polling with exponential backoff

Imagine you're waiting for a package to be delivered, but every time you check on it, the delivery person is nowhere to be found. You start checking more frequently, hoping to catch them at the right moment. But what if instead of checking every 5 minutes, you waited 10 minutes, then 20 minutes, and so on? Eventually, you'd be waiting for hours, but there's a good chance that by then, your package would have arrived.

This is similar to how polling with exponential backoff works in the world of APIs. It's a technique used when real-time updates are not feasible or cost-effective, especially for free-tier friendly projects. Let's dive into this concept and explore some code examples.

The Technique

Polling with exponential backoff involves sending repeated requests to an API at increasingly longer intervals, based on previous failed attempts. This approach helps prevent overwhelming the server with too many requests in a short period. Here's a simple example of how it works:

1. Initial request: Send a request to the API.
2. Failure: If the request fails, wait for a short interval (e.g., 500ms) before sending another request.
3. Next failure: If the next request also fails, increase the waiting interval by a factor (e.g., 2x or 4x). This is where exponential backoff comes in.

Here's some sample code in TypeScript to demonstrate this concept:

interface ApiConfig {
  baseUrl: string;
  retryDelay: number; // initial delay
  maxAttempts: number;
}

class PollingClient {
  private config: ApiConfig;

  constructor(config: ApiConfig) {
    this.config = config;
  }

  async pollApi(): Promise<any> {
    let attempt = 1;
    while (attempt <= this.config.maxAttempts) {
      try {
        const response = await fetch(`${this.config.baseUrl}/api/data`);
        return response.json();
      } catch (error) {
        console.error(`Attempt ${attempt} failed:`, error);
        if (attempt < this.config.maxAttempts) {
          // Exponential backoff
          const delay = Math.min(this.config.retryDelay * 2 ** (attempt - 1), 30000); // 30s max delay
          await new Promise(resolve => setTimeout(resolve, delay));
          attempt++;
        } else {
          throw error; // Max attempts reached, re-throw the error
        }
      }
    }
  }
}

This example uses a simple exponential backoff strategy with a maximum delay of 30 seconds.

Concrete Examples

1. Real-time updates for a chat application: Imagine a chat app that needs to fetch new messages from an API every second. However, the free-tier plan limits the number of requests per minute. Using polling with exponential backoff, you can send requests at increasingly longer intervals (e.g., 2s, 4s, 8s) while still providing a responsive user experience.
2. Monitoring server status: A monitoring tool might need to check on the health of multiple servers every few minutes. Polling with exponential backoff allows for efficient resource usage by increasing the interval between checks when no changes occur.
3. File upload progress tracking: When uploading large files, polling with exponential backoff can be used to fetch progress updates from an API at increasingly longer intervals (e.g., 10s, 30s) while ensuring that the server doesn't become overwhelmed.

When NOT to Use It

Polling with exponential backoff is not suitable for real-time applications where immediate updates are critical. In such cases, WebSockets or other push-based technologies might be more effective. Additionally, if your API has a high request volume and you're concerned about the server load, consider using a more efficient approach like long-polling (see below).

Related Apiary Lessons

• Long-Polling with Jitter: A related technique that uses long-polling instead of exponential backoff for more efficient resource usage.
• WebSockets and Real-Time Communication: Explore the world of WebSockets and push-based technologies for real-time updates.

Conclusion

Polling with exponential backoff is a reliable technique for handling periodic requests to APIs, especially when real-time updates are not feasible. By gradually increasing the interval between requests, you can prevent overwhelming the server while still providing an efficient user experience. Remember to consider your specific use case and adjust this approach as needed.

A busy bee's motto: "Polling with exponential backoff: don't get stuck in a honeycomb of retries."