Rate Limiting

Algorithms, distributed challenges, and Redis implementations

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Rate limiting protects your APIs from abuse, ensures fair resource usage, and maintains system stability. Different algorithms offer different trade-offs: token bucket for burst allowance, sliding window for accuracy, and fixed window for simplicity. In distributed systems, the challenge intensifies with race conditions and coordination overhead.

The key insight: choose algorithms based on your accuracy needs. Fixed window is simple but allows double-rate bursts at boundaries. Sliding window is accurate but memory-intensive. Token bucket handles bursts well. For distributed systems, use Redis with Lua scripts for atomic operations or accept eventual consistency.

⚡ Quick Decision

Choose Token Bucket When:

• Traffic has natural bursts
• Users expect burst allowance
• Memory efficiency matters

Choose Sliding Window When:

• Precision is critical
• Traffic volume is moderate
• No burst tolerance needed

💡 For implementation guides and code examples: See our comprehensive Rate Limiting Technology Deep Dive

Algorithm Selection Matrix

Choose your rate limiting algorithm based on traffic patterns and accuracy requirements.

Token Bucket

LowHigh

Best for:

APIs that need to allow occasional bursts but maintain average rate

Memory:

Low

Trade-off:

Can allow sustained bursts if bucket is full

Leaky Bucket

MediumHigh

Best for:

Systems requiring strict rate control and can buffer requests

Memory:

Medium

Trade-off:

No burst allowance

Fixed Window

LowLow

Best for:

Simple rate limiting where boundary effects are acceptable

Memory:

Low

Trade-off:

Suffers from boundary problems

Sliding Window Log

HighHigh

Best for:

Applications requiring precise rate limiting with low traffic

Memory:

High

Trade-off:

High memory usage (stores all timestamps)

Sliding Window Counter

MediumMedium

Best for:

High-traffic applications needing better accuracy than fixed window

Memory:

Low

Trade-off:

Still some approximation errors

Algorithm Comparison

Implementation Complexity

2 algorithmsSimple

1 algorithmsComplex

Accuracy vs Memory Trade-off

3 algorithmsLow Memory

3 algorithmsHigh Accuracy

Distributed Systems Quick Guide

✅ Simple Solutions

Accept Eventual Consistency: Allow slight over-limit during race conditions

Use Redis Cluster: Atomic operations prevent most races

Sticky Load Balancing: Route users to same server

⚠️ Complex Solutions

Distributed Locks: High latency but perfect accuracy

Consensus Protocols: Raft/Paxos for critical limits

Two-Phase Commit: Database-like consistency

Race ConditionsHigh

Problem:

Multiple servers checking and updating counters simultaneously can lead to exceeding limits

Best Solution:

Distributed locks (performance impact)

Network PartitionsHigh

Problem:

Rate limiter storage becomes unavailable, decisions needed on fail-open vs fail-closed

Best Solution:

Local fallback counters

Clock SynchronizationMedium

Problem:

Different server clocks can cause inconsistent window calculations

Best Solution:

NTP synchronization

Storage BottlenecksMedium

Problem:

Centralized rate limit storage becomes performance bottleneck

Best Solution:

Sharding rate limit data

🚀 Production Checklist

Essential Setup

□ Health check endpoints excluded from rate limiting

□ Rate limit headers returned (X-RateLimit-*)

□ Monitoring dashboards for hit rates

□ Fail-open vs fail-closed decision documented

Performance Optimization

□ Redis pipelining for batch checks

□ TTL on rate limit keys

□ Local caching with short expiry

□ Circuit breaker for rate limiter storage

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