Rate Limiting in Distributed Systems: Token Bucket & Leaky Bucket Algorithms Explained
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Rate Limiting in Distributed Systems: Token Bucket & Leaky Bucket Algorithms Explained
🚀 Master Rate Limiting in Distributed Systems! Learn how to control request rates across multiple servers using two powerful algorithms: Token Bucket and Leaky Bucket. This comprehensive tutorial covers everything you need to know about implementing rate limiting in production systems. 📚 What You'll Learn: • Why rate limiting is ...
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Rate Limiting Vs Throttling Both are used to control API traffic and protect systems , but let’s see how do they differ. 👉Rate Limiting : ➡️Rate limiting is used to apply a hard cap on requests say 100 per hour per user and if limit is exceeded reject excess ones. ➡️ Goal of rate limiting is to prevent abuse and enforce quotas. ➡️ Rate limit immediately blocks user request if it exceeds the limit. 👉 Throttling: ➡️ Throttling is used to slow down request or queue them to maintain a steady flow.
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Sushant Kumbhar on Instagram: "💡 Answer: To restrict how many times a specific method can be invoked by any client to N times per minute, we can design a Rate Limiting Mechanism. This ensures system stability, fair resource usage, and prevents abuse (like DoS attacks or excessive API calls). ⚙️ 1️⃣ Algorithm Choice We can use one of the following algorithms: Token Bucket → Allows short bursts while enforcing average rate limit. Leaky Bucket → Smooths out request flow and enforces a constant rat
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