How to Combine Multiple API Requests with aiohttp in Python Efficiently

Learn how to optimize your API requests in Python using `aiohttp` and batching techniques to enhance performance and reduce wait times.
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Combining Multiple API Requests with Python aiohttp

APIs often allow you to request data in various ways, and one common challenge developers face is how to optimize those requests, especially when they're rate-limited. If you're using Python's aiohttp, you might want to leverage the power of asynchronous programming to combine multiple API requests into a single batch call.

In this guide, we'll address a specific problem: how to modify an API GET request function so that it can handle multiple object IDs efficiently without changing its signature. By the end of this guide, you'll have a robust solution that improves the performance of your API calls.

The Problem

Imagine you have an API endpoint that takes one or more object IDs to return their metadata. For instance, the URL http://example.com/api/metadata?id=1&... allows you to retrieve information about multiple IDs in one request. However, if you’re making individual calls for each ID as shown in the code snippet below, you may run into performance bottlenecks:

[[See Video to Reveal this Text or Code Snippet]]

With this setup, you are calling the API for each ID separately which is inefficient, especially when the API is rate-limited per request rather than per ID. The goal here is to batch multiple requests while keeping the function's signature consistent.

The Solution: Batching API Requests

To implement a more efficient solution, we will create a batching mechanism using an asynchronous class called Batcher. The idea is to wait until either a maximum number of IDs (e.g., 50) is reached or a timeout period (e.g., 10 seconds) has elapsed before making the API request.

Step 1: Implementing the Batcher Class

Here’s a breakdown of the Batcher class which will serve as the foundation for our batching logic:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Extending the Batcher Class

Next, we create a subclass of Batcher, named get_metadata, which will handle the specifics of fetching metadata in batches. This class incorporates the logic for forming the HTTP request:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

With this implementation, you are now capable of efficiently handling multiple API requests in one batch. Here's a quick recap of the key features of the solution:

Efficient Batching: Waits for either a specified number of IDs or a timeout before making a single API call.

Consistent Function Signature: Maintains the original function signature, so compatibility with existing code is preserved.

Asynchronous Processing: Maximizes the performance benefits of async code with aiohttp.

By applying this pattern, you'll improve your application's performance and reduce unnecessary load on the API. Start integrating this batching technique into your codebase today!