The Token Bucket Algorithm: Defending Your API from Hackers
Remember yesterday on Day 16 when we built a massive, scalable cache using Consistent Hashing? That was awesome. But here is the brutal truth: if we don't protect it today, a single bot will crash that entire system in seconds.
Welcome to Day 17 of "100 Days of System Design." Today, we are putting on our security hats and building the ultimate shield for our architecture: a Rate Limiter.
In this fast-paced insider class, we cover:
The 'Why' (Beyond Just Hackers) 🛑
Why are rate limiters crucial? Yes, they prevent massive DDoS attacks, but they also control costs for expensive third-party APIs and prevent "server starvation"—where one greedy user eats up all the server resources, leaving normal users trapped in loading screens.
The Visual Walkthrough: HTTP 429 🚦
Imagine our vibrant minimalist vector art. A glowing neon API Gateway is peacefully taking in traffic (normal users as glowing green orbs).
Suddenly, a malicious user—a giant, spikey red orb—fires 10,000 requests at once to scrape your data.
Boom. The Gateway instantly throws up a glowing shield: an 'HTTP 429 Too Many Requests' firewall. It turns the red orbs to dust on impact, while seamlessly letting the normal green orbs pass straight through.
Where Does It Go? 🗺️
Reality check: Where do you put this shield? You can't put it in the client code (hackers just bypass it), and putting it deep inside your application servers wastes resources. The industry standard is to put it right at the front door: inside the API Gateway.
The Token Bucket Algorithm 🪣
Plot twist: The logic behind the shield is surprisingly simple. We break down the famous Token Bucket algorithm. Imagine a literal bucket that constantly drips digital coins at a steady rate. Every time a user makes a request, it costs one coin. If the bucket runs out of coins, their request gets dropped until the bucket refills.
The Redis Fix ⚡
If millions of requests are hitting the bucket, where do we store the coin counters? A standard SQL database is way too slow because writing to disk takes forever. Instead, we use memory. We dump the counters into a lightning-fast cache using Redis INCR commands.
Now your API is bulletproof. But how do we track which servers are dead and which are alive?
Tomorrow for Day 18, we build the Heartbeat Mechanism. Follow along! 👇
References:
System Design Interview by Alex Xu (Chapter 4)
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