In this episode, we're taking a big step forward in handling large CSV uploads efficiently! The main focus is on chunking through CSV records—which means breaking up potentially massive sets of CSV data into smaller, more manageable batches (or "chunks") that can be processed one at a time, without ever loading the whole file into memory. This is super useful if your users might upload files with hundreds of thousands or even millions of rows, where loading everything at once would be a nightmare for your server.
We start by looking at the CSV import process and seeing firsthand what kind of data we get back after importing. We play around a bit with iterator methods, and I show you how each record can be looped over one at a time—cool, but not quite what we want, especially if we need to process lots at once without blowing up memory usage.
The hero of this episode is our new ChunkIterator class. We go through building it out, and then use it to create a generator that will yield chunks of records (like groups of 10, 100, or 1000 at a time, whatever you choose). This way, you're never hanging on to more records in memory than you need. We run some tests to see this in action with a sample CSV file, and verify that chunks are being created exactly as expected.
Finally, we talk about how you can tweak the chunk size for your own needs, and why this approach is ideal for processing super large CSVs with background jobs. By the end, you've got the code necessary to split any CSV import into safe, memory-friendly batches—setting the stage for queuing up each chunk as a job in the next episode. If you’re still wondering about how generators work, now's a good time to give them a quick read, as they’re at the core of this chunking approach!