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API Security Rate Limiting

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Rate limiting is restricting request frequency per user/IP/API key. Naive implementations (simple counter) fail: fixed windows allow spike attacks, no user context. Mastery involves: sliding windows, token buckets, distributed rate limiting (Redis), user-tier aware limits, graceful degradation. Learning takes 3-4 weeks. Companies that rate-limit poorly lose $10k-100k/month to abuse; implementing correctly prevents fraud, DDoS, and ensures SLA stability.

Vad är API Security Rate Limiting

Rate limiting is the practice of restricting the number of requests a client can make to an API within a time window. Rate limiting prevents abuse (credential stuffing, scraping, DDoS), ensures fair resource usage, and protects infrastructure from overload. Implementation requires choosing an algorithm (fixed window, sliding window, token bucket), storage backend (in-memory, Redis), and deciding what to rate-limit (IP, user ID, API key). Rate limiting is a layers defense: edge (Cloudflare), gateway (Kong), server-side. Layered defense is better than any single layer.

🔧 VERKTYG & EKOSYSTEM
Redis rate limitingsliding window algorithmstoken bucket implementationAPI gateways (Kong, Tyk)Cloudflare rate limitingAWS WAFcustom middlewaredistributed systems patterns

📋 Innan du börjar

Api SecurityBackend Development

💰 Lön per region

OmrådeNybörjareMidErfaren
USA$85k$140k$200k
UK£51k£84k£120k
EU€56k€92k€130k
CANADAC$90kC$145kC$210k

🎯 Karriärer som använder API Security Rate Limiting

Api Orchestration Engineer

❓ Vanliga frågor

What's rate limiting and why is it different from throttling?
Rate limiting = rejecting requests that exceed the limit (HTTP 429). Throttling = slowing down requests (delays). Both protect against abuse. Rate limiting is simpler; throttling is friendlier but can queue up requests.
What's the difference between fixed window and sliding window rate limiting?
Fixed window: reset limit every minute (00-59s). Vulnerable: all requests at minute 59 + requests at minute 0 = 2x limit burst. Sliding window: track last N requests in a rolling window. Prevents spike attacks but more CPU-intensive.
How do I rate limit per user vs per IP?
Per IP: simple (no auth needed), but blocks all users behind NAT. Per user: better for logged-in users. Hybrid: rate limit by IP for unauthenticated, by user ID for authenticated.
What's a token bucket and why is it better than counters?
Token bucket: you have N tokens, refill at rate R tokens/second. Each request costs 1 token. Out of tokens? Request is rejected. Elegant for distributed systems and allows burst (if you have 100 tokens and refill 10/sec, you can do 100 requests in a burst, then rate-limited).
How do I handle rate limiting in a distributed system?
Single server: in-memory counter. Multiple servers: shared Redis. Requests to any server check/increment counter in Redis. Trade-off: Redis latency (5-10ms) per request. Solutions: cache locally + sync, or use approximate algorithms.
What happens when a rate-limited user retries?
Retry-After header tells client when to retry (e.g., 'Retry-After: 60'). Well-behaved clients respect this. Malicious clients ignore it. Your server can't prevent retries, only reject them.
Can rate limiting cause false positives (blocking legitimate users)?
Yes. If you set limits too low, real users hit them. Solutions: tiered limits (free = 10/min, paid = 1000/min), whitelist trusted IPs, adaptive limits based on user history.

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