what to do when Polars runs out of memory when collecting a JSON file

What should I do when Polars runs out of memory while collecting a JSON file, and how can I optimize memory usage in such scenarios?

When Polars runs out of memory while collecting a JSON file, you should consider the following steps to optimize memory usage:

Read in Chunks: Instead of reading the entire JSON file at once, try reading it in smaller chunks. This can help reduce memory consumption.

Use Lazy Evaluation: Polars supports lazy evaluation, which means you can define operations on the data without actually loading it into memory. This can help manage memory usage more effectively.

Filter Data Early: Apply filters and conditions as early as possible in your data processing pipeline to reduce the amount of data that needs to be held in memory.

Optimize Data Types: Ensure that you are using the most memory-efficient data types for your columns. For example, use smaller numeric types if possible.

Garbage Collection: Make sure that Python's garbage collection is running properly to free up memory from objects that are no longer needed.

Increase System Memory: If possible, increase the physical memory (RAM) available to your system. This can provide more headroom for memory-intensive operations.

By following these steps, you can help manage and optimize memory usage when working with large JSON files in Polars.