Dealing with large file sizes in Python with Azure Function App Blob Trigger

Currently, I am utilizing Azure function apps (Python) with a blob trigger to handle CSV processing and transferring the records to an event hub. The existing code is functional for files up to 50 rows, which was developed by following standard documentation. However, I am interested in understanding the best approach for scenarios where the file size can reach several gigabytes.

When dealing with larger files, will the entire file be sent to the Azure function all at once? In situations where the file needs to be processed in fixed-size chunks or line by line, does Azure's trigger concept support this functionality?

I am seeking guidance on any potential approaches or sample code in Python that can address this issue while avoiding loading the complete file into the memory of the Azure function container.

Answer №1

If you find yourself dealing with a large file that is too cumbersome for a typical web request, it may be more efficient to upload it to an object storage system (like Azure Blob Storage) and then provide the function with the new destination address.

AMQP messages (which power Event Hub behind the scenes) are better suited for handling small amounts of data. You could potentially treat each line or block of lines in your CSV as a separate message, but this approach would heavily depend on your specific scenario.

In this case, you should consider using an object that supports streaming rather than loading the entire file at once, like using BlockBlobService - check out this example for guidance on how to implement this.

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