How I rate limit without third party services

The video discusses the importance of rate limiting in web applications to prevent abuse and maintain a good user experience. The speaker shares how they implemented a simple rate limiter by IP and by key (such as user ID) in their application. The rate limiter allows a certain number of requests within a specified time period, and throws an error if the limit is exceeded. The speaker notes that while this in-memory approach works well for a single server, it can run into issues when scaling up to multiple servers or a serverless environment. In such cases, using a centralized store like Redis is recommended. The video provides a practical example of how to implement rate limiting, and suggests ways to further refine the approach for different types of actions and environments.

Takeaways

• 🛡️ Implementing rate limiting is crucial for web applications to prevent abuse and ensure a good user experience.
• 🚫 Without rate limiting, malicious users can flood the system with data, leading to a poor experience and strain on the database.
• 💡 The speaker has created a custom rate limiter that allows a specified number of requests within a certain period, using an interface that is easy to implement.
• 🔒 An action error is used to handle exceptions within the rate limiting process, which can be displayed in the user interface.
• 📈 The rate limiter initially works in memory, which is suitable for single server setups, but can run into issues when scaling up.
• ⚖️ For scaling, using a centralized data store like Redis can help maintain consistent rate limiting across multiple servers.
• 🌐 The rate limiter function checks the IP address from the request headers, considering 'x-forwarded-for' or 'x-real-ip' if set.
• 🔄 The rate limit tracking uses a JavaScript object as a map to keep track of IP addresses and their request counts and expiration times.
• 🚦 If the request count exceeds the limit within the time window, an error is thrown to prevent further requests.
• 🔑 An alternative approach is to rate limit by a key, such as a user ID, which can be more effective for authenticated users.
• 🧹 Code cleanup and abstraction allow for easier maintenance and the potential to switch to different storage solutions like Redis in the future.
• 📚 The speaker advises keeping the rate limiter simple and considering the potential for abuse or edge cases where the system might need to scale or handle high traffic.

Q & A

What is the primary reason for implementing rate limiting in web applications?

Rate limiting is crucial in web applications to prevent abuse by malicious users who could otherwise flood the system with data, leading to a poor user experience and increased strain on the database and backend systems.

How does the rate limiter in the script work?

The rate limiter works by allowing a certain number of requests within a specified time period for each IP address. It uses an in-memory JavaScript object to track the count and expiration time of requests.

What is the potential issue with using an in-memory rate limiter when scaling up a system?

An in-memory rate limiter can run into issues when scaling up because each server instance operates independently. This means that with multiple servers behind a load balancer, a user could potentially bypass the rate limit by making requests through different servers.

What is the suggested solution for handling rate limiting in a scaled environment?

For a scaled environment, it's recommended to use a centralized data store like Redis to keep track of rate limits. This ensures that the rate limiting is consistent across all instances of the application.

How does the rate limiter identify the user's IP address?

The rate limiter identifies the user's IP address by checking the request headers for 'x-forwarded-for' or 'x-real-ip'. If these headers are set, it uses the value; otherwise, it returns null.

What is the significance of using 'action error' in the script?

The 'action error' is used because the rate limiting functionality is wrapped in a 'next safe action' library, which requires an 'action error' to display errors in the user interface.

How can rate limiting be applied based on a user ID instead of an IP address?

Rate limiting can be applied based on a user ID by using a 'rate limit by key' approach, where the key is a specific identifier such as the user's ID. This provides a more granular level of control, especially for authenticated users.

What is the benefit of abstracting rate limiting logic into a function?

Abstracting the rate limiting logic into a function allows for easier maintenance and potential refactoring. It also enables the application to easily switch to different rate limiting strategies, such as using an external service like Redis, without major code changes.

What is the potential downside to using a large in-memory JavaScript object for rate limiting?

The potential downside is that if the application receives a high volume of traffic over a long period, the in-memory object could grow very large, which could lead to performance issues or even a denial of service if it's filled with random IP addresses.

How can the rate limiter be improved to handle default rate limiting for authenticated actions?

The rate limiter can be improved by implementing a middleware function that applies a default rate limit to all authenticated actions. This can be done by using a 'rate limit by key' approach with a unique key for each action, such as the user ID combined with a descriptor for the action.

What is the purpose of the toast message that appears when rate limits are exceeded?

The toast message serves as a user-friendly way to notify the user that their request has been rate-limited and cannot be processed at the moment. It provides immediate feedback without requiring a full page reload or additional server requests.

Outlines

00:00

🛡️ Implementing Rate Limiting for Web Applications

The first paragraph discusses the necessity of implementing rate limiting in web applications to prevent abuse and ensure a good user experience. The speaker shares an example where a user can create groups in an application and how, without rate limiting, a malicious user could flood the system with data. They introduce a custom rate limiter they've created that allows a specified number of requests within a certain time frame. The rate limiter uses an IP-based approach and throws an 'action error' when limits are exceeded. The paragraph also touches on the limitations of this in-memory approach when scaling up to multiple servers or using a load balancer, suggesting the use of a centralized system like Redis for more robust rate limiting.

05:01

🔄 Refactoring and Abstracting Rate Limiting Logic

The second paragraph focuses on refactoring the rate limiting code to make it more reusable and less verbose. The speaker demonstrates how to create a more generic function that can be used for both public endpoints limited by IP address and authenticated endpoints limited by a key, such as a user ID. They also discuss the potential for the rate limiting data to grow over time and the need to consider this when building a system. The paragraph concludes with the speaker abstracting the rate limiting logic further to allow for default rate limiting across all authenticated actions, showcasing the power of the abstraction to easily adjust and scale the rate limiting strategy.

Keywords

Rate Limiting

Rate limiting is a technique used to control the amount of incoming and outgoing traffic to or from a network or an application. It's crucial for preventing abuse, ensuring fair usage, and maintaining the quality of service for all users. In the video, the speaker discusses implementing rate limiting to prevent a user from creating too many groups in a short period, which could flood the system with data and degrade the experience for other users.

Web Applications

Web applications are programs that run inside a web browser or on a web server, allowing users to interact with them over the internet. The video script refers to web applications needing rate limiting to manage user actions and prevent malicious behavior, such as a user creating an excessive number of groups.

Malicious User

A malicious user is someone who intentionally tries to harm or exploit a system, often by overloading it with requests or data. The video mentions the need for rate limiting to protect against such users who might flood the system with bogus data, causing a poor user experience and strain on the database.

In-Memory

In-memory refers to data that is stored and managed within the random access memory (RAM) of a computer. The script discusses an in-memory rate limiter, which works well on a single server but can run into issues when scaling up to multiple servers because each server operates independently.

Load Balancer

A load balancer is a device or software that distributes network or application traffic across multiple servers to ensure no single server bears too much load. The video script mentions that when using a load balancer with multiple VPS servers, the in-memory rate limiter may not work effectively because each server maintains its own rate limit state.

IP Address

An IP address is a unique numerical label assigned to each device connected to a computer network that uses the Internet Protocol for communication. The video script explains that rate limiting by IP is done by tracking the number of requests from a specific IP address within a given time frame to prevent abuse.

X-Forwarded-For and X-Real-IP

X-Forwarded-For and X-Real-IP are HTTP headers used to identify the originating IP address of a client connecting to a web server through an HTTP proxy or a load balancer. The video discusses using these headers to determine the client's IP address for implementing rate limiting.

Action Error

An action error is a type of error that occurs within the context of a user action, often used in web development to handle and communicate errors back to the user interface. The video script mentions using an action error to display messages to the user when rate limits are exceeded.

Next Safe Action Library

The Next Safe Action library is a hypothetical or proprietary library mentioned in the video that is used to handle actions safely, possibly including error handling. It requires an action error to display messages in the user interface when rate limits are exceeded.

Rate Limit by Key

Rate limiting by key is a method of controlling access to a resource based on a specific identifier, such as a user ID. This is more granular than IP-based rate limiting and can be used to limit actions for authenticated users. The video script describes implementing rate limiting by key as a more effective approach for authenticated endpoints.

Global Rate Limiting

Global rate limiting is the practice of applying rate limits across an entire system or for all users collectively. The video script suggests setting a global rate limit as a default for authenticated actions to prevent abuse of public mutations, ensuring a fair usage policy for all users.

Highlights

The importance of rate limiting for web applications to prevent abuse and maintain a good user experience.

Creating a custom rate limiter is easier than expected and can be done in-house.

Rate limiting by IP can be effective for single server setups but may encounter issues when scaling.

Using a centralized data store like Redis can help maintain consistent rate limiting across multiple servers.

Rate limiting can be implemented by IP or by a unique key, such as a user ID, for more granular control.

The rate limiter can be abstracted into a function for easier maintenance and potential future changes.

Action errors are used in the UI to display errors when the rate limit is exceeded.

A simple in-memory solution can work for small to medium traffic, but larger applications may require a more robust solution.

Rate limiting can prevent denial of service attacks by limiting the number of requests from a single source.

The rate limiter function can be tested by attempting to perform an action that should trigger the limit.

For authenticated endpoints, rate limiting by user ID can provide a more secure and personalized approach.

Default rate limiting can be applied to all authenticated actions to prevent abuse.

Individual actions can have their own specific rate limits for more fine-tuned control.

The rate limiter can be adjusted to fit the needs of different actions within an application.

The rate limiter implementation is designed to be simple and straightforward for easy understanding and use.

The use of a toast message provides user feedback when a rate limit is exceeded.

The rate limiter can be easily extended or modified to use different storage solutions like Redis for scaling.

The speaker encourages viewers to provide feedback and share their own rate limiting methods.