What is the most efficient way to handle large JSON files in Python?

For large JSON files, use streaming parsers instead of loading everything into memory. The ijson library provides iterative parsing: for item in ijson.items(file, "item"): process(item) handles massive files efficiently. For JSON Lines format (newline-delimited JSON), read line by line: for line in file: json.loads(line). Use json.load() with file chunks for moderate sizes. pandas.read_json() with chunksize parameter processes large datasets incrementally. Consider ujson or orjson libraries that are significantly faster than standard json module. For very large files, use memory mapping or database imports instead of in-memory processing. Generator expressions conserve memory when processing. Before implementing streaming, test your JSON structure with our JSON Viewer at jsonconsole.com/json-viewer to understand the data layout. Profiling with cProfile helps identify bottlenecks. Choose streaming for files over 100MB or limited memory environments.