In today's connected world, applications can often have to make hundreds of thousands of API calls towards an external service, and efficient management of such requests is crucial. One of the most prolific techniques adopted to prevent abuse or overuse of a resource is rate limiting - limiting the number of requests a client may make in a given period of time. While rate limiting promotes the service's stability, it's often a challenge to developers who have to ensure their applications have these limits handled in an innocuous manner.

In this blog, we're going to discuss what rate limiting is, why it matters, common strategies, and best practices for dealing with rate limiting in your application.

What is Rate Limiting?

Rate limiting is one of the techniques that web servers and APIs use to regulate incoming traffic and often done in services to avoid an overload, fairly use resources, and protect the server from being overwhelmed with a flood of requests.

For instance, an API can allow a client to make only 100 requests per hour. If this threshold is breached, it will be either reserving the server for further requests or delaying them until the rate limit is reset.

Why is Rate Limiting Important?

• Prevents Abuse: Prohibits spamming or overwhelming a service by its users or bots.
• Ensures Fair Resources utilization through different users and applications.
• Server Protection: Prevents server crashes and degradation of service brought about by overload.
• Cost Optimization: Helps providers manage their infrastructure better to avoid any unnecessary costs from excessive requests.

Common Rate Limiting Strategies

1. Fixed Window Limiting This approach employs a finite time window (for example, 1 hour). The client can then send up a number of requests in that window. Every time the defined window closes, the request count restarts from zero.

Example: The client can make 100 requests in a 1-hour window. If it hits the maximum limit, then it needs to wait for the new window.

Advantages:
Easy to implement.
Suitable for known traffic.

Disadvantages:
If at the beginning of the window a client exceeds the limit it cannot forward more requests even if the server has available capacity.

1. Sliding Window Limiting It follows the count of requests received during a rolling or sliding window of time. In this method, the request count does not reset at specified intervals but declines with time.

Example :
If a client can make 100 requests in an hour, and they make a request at noon, that request will "expire" at 1 PM, freeing up space for a new request.

Advantages

• More flexible than fixed window.
• Reduces traffic bursts.

Disadvantages

• More complicated to implement.
  1. Token Bucket In this algorithm, a "bucket" contains a fixed number of tokens, and each request consumes one token. Tokens are added at a certain rate over time. When the bucket is empty, the client has to wait for more tokens to arrive.

Example:
A client makes 10 requests per second but if they use no tokens for 5 seconds then they will have 50 tokens stored. This allows them to burst up to 50 requests, then drop back down to 10 requests per second.

Advantages
Supports burst traffic.
Guarantees consistent flow of requests.

Disadvantages:

• Difficult to adjust refill rate and bucket size for the best possible performance.

1. Leaky Bucket , Similar to the token bucket, but with the addition of a queue. Requests enter the queue, and they "leak" out at a constant rate. If the bucket overflows, excessive requests are rejected. . Advantages:
2. Ensures constant rate of request.

3. It helps in handling burst traffic.

   Cons

   • In case of request, if it's more than the capacity, then such requests get lost.

Handling Rate Limiting Gracefully

It is very important to handle the rate limiting so that your application does not make requests which are doomed to be failures or the worse, to have its API keys being revoked.

Here's how you can handle the rate limiting:

1. Check the Rate Limit Headers

Most APIs will provide rate limit headers in their response to indicate the current rate limit status. Common headers are:

• X-RateLimit-Limit: The number of allowed requests.
• X-RateLimit-Remaining: The number of remaining requests in the current window.
• X-RateLimit-Reset: When (in Unix epoch) the rate limit will reset.

Using this information you can modify your request patterns in real-time, based on current usage and remaining requests.

2. Implement Exponential Backoff

If you get a response saying that the rate limit has been exceeded, you have to pause for some time and try again later. A good way to handle this is via exponential backoff, that is, increase the time between retries.

For example:

Wait after the first failure for 1 second
Wait after the second failure for 2 seconds
Wait after the third failure for 4 seconds, and so on.

This is a way of preventing flooding the server with consecutive requests, especially if it is overloaded.

3. Use of Caching

By using caching responses, you make fewer API calls. If the data you are requesting does not change often, by storing the response and reusing it for future requests, you will significantly reduce the number of API calls.

For example, instead of repeatedly pinging the same API to return static information such as user details or configuration data, you can cache the results and retrieve them from memory on subsequent requests.

4. **Batch Requests Where Possible

Many APIs allow you to send batches of requests in a single API call. This decreases the number of requests your application has to make, which lowers the chances of you hitting the rate limit.

5. Monitor Your API Usage

Keeping an eye on real-time API usage is obviously super important. With usage monitoring, you could get a sense of your request patterns and know how close you are to hitting the rate limits. You can also set up alerts if usage hits some threshold, so you can adjust your behavior before you hit the limit.

6. Queue Requests

If you expect to reach a rate limit, you can simply use a queue to make the request wait. You can hold them in a queue and then process them once the rate limit is reset so that those requests won't fail.

Tools and Libraries for Rate Limiting

• Axios Rate Limiter: A JavaScript library for Node.js and React applications to limit requests with Axios.
• Redis: Often used as a rate limiting store in distributed systems to track usage across multiple instances.
• Polly (Java): A .NET library that handles retries, even supports exponential backoff and rate limiting.
• Nginx Rate Limiting: If you control the server, you can put the rate limiting directly at the web server level with Nginx's rate-limiting module.

Modern web services require several important features, and one of the most essential is rate limiting-to ensure fair and efficient use of your system. Knowing, as well as implementing, these types of rate-limiting strategies in your application will help you avoid not-so-expected downtime, predict performance, and save money. Right? To manage the rate limiting effectively, you should look at rate limit headers, use proper caching and backoff strategies, have batching requests, and monitor usage.

Implement these best practices, and your application will be well prepared to handle the intricacies of rate limiting!