Rate Limiter Algorithms Lab (Token Bucket, Leaky Bucket, Sliding Window)
Inject traffic bursts and watch Token Bucket and Sliding Window algorithms defend against API abuse. Interactive laboratory comparing Token Bucket, Leaky Bucket, Fixed Window Counter, and Sliding Window Log algorithms under bursty network traffic.
Distributed Rate Limiter Simulator
Test Token Bucket, Leaky Bucket, and Sliding Window algorithms under sudden request traffic spikes.
Live Traffic Ingestion Stream (Last 20 Requests)
How It Works Under the Hood
Rate limiters prevent denial-of-service attacks, brute-force credential stuffing, and cascading downstream server exhaustion. The Token Bucket algorithm maintains a bucket of tokens that fills at a constant rate up to a fixed maximum capacity; incoming requests consume tokens, allowing smooth handling of short bursts while strictly capping average throughput. The Sliding Window algorithm tracks timestamps to prevent the classic double-burst vulnerability that plagues Fixed Window counters at boundary transitions.
Core Architectural Principles
- Token Bucket: Tokens added at constant rate; supports instantaneous traffic bursts up to bucket capacity.
- Leaky Bucket: Requests enter a FIFO queue and leak out at a constant, smooth rate (ideal for packet traffic shaping).
- Sliding Window Counter: Memory-efficient approximation that weights counts across current and previous time windows.
In interviews, always recommend Token Bucket for general API rate limiting because of its simplicity and native burst support. In distributed setups, mention using Redis Sorted Sets (`ZADD`, `ZRANGEBYSCORE`) or Redis with Lua scripts to guarantee atomic bucket updates.
Fixed window memory efficiency vs sliding window boundary precision; local memory speed vs distributed Redis synchronization latency.