Rate Limiter
Diagram: Rate Limiter · 78 elements
%20Design_Rate%20Limiter.svg)
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"text": "Functional Requirements\n\n- Rate limiter throttle API requests based on IP, \nuser ID or other properties\n- Distributed rate limiting. The rate limiter can be shared\nacross multiple servers or process\n- Exception handling. show clear exceptions to users\nwhen their requests are throttled\n- High Fault Tolerance: any problems with the rate limiter\nlike cache offline, it does not affect the entire system",
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"text": "Non Functional Requirements\n\n- Minimum latency overhead (<10ms per request check)\n- availability >> consistency\n- The system should handle 1M requests/second across\n100M DAU\n",
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"text": "Rules: Authenticated users get 1k requests/hr \n\nClients: Rate limited by user ID, IP addresses, API keys\n or combinations of them.\n\nRequests: Incoming API requests evaluated against rate\nlimiting rules. Each requsts carries context like client identiy\nendpoint being accessed and timestamp that determines\nwhich rules to apply and how to track usage.\n\nisRequestAllowed(clientID,ruleID):\n return {\n passess: bool,\n remaining: number,\n resetTime: timestamp\n }\n\n\nX-RateLimit-Limit: how many call clients can make per time window.\nX-RateLimit-Remaining: no. of req left in the current window.\nX-RateLimit-Reset: rate limit resets as a Unix Timestamp.\nX-RateLimit-Retry-After: no. of seconds to wait until \nyou can make a request again without being throttled.\n\nIdentify Client?\n- User ID: JWT token present in Authorization header\n- IP Address: Present in X-Forwarded-For header\nAPI Key: X-API-Key header. Each key holder gets their own\nlimits\n\n",
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"originalText": "Rules: Authenticated users get 1k requests/hr \n\nClients: Rate limited by user ID, IP addresses, API keys\n or combinations of them.\n\nRequests: Incoming API requests evaluated against rate\nlimiting rules. Each requsts carries context like client identiy\nendpoint being accessed and timestamp that determines\nwhich rules to apply and how to track usage.\n\nisRequestAllowed(clientID,ruleID):\n return {\n passess: bool,\n remaining: number,\n resetTime: timestamp\n }\n\n\nX-RateLimit-Limit: how many call clients can make per time window.\nX-RateLimit-Remaining: no. of req left in the current window.\nX-RateLimit-Reset: rate limit resets as a Unix Timestamp.\nX-RateLimit-Retry-After: no. of seconds to wait until \nyou can make a request again without being throttled.\n\nIdentify Client?\n- User ID: JWT token present in Authorization header\n- IP Address: Present in X-Forwarded-For header\nAPI Key: X-API-Key header. Each key holder gets their own\nlimits\n\n",
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"text": "Fixed Window Counter: \n- Simplest approach divides time into fixed windows and counts requests in each window.\n- Concept: Track request count in rolling time window (e.g., last 60 seconds). Reject if exceeds limit.\n- Types: Fixed window (simpler, edge case spike), Sliding window (exact, needs log of requests)\n- PROS: Easy to understand, accurate tracking, no burst complexity, works well for simple limits\n- CONS: Requires storing request timestamps, edge case at window boundaries (fixed), higher memory usage\n- WHEN TO USE: Simple rate limiting, API quotas (requests per minute/hour), when accuracy matters more than simplicity.\n- EXAMPLE: 100 req/min → Track all requests in last 60 sec. If count >= 100, reject new ones. Old requests fall out of window.\n- EDGE CASE (FIXED WINDOW): Request limit changes at boundary → spike of 200 req/sec if 100 at :59 and 100 at :00.\n\nSliding Window Log: \n- Log of individual request timestamps for each users. When a new requests arrives, remove all timestamps older that your window.\n- Concept: Keep ordered log of all request timestamps. Check if Nth request (from end) is within window.\n- Memory: Stores all request times → High memory usage. Good for small limits or counting specific requests.\n- PROS: Most accurate, handles all edge cases, supports weighted requests\n- CONS: High memory overhead, slower lookups, difficult to scale to distributed systems\n- WHEN TO USE: Accurate quota tracking, fraud detection, when precision is critical and request volume is moderate.\n- EXAMPLE: Limit 10 requests per minute. Store timestamps [12:00:05, 12:00:15, 12:00:45, ...]. Check if request at 12:01:05 exceeds.\n\n\nSliding Window Counter: \n- Combines sliding window log and fixed window counter. More accurate than fixed window, less memory than sliding log.\n- Token Bucket: Tokens added at fixed rate (refill). \n- Each request consumes 1 token. If no tokens, request rejected. \n- Allows burst traffic up to bucket capacity, then limited by refill rate. \n- Used by AWS, Google. Most versatile for APIs.\n\n\nLeaky Bucket:\n- Concept: Requests flow into bucket. Bucket leaks (processes) at fixed rate. If bucket full, new requests dropped.\n- Stability: Output rate = leak rate. Very stable, predictable latency. No burst allowed.\n- PROS: Smooth traffic output, perfect for bandwidth-constrained systems, predictable rate\n- CONS: No burst handling, can feel unfair (requests queued but dropped if bucket full), higher latency\n- WHEN TO USE: Stream processing, fixed bandwidth allocation, when you need smooth constant rate (not bursty).\n- EXAMPLE: 10 req/sec leak rate → All requests processed at exactly 10/sec, queued if arrives faster. Dropped if queue full.\n\nREAL-WORLD IMPLEMENTATIONS\n- Google APIs: Token Bucket (with burst allowance). 100 req/sec sustained, 1000 burst capacity.\n- AWS API Gateway: Token Bucket algorithm with Reserved Concurrency model.\n- Twitter API: Leaky Bucket style (window-based counting).\n- Stripe API: Sliding Window with edge case mitigation. Detailed 429 responses.\n- Redis-based: Implement any algorithm with Lua scripts for atomic operations in distributed systems.\n- Nginx: Token Bucket (limit_req). Leaky bucket queue model (queue bursts).\n- Cloud CDNs: Token Bucket with geographic distribution (per-region limits).\n\nDistributed Environment:\n- Race Condition: locks may slow down the system.\n Lua scripts\n\n- Synchronization issues: make it stateless with the help of common redis\n",
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