Understanding Rate Limiting: An Essential Tool for System Stability

10 min readJun 30, 2023

Introduction

Rate limiting is a crucial technique used in computer systems to control the amount of traffic a server can handle. It is a mechanism that monitors and controls the number of requests a client can make to a server within a specific timeframe. Rate limiting is a powerful tool that helps maintain the health and quality of services, and it is widely used in various scenarios, including API management, system security, and operational stability.

What is Rate Limiting?

Rate limiting is the process of controlling network traffic by limiting the number of requests a client (which could be a user, a device, or an IP address) can make to a server within a specified time period. This technique is used to prevent abuse, ensure fair usage, and protect a system from being overwhelmed by too many requests at once, which could lead to a denial of service.

How Does Rate Limiting Help?

Rate limiting serves several purposes for us:

Preventing Abuse: It prevents a single user or a group of users from overloading the system with a high number of requests, intentionally or unintentionally. This is particularly important for public APIs, where a user might send a large number of requests in a short period, causing a denial of service.
Ensuring Fair Usage: By limiting the number of requests per user, rate limiting ensures that all users get a fair share of the system’s resources.
Maintaining System Stability and Resiliency: By preventing the system from being overwhelmed by too many requests, rate limiting helps maintain the system’s stability and performance.

Scenarios for Rate Limiting

Rate limiting is commonly used in the following scenarios:

API Management: APIs often have rate limits to prevent abuse and ensure fair usage. For example, a weather API might limit users to 1000 requests per day. This applies even for the APIs called within the services in a micro-service based architecture.
System Security: Rate limiting can be used to prevent brute force attacks. For example, a login system might limit users to 5 login attempts per minute.
Operational Stability: In microservices architectures, rate limiting can be used to prevent a service from being overwhelmed by too many requests from other services.

Common Algorithms for Rate Limiting

If we talk about how rate limiting can be achieved. Then there are several algorithms used for rate limiting, including:

Fixed Window: This algorithm divides time into fixed-size windows (e.g., 1 minute) and allows a certain number of requests in each window. However, this can lead to a burst of traffic at the beginning of each window.
Sliding Window Log: This algorithm keeps a log of all requests in the past window. It allows a more evenly distributed limit but requires more storage.
Token Bucket: This algorithm allows a certain number of tokens (representing requests) to be added to a bucket at a fixed rate. When a request is made, a token is removed from the bucket. If the bucket is empty, the request is denied. This allows for some burstiness while still limiting the rate.
Leaky Bucket: This algorithm is similar to the token bucket but in reverse. Requests fill the bucket, and the bucket leaks at a fixed rate. If the bucket is full, the request is denied. This enforces a very strict limit with no burstiness.

Let’s look at a simple implementation of the Fixed Window algorithm in Python:

import time
from collections import defaultdict

class RateLimiter:
    def __init__(self, max_requests, window_size):
        self.max_requests = max_requests
        self.window_size = window_size
        self.requests = defaultdict(int)

    def allow_request(self, user_id):
        current_time = time.time()
        window_start = current_time - self.window_size

        # Remove old requests```python
        self.requests[user_id] = [req for req in self.requests[user_id] if req > window_start]

        # Check if user has exceeded the rate limit
        if len(self.requests[user_id]) < self.max_requests:
            self.requests[user_id].append(current_time)
            return True
        else:
            return False

In this code, we create a RateLimiter class that uses a dictionary to keep track of requests from each user. The allow_request method checks if the user has exceeded the rate limit and either allows or denies the request accordingly.

Distributed Rate Limiting

While the rate limiting algorithms discussed so far work well for a single server, they can be problematic in a distributed system where multiple servers need to enforce the same rate limit. This is because each server only has a local view of the requests it has processed, and it doesn’t know about the requests processed by other servers.

Distributed rate limiting aims to enforce a global rate limit across all servers. This is more complex than local rate limiting because it requires communication between servers, which can be slow and unreliable. It also requires a way to deal with clock skew, as the servers’ clocks might not be perfectly synchronized.

There are several approaches to distributed rate limiting, including:

Centralized Rate Limiter: One approach is to have a centralized rate limiter that all servers communicate with to check if a request is allowed. However, this can become a bottleneck and a single point of failure.
Distributed Counters: Another approach is to use distributed counters, such as those provided by distributed databases like Cassandra or Redis. Each server increments the counter when it processes a request, and the rate limit is enforced based on the total count.
Probabilistic Rate Limiting: This approach uses probabilistic data structures, such as a Count-Min Sketch, to estimate the number of requests. This allows for a high degree of scalability, but at the cost of some accuracy.

Rate Limiting in Microservices Architecture

Microservices architecture is a design pattern where an application is structured as a collection of loosely coupled services. Each service is a small, independent unit that performs a specific function. While this architecture offers many benefits, it also introduces new challenges, one of which is managing the rate of requests between services. This is where rate limiting comes into play.

The Role of Rate Limiting in Microservices

In a microservices architecture, each service typically runs in its own process and communicates with other services over a network. This means that a service can become overwhelmed if it receives too many requests from other services in a short period of time.

Rate limiting can be used to prevent this by controlling the number of requests a service can send to another service within a specific timeframe. This helps to maintain the stability and performance of the system as a whole.

Example Scenarios

Let’s consider an e-commerce application that is built using a microservices architecture. The application might have separate services for user management, product catalog, order processing, and payment processing.

User Management Service: This service might be subject to a large number of login attempts. Without rate limiting, an attacker could attempt a brute force attack, trying thousands of password combinations in a short period of time. By applying rate limiting, the system can limit the number of login attempts from the same IP address, effectively mitigating such attacks.
Product Catalog Service: During a flash sale, this service might receive a huge number of requests from the user interface. Without rate limiting, the service could become overwhelmed and crash, affecting the availability of the product catalog. Rate limiting can help to prevent this by ensuring that the service only handles a manageable number of requests at any given time.
Order Processing and Payment Processing Services: When a user places an order, the Order Processing Service communicates with the Payment Processing Service to handle the transaction. If the Order Processing Service sends too many requests to the Payment Processing Service in a short period of time, it could overwhelm the Payment Processing Service. Rate limiting can be used to control the rate of requests from the Order Processing Service, ensuring that the Payment Processing Service can handle its load.

Implementing Rate Limiting in Microservices

Rate limiting in a microservices architecture can be implemented at various levels:

Service Level: Each service can implement its own rate limiting. This can be effective, but it requires each service to manage its own rate limiting logic.
API Gateway Level: An API gateway can be used to manage requests between services. The API gateway can implement rate limiting, providing a centralized place to manage the rate of requests.
Client Level: The clients (i.e., the services making the requests) can implement rate limiting. This can be useful to prevent a client from overwhelming a service with requests.

In a distributed system like a microservices architecture, it’s important to consider distributed rate limiting, as discussed earlier in this blog. This ensures that the rate limit is enforced across all instances of a service, not just on a per-instance basis.

Rate Limiting Libraries: Tools to Manage Traffic

Implementing rate limiting from scratch can be a complex task, especially in a distributed environment. Thankfully, there are several libraries available that can help simplify this process. Here are some of the most commonly used rate limiting libraries across various programming languages:

Express-rate-limit (Node.js): This is a simple rate limiting middleware for Express routes. It uses a fixed window counter for limiting the number of requests from a single IP address. It’s easy to use and can be a good fit for small to medium-sized applications.
Ratelimiter (Node.js): This is another rate limiting library for Node.js, but it uses Redis for storing request data, making it suitable for distributed environments.
django-ratelimit (Python): This is a rate limiting library for Django applications. It provides a decorator that you can use to apply rate limits to your views. It supports several rate limiting strategies, including fixed window, sliding window, and token bucket.
Flask-Limiter (Python): This library provides rate limiting features for Flask applications. It supports multiple strategies and storage backends, including memory, Redis, and Memcached.
Guava (Java): Google’s Guava library provides a RateLimiter class for Java applications. It uses the token bucket algorithm and supports both blocking and non-blocking acquisition of tokens.
Bucket4j (Java): This is a powerful rate limiting library for Java that supports multiple algorithms, including token bucket and leaky bucket. It can be used with various backends, including in-memory, Hazelcast, Ignite, and JCache.
Laravel Throttle (PHP): This package provides a simple and flexible rate limiting solution for Laravel applications. It uses the token bucket algorithm and supports both IP-based and authenticated user-based rate limiting.
Rack::Attack (Ruby): This is a rack middleware for blocking & throttling abusive requests. It allows you to safelist, blocklist, throttle, and track based on arbitrary properties of the request.
Tollbooth (Go): This is a rate limiting library for Go. It provides a flexible rate limiter with multiple strategies and supports HTTP middleware.
Governor (Rust): A flexible rate limiting library for Rust. It provides a general-purpose rate limiter with a variety of strategies, including token bucket, leaky bucket, and fixed window.

These libraries provide a wide range of features and support various rate limiting strategies, making it easier to implement rate limiting in your application. However, it’s important to choose a library that fits your specific needs and environment. For example, if you’re working in a distributed environment, you’ll need a library that supports distributed rate limiting, such as Ratelimiter for Node.js or Bucket4j for Java.

Rate Limiting and Circuit Breakers: Complementary Techniques for System Stability

Rate limiting and circuit breakers are two techniques used to maintain the stability and performance of a system, especially in a microservices architecture. While they serve different purposes, they can be used in tandem to effectively manage the flow of requests and handle failures in a system.

Rate Limiting vs Circuit Breakers

As we’ve discussed, rate limiting is a technique used to control the number of requests a client can make to a server within a specific timeframe. It’s primarily used to prevent a server from being overwhelmed by too many requests at once.

On the other hand, a circuit breaker is a design pattern used in modern software development that allows a system to handle failures gracefully. It’s named after the electrical switch that “trips” or “breaks” the circuit when there’s too much current, preventing damage to the electrical system.

In a microservices architecture, a circuit breaker can be placed between services. If the downstream service fails or starts to respond slowly, the circuit breaker “trips” and starts to fail fast for a certain period of time. This gives the failing service time to recover and prevents the failure from cascading to other parts of the system.

How They Work Together

While rate limiting and circuit breakers serve different purposes, they can be used together to effectively manage the flow of requests and handle failures in a system.

Rate limiting can be used to prevent a service from being overwhelmed by too many requests, which is especially important in a microservices architecture where one service might make many requests to another service.

Circuit breakers, on the other hand, can be used to handle failures when they do occur. If a service starts to fail, the circuit breaker can “trip” and prevent further requests from reaching the failing service. This gives the service time to recover and prevents the failure from affecting the rest of the system.

In this way, rate limiting and circuit breakers complement each other. Rate limiting helps to prevent failures by managing the flow of requests, while circuit breakers help to handle failures when they do occur.

Example Scenario

Let’s consider a scenario in a microservices based e-commerce application. The Order Processing Service makes requests to the Inventory Service to check if a product is in stock.

With rate limiting, the Order Processing Service is limited in the number of requests it can make to the Inventory Service within a certain timeframe. This prevents the Inventory Service from being overwhelmed by too many requests.

Now, let’s say the Inventory Service starts to respond slowly or fails completely. Without a circuit breaker, the Order Processing Service would continue to make requests, waiting for a response each time. This could cause the Order Processing Service to become slow or unresponsive, affecting the user experience.

With a circuit breaker, after a certain number of failures or slow responses, the circuit breaker would “trip” and start to fail fast. This would prevent further requests from the Order Processing Service from reaching the Inventory Service, giving the Inventory Service time to recover and preventing the failure from affecting the Order Processing Service and the rest of the system.

In conclusion, while rate limiting and circuit breakers serve different purposes, they are both crucial tools for maintaining the stability and performance of a system. By using them in tandem, you can effectively manage the flow of requests and handle failures in your system

Conclusion

Rate limiting is a powerful tool for maintaining system stability and preventing abuse. While it can be complex to implement, especially in a distributed system, it is an essential part of any robust system. By understanding the different algorithms and approaches to rate limiting, you can choose the one that best fits your needs and ensure that your system can handle high traffic loads without being overwhelmed.