Speed Up Your Async Requests in Python: Mastering aiohttp for Efficient Data Collection

Discover how to optimize async requests in Python using `aiohttp` to scrape large datasets efficiently. Eliminate bottlenecks and enhance speed for seamless data processing.
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Speed Up Your Async Requests in Python: Mastering aiohttp for Efficient Data Collection

When it comes to downloading data at scale, especially when dealing with millions of records, efficiency is critical. If you've ever tried to scrape or download a massive dataset using Python, you may have found yourself grappling with slow speeds, connection limitations, and frustrating errors. In this guide, we'll explore how to effectively speed up your asynchronous requests in Python using the aiohttp library. By understanding the bottlenecks and implementing smarter solutions, you'll be able to handle requests more efficiently and save valuable time.

The Problem at Hand

Imagine you need to scrape 50 million log records from a website. Attempting to download all this data in one go can lead to monumental slowdowns or even total failures in requests. A current setup may only allow downloading around 20,000 records at a time, taking several minutes to complete. This is not only time-consuming but can lead to hitting limits set by the server or your own connection.

The main issues are often related to the number of simultaneous connections and handling the sheer volume of coroutines when processing multiple requests. Understanding these limitations is the first step toward finding a viable solution.

The Solution Breakdown

1. Identify Bottleneck: Not Enough Simultaneous Connections

The first bottleneck often arises from the total number of simultaneous connections allowed in the TCP connector. The default limit for aiohttp.TCPConnector is set to 100 connections. For many users, especially on macOS, increasing this limit can yield significant speed improvements. Here's how you can accomplish this:

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By doubling the connection limit, you should notice a marked decrease in processing time. For example, increasing the limit from 100 to 200 on macOS can reduce the time taken for a test run from 58 seconds to 33 seconds for 20,000 records.

2. Handling Large Requests with Async Generators

When working with extremely large datasets, creating millions of coroutines all at once can overwhelm the event loop, causing significant slowdowns. Instead, we can use async generators to defer the creation of coroutines. Here’s how it works:

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This function allows us to create coroutines as needed instead of all at once, which keeps the event loop much cleaner and more responsive.

3. Controlling Concurrency

Next, we need a way to control how many concurrent requests are being made at any time. This is key to ensuring efficient use of resources while still maximizing throughput. Here’s an example of how to implement a custom concurrency manager:

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4. Updating Your Fetch Logic

Now that we have our generator and concurrency manager set up, we can update the main fetch() function to utilize these features, while still optimizing our request handling:

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5. Additional Considerations

While the above adjustments will help speed things up, here are a couple of additional limitations to keep in mind:

Memory Constraints: You may run out of memory if you're collecting extensive responses all at once. Consider processing each response as it finishes to manage memory better.

Execution Time: Even with optimizations, processing a large number of requests may still take considerable time. Use profiling tools to monitor performance and adjust as necessary.

6. Fixing Common Errors

Finally, ensure all pieces of your code are working correctly. For instance, remember to return the data in the do_get function: