This article explains four common rate‑limiting algorithms—fixed‑window counter, sliding window, leaky bucket, and token bucket—detailing their mechanisms, advantages, drawbacks such as the critical‑window problem, and how they can be implemented simply using Redis in backend systems.
This article explains the purpose, scenarios, and detailed implementations of common rate‑limiting algorithms—including counter (fixed window), sliding window, token bucket, and leaky bucket—highlighting their advantages, drawbacks, and sample Python code for each.
The article introduces the concept of rate limiting, explains why it is needed both offline (e.g., crowded theme parks) and online (e.g., flash sales), and details four common algorithms—counter, sliding window, leaky bucket, and token bucket—along with their implementation considerations and trade‑offs.
This article explains the fundamentals of rate limiting for high‑concurrency systems, covering why it is needed, the conditions that trigger it, and detailed introductions to the three main algorithms—counter (fixed and sliding windows), leaky bucket, and token bucket—along with their advantages, drawbacks, and sample pseudo‑code implementations.
During high‑traffic scenarios, services can become unavailable, so implementing rate‑limiting techniques like token bucket, leaky bucket, and counter algorithms—illustrated with Java code examples using Guava RateLimiter, AtomicInteger, and Semaphore—helps smooth bursts, control concurrency, and prevent system overload.
