Last modified: Jan 30, 2025 By Alexander Williams

Python httpx.stream_async() Guide: Stream Async Data

Python's httpx library is a powerful tool for making HTTP requests. One of its standout features is the ability to stream data asynchronously using httpx.stream_async().

This method is particularly useful when dealing with large datasets or real-time data streams. It allows you to process data as it arrives, without waiting for the entire response to be downloaded.

What is httpx.stream_async()?

The httpx.stream_async() function is used to asynchronously stream data from an HTTP response. It is part of the httpx library, which is designed to be a fully asynchronous HTTP client.

This method is ideal for scenarios where you need to handle large files or continuous data streams. It ensures that your application remains responsive and efficient.

How to Use httpx.stream_async()

To use httpx.stream_async(), you first need to install the httpx library. You can do this using pip:


    pip install httpx
    

Once installed, you can start streaming data asynchronously. Here's a basic example:


    import httpx
    import asyncio

    async def fetch_data():
        async with httpx.AsyncClient() as client:
            async with client.stream("GET", "https://example.com/large-data") as response:
                async for chunk in response.aiter_bytes():
                    print(chunk)

    asyncio.run(fetch_data())
    

In this example, we use httpx.stream_async() to fetch data from a URL. The data is processed in chunks as it arrives, ensuring efficient memory usage.

Example: Streaming JSON Data

You can also use httpx.stream_async() to stream JSON data. This is useful when dealing with APIs that return large JSON objects.


    import httpx
    import asyncio

    async def fetch_json():
        async with httpx.AsyncClient() as client:
            async with client.stream("GET", "https://api.example.com/large-json") as response:
                async for chunk in response.aiter_json():
                    print(chunk)

    asyncio.run(fetch_json())
    

This code fetches JSON data from an API and processes it asynchronously. The aiter_json() method is used to iterate over the JSON chunks.

Handling Errors

When streaming data, it's important to handle errors gracefully. The httpx.stream_async() method allows you to catch exceptions and retry requests if necessary.


    import httpx
    import asyncio

    async def fetch_data_with_retry():
        async with httpx.AsyncClient() as client:
            try:
                async with client.stream("GET", "https://example.com/large-data") as response:
                    async for chunk in response.aiter_bytes():
                        print(chunk)
            except httpx.RequestError as e:
                print(f"Request failed: {e}")
                # Retry logic here

    asyncio.run(fetch_data_with_retry())
    

In this example, we catch any httpx.RequestError exceptions and print an error message. You can add retry logic to handle transient errors.

Conclusion

The httpx.stream_async() method is a powerful tool for streaming data asynchronously in Python. It is ideal for handling large datasets, real-time data streams, and APIs that return large JSON objects.

By using httpx.stream_async(), you can ensure that your application remains responsive and efficient. It is a must-have tool for any developer working with HTTP requests in Python.

For more information on related topics, check out our guides on streaming JSON data, streaming bytes, and streaming text.