[Python] How can I efficiently process a large JSON file (e.g., >1GB) in Python without...

I'm working with a very large JSON file (approximately 1.5GB in size) that I need to process in Python. I need to extract specific data from the file and perform some calculations on it.

The problem is that loading the entire JSON file into memory using json.load() or similar methods causes my script to crash due to memory limitations.

I'm looking for an efficient way to process this file, ideally without loading the entire file into memory at once. I've considered using methods like:

• Streaming the file: Reading the file line by line or in chunks.
• Using a JSON parsing library designed for large files: (e.g., ijson or similar)
• Iterating through the JSON structure: If the JSON is structured in a specific way.

Could you provide examples or best practices for effectively parsing a large JSON file in Python, focusing on memory efficiency? Specifically, I'm interested in:

• Code examples demonstrating the recommended approach.
• Considerations for handling nested JSON structures.
• Performance tips and potential bottlenecks to avoid.
• Any recommended libraries for this purpose.

So far my naive code is following, which is obviously not acceptable:

import json

try:
with open('large_file.json', 'r') as file:
data = json.load(file)
print(f"Loaded {len(data)} items from the JSON file.")
except MemoryError:
print("Memory overload! The file is too large to load into memory.")
except Exception as e:
print(f"An error occurred: {e}")

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