Introduction In the previous article, we set up a local Kubernetes cluster, installed Istio, the rate limit, and the light service, and implemented the simplest rate limiting scenario. In real world environments, a cluster may run dozens or even hundreds of services. Each of them needs its own api rate limit configuration. In this article, we will look at how to define separate rate limit configuration for different services. To route traffic to specific services, we will rely on path-based routing. ...
Rate Limiting in Practice: Simple Scenario
Why Rate Limiting Matters Modern APIs often serve thousands or even millions of requests every minute. Without limits, this can quickly lead to problems: Abuse: a single client may flood the API with many requests. Noisy neighbor: one misbehaving user can negatively affect everyone else. Unpredictable load: sudden traffic spikes may overwhelm backend services. Rate limiting is the standard solution to these issues. By restricting how many requests a client can make in a given time window, we protect the reliability and fairness of our system. ...