How I rate limit without third party services

Web Dev Cody
8 May 202408:45

TLDRThe video script discusses the importance of rate limiting in web applications to prevent abuse and maintain a good user experience. The speaker shares their experience in implementing a custom rate limiter using JavaScript objects to track IP addresses and user keys. They explain how to set up rate limits for both public and authenticated endpoints, highlighting the simplicity of the in-memory solution for single VPS deployments and the need for a centralized system like Redis for scaling. The script also covers error handling with action errors and the potential for refactoring to use more robust solutions like Redis. The speaker emphasizes the ease of implementing such a system and the benefits of abstraction for future scalability.

Takeaways

  • 🔒 Implementing rate limiting is crucial to prevent abuse and maintain a good user experience by avoiding spamming of data and excessive strain on the database.
  • 🛠️ A custom rate limiter can be created without relying on third-party services, which is straightforward and involves tracking requests by IP or a specific key.
  • ⏱️ The rate limiter allows a certain number of requests within a specified time period, for instance, one request every 10 seconds.
  • 🚫 When the rate limit is exceeded, an error is thrown, and the user is notified via a user interface element like a toast message.
  • 📈 For a single server deployment, in-memory rate limiting is sufficient, but for a scaled environment, a centralized solution like Redis is recommended.
  • 📊 The rate limiter function checks the request headers for the IP address, using 'x-forwarded-for' or 'x-real-ip' if available.
  • 🔄 If the tracking record for an IP or key exists and has not expired, it is reset and updated; otherwise, a new record is created.
  • 🚦 The rate limiter can be applied both for public endpoints using IP addresses and for authenticated endpoints using a unique key like a user ID.
  • ✅ Abstracting the rate limiting logic allows for easy adjustments or integration with other systems like Redis in the future.
  • 💡 It's important to consider the potential growth of the rate limiting data structure and the possibility of a denial-of-service attack filling it with random IP addresses.
  • 📋 The script suggests creating a generic rate limiting function that can be applied to various scenarios, such as global rate limiting for authenticated actions.

Q & A

  • Why is rate limiting important for web applications?

    -Rate limiting is crucial to prevent abuse and malicious activities such as data flooding, which can lead to a poor user experience and strain on the database and backend systems.

  • What is the basic concept of the rate limiter described in the transcript?

    -The rate limiter described in the transcript allows a certain number of requests per time period, using an in-memory approach for tracking. It uses an interface that can be called by a function named 'rate limit by IP', which sets the limit on how many requests can be made within a specified time frame.

  • How does the rate limiter handle errors?

    -The rate limiter catches errors and throws an 'action error', which is then handled by a 'next safe action' library that displays the error in the user interface.

  • What is the potential issue with the rate limiter when scaling up to multiple servers?

    -The in-memory rate limiter operates in isolation on each server, which means that if multiple servers are running behind a load balancer, a user could potentially bypass the rate limit by making requests to different servers within the same time window.

  • How can the rate limiter be improved for a scalable system?

    -For a scalable system, the rate limiter can be improved by using a centralized data store like Redis to keep track of the rate limits, ensuring consistent behavior across multiple servers or instances.

  • What is the function of the 'getIP' method in the rate limiter?

    -The 'getIP' method is used to extract the IP address from the incoming request headers, checking for 'x-forwarded-for' or 'x-real-ip', which are commonly used to identify the client's IP address in web applications.

  • How does the rate limiter implement rate limiting by key?

    -The rate limiter can implement rate limiting by key by using a unique string identifier, such as a user ID, instead of an IP address. This allows for more granular control, especially for authenticated users.

  • What is the advantage of limiting by an authenticated user's ID over limiting by IP?

    -Limiting by an authenticated user's ID provides a more specific control mechanism, as it targets individual users rather than IP addresses, which can be shared by multiple users or easily changed by malicious actors.

  • How can the rate limiter be made more generic for different types of keys?

    -The rate limiter can be made more generic by abstracting the logic into a function that accepts a key and a limit, allowing it to be used with various types of identifiers, such as user IDs, action types, or any other relevant key.

  • What is the potential downside of using an in-memory rate limiter with high traffic?

    -An in-memory rate limiter could potentially grow very large over time with high traffic, which might lead to performance issues or memory constraints. It's important to monitor and manage the size of the in-memory store or consider a more scalable solution like Redis.

  • How can the rate limiter be extended to include default rate limiting for all authenticated actions?

    -The rate limiter can be extended by adding a middleware function that applies a default rate limit to all authenticated actions. This can be done by calling the rate limit function with a generic key, such as 'user.ID', and setting a default limit and window size.

Outlines

00:00

🛠️ Implementing Rate Limiting for Web Applications

The first paragraph discusses the necessity of rate limiting in web applications to prevent abuse and flooding of data by malicious users. The speaker shares their experience with implementing a custom rate limiter that restricts the number of requests a user can make within a specific time frame, in this case, one request every 10 seconds. They explain the negative impact of not having rate limiting, such as a poor user experience and strain on the database and backend. The speaker also touches on the limitations of an in-memory rate limiter when scaling up and suggests using a centralized solution like Redis for more robust rate limiting. Additionally, they provide insight into extracting the IP address from the request headers and how to handle different deployment environments.

05:01

🔄 Refactoring and Abstracting Rate Limiting Logic

The second paragraph focuses on the process of refactoring and abstracting the rate limiting logic for better code organization and scalability. The speaker demonstrates how to create a more generic rate limiting function that can be applied to both public and authenticated endpoints. They mention the use of an 'action error' for handling errors within the UI and the benefits of using a centralized data store like Redis for maintaining rate limit state across multiple servers. The paragraph also explores the concept of rate limiting by a key, such as a user ID, which provides a more granular control over the rate limiting for authenticated users. The speaker concludes by emphasizing the importance of considering potential edge cases and the ease of refactoring due to the abstracted nature of the rate limiting functions.

Mindmap

Keywords

💡Rate Limiting

Rate limiting is a technique used to control the rate at which a user can perform a particular action, such as logging in or creating groups in a web application. It is crucial for preventing abuse, ensuring a good user experience, and protecting the backend and database from being overwhelmed. In the video, the speaker discusses implementing rate limiting without relying on third-party services, which is essential for maintaining control over the application's performance and security.

💡Web Applications

Web applications are programs that users can access via the internet using a web browser. They are designed to perform various tasks and can range from simple websites to complex applications like social media platforms or online marketplaces. The script mentions web applications in the context of needing rate limiting to prevent malicious users from creating excessive groups or data, which could degrade the user experience and strain the system.

💡Malicious User

A malicious user is someone who intentionally tries to harm or exploit a system, often by performing actions that are not allowed or intended by the system's design. In the video, the speaker refers to a hypothetical scenario where a malicious user attempts to flood the system with data by creating groups repeatedly, which would be mitigated by implementing rate limiting.

💡In-Memory

In-memory refers to the use of a computer's RAM (Random Access Memory) to store and manipulate data. This approach is faster than using disk storage but is limited by the amount of available memory. The speaker mentions that their rate limiter is in-memory, which works well for a single server but could cause issues when scaling up to multiple servers or using a load balancer.

💡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 traffic. It helps in managing the load and improving the reliability and availability of applications. The script discusses how rate limiting can become problematic when using a load balancer if the rate limiter is not centralized, as each server would have its own separate rate limiter.

💡IP Address

An IP (Internet Protocol) address is a unique numerical label assigned to each device connected to a computer network that uses the Internet Protocol for communication. The speaker uses IP addresses to implement rate limiting by tracking the number of requests from each IP within a certain time frame, which helps prevent abuse from a single source.

💡X-Forwarded-For and X-Real-IP

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

💡Action Error

An action error is a type of error that occurs within the context of an action or a function call in a software application. In the video, the speaker uses action errors to handle situations where the rate limit has been exceeded, allowing the application to provide feedback to the user through the user interface.

💡Rate Limit by Key

Rate limit by key is a method of rate limiting where the restriction is applied based on a specific key, such as a user ID. This is more granular than rate limiting by IP, as it allows for different limits to be applied to different authenticated users. The speaker demonstrates how to implement rate limiting by key, which is particularly useful for authenticated endpoints.

💡Global Rate Limiting

Global rate limiting is a strategy where a limit is applied across all users or actions, regardless of individual circumstances. This is used to prevent the system from being overwhelmed by too many requests in a short period. The speaker discusses the concept of applying a global rate limit as a default for authenticated actions to ensure that users do not abuse the system.

💡Abstraction

Abstraction in programming refers to the process of hiding the complex reality while exposing only the necessary parts. It allows developers to manage complexity by creating functions or modules that encapsulate specific behaviors. The speaker abstracts the rate limiting functionality into a function, which simplifies the process of changing the underlying implementation, such as switching to a different storage mechanism like Redis.

Highlights

Web applications often require rate limiting to prevent malicious users from overwhelming the system with data.

Rate limiting is crucial for maintaining a good user experience and reducing strain on the database and backend.

The speaker implemented a custom rate limiter using an in-memory approach for single server deployments.

For multiple server deployments, using a centralized data store like Redis is recommended for consistent rate limiting.

The rate limiter function tracks requests by IP, with a fallback to other headers if necessary.

A JavaScript object is used as a map to track IP addresses and their corresponding request counts and expiration times.

If the rate limit is exceeded, an 'action error' is thrown to be handled by the UI.

The rate limiter can be easily adjusted to limit by different keys, such as user IDs for authenticated endpoints.

The system provides feedback to the user when rate limits are exceeded through UI notifications.

The rate limiter is designed to be simple and scalable, with the ability to integrate with third-party services if needed.

The speaker demonstrates how to refactor the rate limiter to be more versatile and less verbose.

Rate limiting by key is shown to be a more secure approach, as it is harder for users to rotate through identifiers.

The implementation allows for default rate limiting on authenticated actions to prevent abuse.

The rate limiter can be further customized to limit specific actions individually, providing granular control.

The speaker emphasizes the importance of considering scalability and potential issues when building out a rate limiting system.

The rate limiter is designed to be easily maintainable and upgradable to handle increased traffic or changing requirements.

A potential downside of the in-memory approach is the growth of a large JavaScript object over time, which may require monitoring.

The speaker suggests that for most use cases, the simple in-memory rate limiter will suffice until a more robust solution is needed.