Typorithm · medium

Rate Limiting

rate-limitingsystem-designrate-limittoken-bucketthrottle

Saved on this device

Full lesson · 8 learning steps Open any exercise for focused practice

Step 1 of 8 · intro card

Learn Rate Limiting

Reliability

Rate limiting caps how many requests a client may make in a window. Token bucket, leaky bucket, and sliding window are the common algorithms; a distributed Redis counter enforces the limit across servers, and exceeding it returns 429 with Retry-After.

Rate limiting protects a service from abuse and overload by capping requests over a time window. The token bucket, leaky bucket, and sliding window are the three algorithms you will see most often.

Limits are keyed by scope: per-user, per-IP, or global. Because requests hit many servers, a shared counter in a store like Redis enforces the same quota everywhere instead of each instance counting on its own.

When a client exceeds its limit, the service responds with HTTP 429 Too Many Requests and a Retry-After header so well-behaved clients back off. A token bucket lets a short burst through, then throttles to the steady refill rate.

Token bucket, leaky bucket, sliding window are the common algorithms.

Scope the limit per-user, per-IP, or global.

Exceeding the limit returns 429 with Retry-After.

rate_limit:
  algorithm: token-bucket
  scope: per-user-or-ip
  store: redis-counter
  response: 429-retry-after
  burst: allow-then-throttle

Read the implementation

Connect each part of the code to the concept before you type.

1. Algorithm

The token bucket refills at a steady rate and lets each request spend a token, allowing short bursts up to the bucket size.

algorithm: token-bucket

2. Scope

Limits can apply per-user, per-IP, or globally — picking the right key decides who shares a quota.

scope: per-user-or-ip

3. Distributed store

A shared counter in Redis enforces the limit across all servers, not just one process.

store: redis-counter

4. Over-limit response

Exceeding the limit returns HTTP 429 Too Many Requests with a Retry-After header telling the client when to try again.

response: 429-retry-after

5. Burst handling

A bucket absorbs a short burst, then throttles steady traffic to the refill rate.

burst: allow-then-throttle

A complete learning loop

Understand

Read the concept map and follow the code breakdown.

Reconstruct

Type the implementation, repair mistakes and work through every recall exercise.

Verify

Use the final check without hints; revisit weak concepts in short practice sessions.