Rate Limiting Strategies for E-commerce APIs

Rate limiting is essential for any production API. Without it, a single misconfigured client can bring down your entire service. Here's how to implement rate limiting that protects your infrastructure while providing a good developer experience.

Why Rate Limiting Matters

For API Providers - **Protect infrastructure**: Prevent any single client from consuming disproportionate resources - **Ensure fairness**: Give all customers equal access to capacity - **Cost control**: Prevent runaway costs from unexpected traffic spikes - **Abuse prevention**: Stop scrapers and bad actors

For API Consumers Understanding rate limits helps you: - Design your application to stay within limits - Implement proper retry logic - Choose the right pricing tier for your needs

Common Rate Limiting Algorithms

Token Bucket The most popular approach. Each client has a "bucket" that fills with tokens at a steady rate. Each request consumes a token. When the bucket is empty, requests are rejected.

Pros: Allows bursts, simple to implement Cons: Doesn't account for request complexity

Sliding Window Track the number of requests in a rolling time window (e.g., last 60 seconds).

Pros: Smooth rate enforcement Cons: More memory-intensive

Fixed Window Count requests in fixed time windows (e.g., per minute).

Pros: Simplest to implement Cons: Allows 2x burst at window boundaries

Implementing Client-Side Rate Limiting

When using EcomSource or any rate-limited API, implement client-side throttling:

class RateLimiter {
  constructor(maxRequests, windowMs) {
    this.maxRequests = maxRequests;
    this.windowMs = windowMs;
    this.requests = [];

async waitForSlot() { const now = Date.now(); this.requests = this.requests.filter(t => now - t < this.windowMs); if (this.requests.length >= this.maxRequests) { const waitTime = this.requests[0] + this.windowMs - now; await new Promise(resolve => setTimeout(resolve, waitTime)); } this.requests.push(Date.now()); } } ```

HTTP Headers

• `X-RateLimit-Limit`: Your max requests per window
• `X-RateLimit-Remaining`: Requests remaining in current window
• `X-RateLimit-Reset`: When the window resets (Unix timestamp)
• `Retry-After`: Seconds to wait before retrying (on 429 responses)

Best Practices

1. 1Always respect 429 responses: Back off and retry
2. 2Use exponential backoff: Don't hammer the API on failure
3. 3Cache aggressively: Don't re-request data that hasn't changed
4. 4Use batch endpoints: One batch request is better than 100 individual requests
5. 5Monitor your usage: Track how close you are to limits