I am working on a Python script that processes large JSON files (~500MB) to extract specific data and save it into a CSV format. The script works, but it is slow, especially for files with nested JSON objects. Here’s a simplified version of my code:

import json  
import csv  

def process_json(file_path):  
    with open(file_path, 'r') as json_file:  
        data = json.load(json_file)  

    with open('output.csv', 'w', newline='') as csv_file:  
        writer = csv.writer(csv_file)  
        writer.writerow(['field1', 'field2', 'field3'])  

        for record in data:  
            writer.writerow([record['field1'], record['field2'], record['field3']])  

process_json('large_file.json')  

I’ve tried using json.load() and then iterating through the data, but it seems to consume a lot of memory and time. I came across the json module’s iterparse, but I’m not sure how to apply it to my use case.

My questions are:

1. Is there a more memory-efficient way to process large JSON files in Python?
2. Would libraries like pandas or ujson provide significant performance benefits for this task?
3. How can I handle nested JSON objects more efficiently while writing to CSV?

Environment:

• Python 3.10
• Windows 11