What is the Rate Limiting pattern and what algorithms are commonly used?

The Rate Limiting pattern caps the number of requests a client (identified by IP, API key, or user ID) can make within a time window. When the limit is exceeded, the server rejects excess requests with an HTTP 429 (Too Many Requests) and optionally includes a Retry-After header. It protects services from accidental or malicious overload, enforces fair-use quotas, and prevents a single client from exhausting shared resources.

Four commonly used algorithms:

• Fixed Window Counter — count requests in fixed time windows (e.g., 0–60 s, 60–120 s). Simple and cheap. Weakness: a burst can occur at the boundary — up to 2× the limit in a single window transition.
• Sliding Window Log — store the exact timestamp of each request. Count requests in the rolling window ending at "now". Precise but memory-intensive (O(N) per client).
• Sliding Window Counter — approximate the sliding window by blending the current and previous fixed-window counts using elapsed time fraction. Good accuracy at low memory cost.
• Token Bucket — a bucket fills with tokens at a fixed rate (e.g., 10 tokens/second, bucket size 100). Each request consumes one token. If the bucket is empty, reject. Allows controlled bursting up to the bucket size.
• Leaky Bucket — requests fill a queue (the "bucket"). The bucket drains at a fixed constant rate. Smooths bursty input to a steady output. Excess requests that overflow the bucket are rejected.

# Redis-based Token Bucket (pseudocode)
tokens = redis.get("rate:" + clientId) or bucketCapacity
if tokens < 1:
    return HTTP 429
redis.decrby("rate:" + clientId, 1)
redis.expire("rate:" + clientId, windowSeconds)
# proceed with request

Rate limits are commonly enforced at the API Gateway using Redis (for distributed state across multiple gateway replicas) with the Token Bucket or Sliding Window Counter algorithm.

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