Distributed Service Rate Limiting, Degradation, and Caching Strategies

When a product interface experiences a sudden traffic surge, typical e‑commerce systems protect the service using caching, rate limiting, and degradation techniques.

Rate limiting controls request concurrency or request rate within a time window, rejecting, queuing, or degrading traffic once limits are reached. Common algorithms include the leaky bucket, token bucket, and sliding window.

Leaky bucket discards requests when the bucket is full, while token bucket allows bursts by accumulating tokens. Sliding window divides a period into sub‑intervals and triggers limits when the sum exceeds a threshold.

At the ingress layer, Nginx implements leaky‑bucket based limiting via the ngx_http_limit_req_module , using client IP or User‑Agent as identifiers.

Within application code, Java’s Semaphore can enforce a maximum concurrent count, as shown in the example below.

private final Semaphore permit = new Semaphore(40, true);

public void process() {
    try {
        permit.acquire();
        // TODO: handle business logic
    } catch (InterruptedException e) {
        e.printStackTrace();
    } finally {
        permit.release();
    }
}

For distributed rate limiting, message queues or Redis lists can act as buffering queues based on the leaky‑bucket principle, consuming requests according to service throughput.

Service degradation provides fallback strategies when traffic spikes exceed capacity, such as postponing or pausing non‑critical features, rejecting excess requests (random, oldest, or non‑core), and scaling consumer services.

Data caching strategies include using distributed locks, caching hot data during spikes, routing requests to cached data, and asynchronously processing results via message queues.

Cache consistency challenges for inventory can be addressed with read‑write separation using Redis Sentinel, load‑balancing cache shards, or page‑cache techniques that aggregate short‑term writes.