Rate Limiting
Algorithms, distributed challenges, and Redis implementations
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⚡ 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
📝 Test Your Knowledge
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