🔄 Advanced JSON Processing — Streaming and Custom Handling

Work with large JSON files and custom data type serialization.

🎯 Streaming JSON Parsing

import ijson

# Parse large JSON files without loading all into memory
with open("huge.json", "rb") as f:
    for item in ijson.items(f, "items.item"):
        process(item)  # Process items one at a time

💡 Custom Encoders/Decoders

import json
from datetime import datetime

class CustomEncoder(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, datetime):
            return obj.isoformat()
        return super().default(obj)

# Serialize custom objects
data = {"timestamp": datetime.now()}
json_str = json.dumps(data, cls=CustomEncoder)

# Custom decoder
def custom_decoder(dct):
    if "timestamp" in dct:
        dct["timestamp"] = datetime.fromisoformat(dct["timestamp"])
    return dct

loaded = json.loads(json_str, object_hook=custom_decoder)

🔑 Key Takeaways

• ✅ Use ijson for streaming large JSON

• ✅ Custom encoders for non-standard types

• ✅ object_hook for custom deserialization

• ✅ Performance critical for large datasets

• ✅ Validate against schemas (jsonschema)

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