How to efficiently handle large JSON files with multiple nested structures in Python 3.10?
After trying multiple solutions online, I still can't figure this out. I'm working on a project where I need to process a large JSON file (approximately 500 MB) containing multiple nested structures. The JSON structure is deep and complex, which makes it challenging to extract specific data points efficiently without running into memory issues. When I attempt to load the entire file at once using `json.load()`, I encounter a `MemoryError` due to the file size. I've tried using the built-in `json` module like this: ```python import json with open('large_file.json', 'r') as file: data = json.load(file) # This raises MemoryError ``` To work around this, I attempted to read the file in chunks using `ijson`, which is supposed to handle large JSON files more gracefully. However, I'm struggling to properly navigate the nested structures. Here’s a snippet of how I’m using `ijson`: ```python import ijson with open('large_file.json', 'r') as file: for item in ijson.items(file, 'item'): # Attempting to access 'item' print(item) # Outputs partial data as expected ``` The question is that the JSON structure has multiple layers, and I need to access specific fields within this hierarchy efficiently. For instance, the JSON might look something like this: ```json { "items": [ { "id": 1, "details": { "name": "Item 1", "properties": { "color": "red", "size": "M" } } }, { "id": 2, "details": { "name": "Item 2", "properties": { "color": "blue", "size": "L" } } } ] } ``` What I need advice on is how to traverse this nested structure correctly while still maintaining performance. Is there a recommended way to filter or extract specific fields without loading the entire JSON file into memory? Any help with this would be greatly appreciated! I'm working on a CLI tool that needs to handle this. What am I doing wrong?