Throttling and Backpressure

• When systems get overwhelmed with too many requests, they can crash or become unresponsive. This is a common problem in APIs and distributed systems. Throttling and backpressure are two techniques that help prevent system overload.

• Throttling limits how many requests a client can make within a specific time period. For example, an API might allow only 100 requests per minute from each user. When a client exceeds this limit, their requests are rejected until the next time window begins. This protects the server from being overwhelmed and ensures fair resource distribution among all clients.

• While throttling works by rejecting excess requests, backpressure takes a different approach. Instead of rejection, it slows down the rate at which requests are accepted. Think of water flowing through a pipe. If you pour too fast, water backs up at the entrance. Similarly, backpressure makes the sender slow down when the receiver can't keep up.

• Backpressure is especially important in streaming systems where data flows continuously. For example, if a service processing video streams can't keep up with incoming data, it signals the sender to slow down rather than dropping frames or crashing.

There are several ways to implement throttling:

1. Token bucket: Clients get tokens that replenish over time

2. Leaky bucket: Requests are processed at a constant rate

3. Fixed window: Simple counter reset at fixed intervals

4. Sliding window: More accurate but complex to implement

Backpressure can be implemented through:

1. Buffer limits: Stop accepting new requests when buffer is full

2. Flow control: Use protocols that support speed control

3. Queue monitoring: Adjust accept rate based on queue size

Here's a comparison of both approaches: