**Title:** How Do Rate Limits Work?

**SEO Keywords:** rate limits, API rate limiting, RESTful API, HTTP requests, server load management

**Intro:**
As developers, we're no strangers to APIs and the importance of managing server load to ensure a smooth user experience. One crucial mechanism for achieving this is rate limiting - a technique that prevents excessive requests from overwhelming our servers. But have you ever stopped to think about how rate limits actually work? In this post, we'll dive into the world of rate limiting, exploring its purpose, types, and implementation.

**Main Blog Content:**
Rate limiting is a simple yet effective way to regulate the number of requests an API receives within a given time frame. Its primary goal is to prevent abuse and ensure that your server resources aren't exploited by malicious users or overzealous scripts. There are two main types of rate limits:

* **Global Rate Limit**: This type of limit applies to all requests made to your API, regardless of the resource being accessed. For example, a global rate limit might allow 100 requests per minute across your entire API.
* **Resource-Specific Rate Limit**: As the name suggests, this type of limit is specific to individual resources within your API. For instance, you might set a rate limit of 5 requests per second for a popular endpoint, while allowing unlimited requests to less critical endpoints.

To implement rate limits, developers typically employ one or more of the following strategies:

* **Token Bucket Algorithm**: This approach involves maintaining a virtual bucket that fills up with tokens at a certain rate. When a request is made, it consumes a token from the bucket. If the bucket is empty, the request is rejected.
* **Leaky Bucket Algorithm**: Similar to the token bucket algorithm, this method uses a leaky bucket that gradually drains and refills its contents. Requests are allowed if there's enough "leak" available, but excessive requests can still be blocked.
* **Simple Counter**: This straightforward approach involves maintaining a simple counter that increments with each request. If the counter exceeds a certain threshold, subsequent requests are rejected.

Here's an example of how you might implement a global rate limit using a simple counter in Java:
```java
public class RateLimiter {
    private int maxRequestsPerMinute = 100;
    private int currentRequests = 0;

    public boolean allowRequest() {
        currentRequests++;
        if (currentRequests > maxRequestsPerMinute) {
            return false; // request exceeded rate limit
        }
        return true; // request allowed within rate limit
    }

    public void resetCurrentRequests() {
        currentRequests = 0;
    }
}
```
**TL;DR:** Rate limits are a vital mechanism for managing server load and preventing abuse. By understanding the different types of rate limits (global and resource-specific) and implementing strategies like token bucket, leaky bucket, or simple counter algorithms, developers can effectively regulate API requests and ensure a smooth user experience.