Create a rate limiting system.

The interviewer aims to assess your understanding of rate limiting concepts within system design. Rate limiting is a method used to control network traffic by restricting the number of requests a client can send to a server within a defined time period. They are typically looking for your ability to design a system that can manage high traffic loads without failure, while also safeguarding against abuse or attacks. When responding to this question, it’s essential to cover the significance of rate limiting, various implementation methods, and the factors to consider during its implementation.

Rate limiting is essential for maintaining system stability, particularly in situations with limited resources or when defending against malicious attacks. It is also utilized to restrict API requests to prevent misuse. One approach to implementing a rate limiter is through the Token Bucket algorithm. In this method, a bucket holds a specific number of tokens, which are added at a certain rate. When a request is made, a token is used if available, and the request is processed. If no tokens are left, the request is rejected until new tokens are added. The Token Bucket algorithm is straightforward and easily distributable. It offers flexible rate limiting, allowing adjustments to both the token addition rate and bucket size. However, it may permit traffic surges if the bucket is full. Another possible design is the Leaky Bucket algorithm. In this model, requests are likened to water poured into a bucket. If the bucket overflows, the excess requests are throttled. The rate at which the water leaks from the bucket determines how quickly requests are processed. A key factor when designing a rate limiter is its placement. It can be implemented on the client-side or server-side, each with its own pros and cons. A client-side rate limiter is generally easier to set up but can be easily circumvented, while a server-side rate limiter offers enhanced security but demands more resources.

Creating a rate limiter entails regulating the number of actions an entity (such as a user, IP address, or API) can execute within a designated timeframe. A widely used approach is the Fixed Window Algorithm , which tracks the number of requests made during the current time window. For example, if a user is permitted 100 requests per hour, once they reach that limit within any segment of that hour, any additional requests will be blocked. Nonetheless, the Fixed Window algorithm can inadvertently permit double the allowable requests near the boundaries of the time frame, specifically at the end and beginning of the window. To address this, the Sliding Log Algorithm offers an enhancement by recording the timestamps of each request in a log, thereby mitigating the risk of a sudden surge in requests that can occur with the fixed window approach. However, this method requires more memory to accommodate the storage of all request timestamps. A more memory-efficient alternative is the Sliding Window Counter Algorithm , which, unlike the Sliding Log, only keeps track of the total number of requests and the timestamp of the earliest request within the current window. When developing a rate limiter, the selection of the algorithm is influenced by the specific requirements of the system. Factors such as the level of protection needed, available resources, and the balance between precision and memory consumption must all be taken into account.