Natuurlijk! Hieronder vind je een uitgebreide strategie voor rate limiting die jouw REST API beschermt tegen piekverkeer en burst-verkeer, rekening houdend met jouw infrastructuur en doelen. **1. API Kenmerken en Burst-verkeer** - **Type API:** REST - **Burst-verkeer:** 100-150 verzoeken per dag tussen 12:00 en 14:00 uur - **Doel:** Beschermen tegen plotselinge pieken, voorkomen overbelasting, en zorgen voor hoge beschikbaarheid en lage latency **2. Infrastructuur en caching** - **Kubernetes cluster** - **Redis caching** (voor stateful rate limiting en caching) **3. Belangrijkste doelen** - **Beschikbaarheid behouden:** Geen downtime door overbelasting - **Latency minimaliseren:** Zo min mogelijk vertraging door rate limiting - **Kosten beheersen:** Efficiënt gebruik van resources en infrastructuur --- ### 4. Technische Aanbevelingen en Plan #### A. Rate Limiting Strategie **a. Per-gebruiker/IP rate limiting** - Beperk het aantal verzoeken per gebruiker/IP gedurende korte tijd (bijvoorbeeld 1 minuut) - Bijvoorbeeld: max 10 verzoeken per minuut, met een burst capaciteit van 20 **b. Dagelijkse quota voor piekuren** - Toewijzen van een maximum aantal verzoeken voor de periode 12:00-14:00 uur - Bijvoorbeeld: 150 verzoeken per gebruiker/IP voor die 2 uur **c. Dynamic rate limiting op basis van verwachte burst** - Toepassen van een 'token bucket' algoritme dat toelaat korte burst-verzoeken (bijvoorbeeld 20 verzoeken ineens), maar daarna een limiet op de gemiddelde snelheid --- #### B. Implementatie met Redis Gebruik Redis als centrale opslag voor rate limiting counters, omdat Redis snel en betrouwbaar is en goed integreert met Kubernetes. **Techniek:** - Elke client/IP krijgt een unieke sleutel in Redis, bijvoorbeeld: `rate_limit:{client_ip}` - Gebruik een token bucket algoritme: ```plaintext - Bij elke request: - Haal de huidige tokens op uit Redis (bv. 20 tokens max) - Indien tokens beschikbaar: - Verlaag tokens met 1 - Laat request toe - Anders: - Weiger request of geef een 429 Too Many Requests - Herlaad tokens na een vaste interval (bijvoorbeeld 1 token per seconde) ``` **Implementatie in Redis:** - Gebruik Lua-scripts voor atomiciteit - Stel een TTL in Redis-sleutels in, zodat oude data automatisch wordt verwijderd **Voorbeeld Lua-script:** ```lua local tokens_key = KEYS[1] local max_tokens = tonumber(ARGV[1]) local refill_rate = tonumber(ARGV[2]) -- tokens per seconde local current_time = tonumber(ARGV[3]) local tokens = tonumber(redis.call("GET", tokens_key)) if not tokens then tokens = max_tokens end local last_refill = tonumber(redis.call("GET", tokens_key .. ":timestamp")) if not last_refill then last_refill = current_time end local delta = current_time - last_refill local refill = delta * refill_rate tokens = math.min(tokens + refill, max_tokens) if tokens >= 1 then tokens = tokens - 1 redis.call("SET", tokens_key, tokens) redis.call("SET", tokens_key .. ":timestamp", current_time) return 1 else return 0 end ``` --- #### C. Kubernetes & Nginx Ingress - Plaats een rate limiting filter in je Nginx Ingress controller met behulp van Lua of native rate limiting modules. - Configureer `limit_req_zone` en `limit_req` voor korte-termijn limieten. - Overweeg custom Lua scripts voor meer granulariteit en dynamische limieten. --- ### 5. Valkuilen en risico's - **Verkeerde configuratie van limieten:** Te strenge limieten leiden tot onterechte blokkades; te losse limieten bieden onvoldoende bescherming. - **Redis bottleneck:** Zorg dat Redis voldoende resources heeft en dat Lua scripts efficiënt zijn. - **Stateful vs stateless:** Bij hoge schaalbaarheid moet je zorgen dat rate limiting consistent is over alle pods. - **Burst explosie buiten verwachte tijden:** Monitoring is nodig om limieten aan te passen. --- ### 6. Monitoring en Aanpassingen - Gebruik Prometheus en Grafana om verkeer en rate limiting hits te monitoren. - Pas limieten aan op basis van echte data. - Automatiseer alerts voor abnormale pieken. --- ### 7. Voorbeeld implementatie Hier is een schematische flow: 1. Een request komt binnen 2. Nginx/Ingress roept Lua script aan dat: - De Redis-sleutel controleert - Tokens bijvult op basis van tijd - Bepaalt of request door mag gaan 3. Bij overschrijding: 429 Too Many Requests --- ### Samenvatting - Gebruik een token bucket algoritme in Redis voor dynamisch en flexibel rate limiting - Implementeer korte-termijn limieten via Nginx of API gateway - Stel dagelijkse quota in voor piekuren (12:00-14:00) - Monitor en pas limieten aan op basis van gebruiksdata - Zorg dat Redis voldoende resources heeft en dat Lua scripts efficiënt worden gebruikt --- Wil je dat ik een concrete codevoorbeeld of configuratie voor je Kubernetes/Nginx setup samenstel?

Of course. Based on your requirements, here is a comprehensive rate limiting strategy designed for your specific environment and traffic patterns. ### **1. Main Goals for the Strategy** Your primary goals, derived from your description, are: 1. **Maintain Availability:** Ensure the API remains responsive and online for all users during the predictable daily burst window (12:00-14:00). 2. **Minimize Latency:** Prevent increased response times for legitimate users by efficiently handling bursts without degrading performance. 3. **Control Costs:** Leverage your existing infrastructure (Kubernetes, Redis) effectively to avoid the need for unnecessary over-provisioning of resources. --- ### **2. Detailed Rate Limiting Plan** Given your predictable traffic pattern (a known 2-hour burst window), a static, overly strict rate limit would be inefficient. A **multi-algorithm approach** is ideal. #### **Recommended Algorithms:** 1. **Sliding Window Log with Redis (Primary Algorithm):** * **Why?** It's the most accurate algorithm for enforcing strict limits over a rolling period. It perfectly handles bursts while preventing clients from exceeding the limit by spacing requests. * **How it works:** For each user or IP, we store a sorted set in Redis of their recent request timestamps. For a new request, we count the number of requests in the last `N` seconds. If it's under the limit, the request is allowed and the new timestamp is added. 2. **Token Bucket (Complementary for very short bursts):** * **Why?** It's computationally simpler and can be useful for extremely rapid, small bursts within the larger window. However, the Sliding Window is superior for your main goal. **Given your infrastructure, the Sliding Window Log using Redis is the strongest recommendation.** #### **Technical Recommendations & Architecture:** **A. Define the Scope and Limits:** * **Identify the "User":** Decide what constitutes a user. For a public API, it's often the **Client IP address**. For a user-authenticated API, it's the **User ID** or **API Key**. Using API Keys is more precise and prevents one abusive user from affecting others sharing an IP (e.g., behind a corporate NAT). * **Set the Limits:** Based on "Bursts of 100-150 requests daily," let's define a sensible limit. * **Limit:** `150 requests per 2 hours` (your entire burst window). * **But also add a short-term burst limit:** `30 requests per minute`. This prevents a single client from firing all 150 requests in the first 10 seconds, which could still overwhelm your application pods. **B. Implementation Layer:** The best place to implement this is in an **API Gateway** or **Ingress Controller** sitting in front of your application pods. * **Recommended Tool: Nginx Ingress Controller for Kubernetes.** It has built-in rate-limiting capabilities (`nginx.ingress.kubernetes.io/limit-rpm` and `nginx.ingress.kubernetes.io/limit-rps`) that use a shared memory zone. However, for a distributed Kubernetes cluster, you need a distributed data store. * **Use the `lua-resty-redis` library** with the Nginx Ingress Controller. This allows Nginx to connect directly to your Redis cluster for rate limit counting, making it distributed and consistent across all your ingress pods. **C. Implementation Example (Conceptual):** 1. **Redis Data Structure (Sorted Set):** * **Key:** `ratelimit:<user_identifier>` (e.g., `ratelimit:192.168.1.1` or `ratelimit:api_key_xyz`) * **Value:** A Sorted Set where the *score* is the Unix timestamp of the request and the *member* is a unique value for that request (e.g., also the timestamp or a UUID). 2. **Pseudocode Logic (to be implemented in Nginx via Lua):** ```lua -- Define limits local limit_per_minute = 30 local limit_per_two_hours = 150 local now = tonumber(ngx.now()) -- User identifier (simplified example) local user_id = ngx.var.remote_addr -- Connect to Redis local redis = require "resty.redis" local red = redis:new() red:connect("redis-host", 6379) -- Define time windows local two_hour_window = 2 * 60 * 60 -- 2 hours in seconds local one_minute_window = 60 -- 1 minute in seconds -- 1. Check 2-hour sliding window -- Remove all requests older than 2 hours red:zremrangebyscore(user_id, 0, now - two_hour_window) -- Count requests in the last 2 hours local two_hour_requests = red:zcard(user_id) if two_hour_requests >= limit_per_two_hours then ngx.header["Retry-After"] = (red:zrange(user_id, 0, 0)[1] + two_hour_window) - now ngx.status = 429 ngx.say("Rate limit exceeded. Please try again later.") return ngx.exit(429) end -- 2. Check 1-minute sliding window (same logic) red:zremrangebyscore(user_id, 0, now - one_minute_window) local one_minute_requests = red:zcard(user_id) if one_minute_requests >= limit_per_minute then ngx.header["Retry-After"] = 60 ngx.status = 429 ngx.say("Too many requests. Please slow down.") return ngx.exit(429) end -- 3. If checks pass, add the current request and allow it red:zadd(user_id, now, now) -- Use 'now' as both score and member red:expire(user_id, two_hour_window) -- Clean up key automatically after 2h -- Allow the request to proceed to the backend service ``` *(Note: This is a conceptual example. Production code requires connection pooling, error handling, and more robust unique member generation.)* 3. **Kubernetes Ingress Annotation Example (using Nginx's built-in, non-Redis limits):** While not distributed, for simplicity you could start with this. It uses a shared memory zone across the ingress pods' instances of Nginx. ```yaml apiVersion: networking.k8s.io/v1 kind: Ingress metadata: name: my-api-ingress annotations: nginx.ingress.kubernetes.io/limit-rpm: "30" # 30 requests per minute nginx.ingress.kubernetes.io/limit-rph: "150" # 150 requests per hour (closest to 2h) nginx.ingress.kubernetes.io/limit-whitelist: "10.0.0.0/8" # Optional: whitelist internal IPs spec: rules: - host: api.mycompany.com http: paths: - path: / pathType: Prefix backend: service: name: my-api-service port: number: 80 ``` **D. HTTP Response Headers:** Always inform the client about their rate limit status. This is good practice and helps developers debug. * `X-RateLimit-Limit: 150` * `X-RateLimit-Remaining: 125` * `X-RateLimit-Reset: 1648825200` (Unix timestamp when the limit resets) * On failure, return **HTTP 429 Too Many Requests** with a `Retry-After` header. --- ### **3. Potential Pitfalls and Mitigations** | Pitfall | Description | Mitigation | | :--- | :--- | :--- | | **Race Conditions** | Concurrent requests from the same user might all check the limit before any of them increment the count, allowing a few extra requests through. | Use Redis atomic transactions (`MULTI/EXEC`) and Lua scripting to ensure the check-and-increment operation is atomic. | | **Redis as a Single Point of Failure** | If your Redis cluster goes down, your API could become unavailable if the rate limit code is strict. | Implement a fallback mechanism. If Redis is unreachable, log the error and **allow the request to proceed**. Availability is more important than strict rate limiting. Monitor Redis health closely. | | **Storage Overhead** | Storing a sorted set for every user/IP could use significant memory over time. | **Set TTLs on all Redis keys** (e.g., `EXPIRE key 7200` for a 2-hour TTL) to ensure they are automatically cleaned up after the time window passes. | | **Misidentifying Users** | Rate limiting by IP can punish groups of users behind a NAT (e.g., an office building). | Where possible, use API keys for authenticated endpoints for fairer and more precise limiting. | | **Caching Layer Bypass** | The rate limiter protects your API infrastructure, but if traffic bursts are hitting uncached endpoints, your database could still be at risk. | Ensure your Redis caching strategy is robust. Use it to cache common responses from the 12:00-14:00 burst period. The rate limiter and cache should work together. | --- ### **4. Monitoring and Analytics** * **Monitor Redis:** Track memory usage, CPU, and latency of your Redis instance. Set alerts for high memory usage or connectivity issues. * **Log 429 Responses:** Aggregate and dashboard the number of 429 responses. A sudden spike could indicate a misconfigured client or an attack. * **Track Usage:** Use the data in Redis to analyze usage patterns. Which clients are the heaviest users? Does the traffic align with the expected 12:00-14:00 window? This data can help you refine your limits. By implementing this strategy, you will effectively smooth out the daily traffic burst, protecting your backend services (Kubernetes pods and databases) from being overwhelmed, thus maintaining availability and low latency for all users while leveraging your current infrastructure.