandwidth if you’re using a cloud hosting provider. However, there are tradeoffs. Caching is not appropriate for all resources or all apps, so make sure that you understand its implications before implementing it in your REST APIs. This article will walk through the basic concepts of caching and explain how to implement caches in your own applications using Django models and views.

Cache your API responses

Caching is a technique that can improve the performance of your REST APIs. It involves storing responses from your server for a specific amount of time and serving them when someone makes a request for the same resource. This allows you to avoid sending large amounts of data over the network, which in turn reduces bandwidth usage and increases overall efficiency.

Caching isn’t free though; it comes with tradeoffs that need to be considered before deciding whether or not to use it in your application:

  • Caching requires additional memory (RAM) on both sides–your client’s device as well as your server’s machine(s). This may not be ideal if you’re running on bare metal hardware with limited resources available at any given time; however, this shouldn’t be an issue if you’re using cloud infrastructure services like Amazon Web Services because they have plenty spare capacity available 24/7/365! In other words: “You’ve got nothing but net!”

  • Since cached responses are stored locally on each user’s device instead of being sent back over TCP/IP connections between themselfes’ devices – there is no guarantee that those same users won’t make identical requests within some period time frame (e.,g., “I need my favorite song again!) – so there will inevitably be some lost bandwidth opportunities due to overlap between two different requests even though they came from different people within same groupings such as families sharing cars together etc..

Cache resources with unique IDs

You can cache resources with a unique ID. For example, you could create a UUID (Universally Unique Identifier) and use it as an identifier for every resource you want to cache. You could also store the hash of the content or headers of each resource in your cache, so that when someone requests that resource again, you look up its cached copy instead of making another request to your server.

Use cache headers to control caching behavior

The cache-control header sets the conditions for how long a response can be cached. It’s an HTTP directive that uses the max-age parameter to specify how long a response can be cached. The max-age value is given in seconds, so if you want to cache a response for one day (24 hours), you would add “max-age=86400” to your request header.

The s-maxage directive works similarly but only applies to shared proxies and edge servers such as those used by CDNs or load balancers; it tells these intermediaries not to forward requests beyond their expiration time when they are already fresh in their own caches, thus saving bandwidth on both sides of the network connection while still allowing them room for growth as new content comes online (and thus requiring less frequent revalidation). For example: `s-maxage=3600`.

No caching headers tell intermediaries not to store any part of this resource at all; this may be necessary when making updates within an API endpoint because there may be stateful data involved that could otherwise lead users into thinking old results were still valid if served from cache instead of being regenerated every time someone accesses them again later down line.”

Set a long-lived cache timeout

When you cache your responses, do not rely on the client to set their own cache expiry time. This can lead to inconsistent results and poor performance if you have multiple clients that use different expiry times. Instead, set a long-lived cache timeout so that each request uses the same cached response for as long as possible. You can do this by setting an Expires header in your response or using other techniques such as setting max age values in Vary headers.

Support stale-while-revalidate

Stale-while-revalidate is a cache control directive that allows you to cache responses that are stale, but not expired. This means that if the response hasn’t been updated since it was last requested, then the server will respond with a 304 Not Modified status code instead of sending back fresh content.

This makes sense for REST APIs that don’t change often–for instance, if your API provides access to user profiles or other static data. Caching these responses can improve performance without compromising freshness because they will still be served from the cache until they expire (or newer versions of those resources become available).

Caching allows your server to return a response faster, but be aware of the tradeoffs.

Caching is a trade-off between speed and consistency. If you’re building an API that needs to be fast, caching may be the answer. But if you have consistency as your top priority, caching might not be for you.

Caching isn’t meant to replace databases or cache invalidation systems; it’s simply a way to make your server respond faster by storing responses from previous requests in memory or on disk rather than querying the database every time someone makes a request.


Caching is one of the most powerful tools to improve your REST APIs’ performance (for example, ipbase states in one of their articles that they rely on caching to improve their response performance). It allows you to reduce response times and increase throughput, which means more users can be served at once. Caching can also make your service more reliable by reducing the load on your servers during peak hours. However, it does have some tradeoffs: caching may cause stale data when network connectivity is poor or non-existent; users may see different versions of resources depending on how long each cached copy has been valid; and if not implemented correctly, it could even make things worse!

Also read: How to implement rate limiting in your REST APIs



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