<ul><li>Python offers efficient tools for processing large JSON files without overwhelming system resources.</li><li>Using libraries like ijson for stream processing enables incremental parsing of large JSON datasets.</li><li>Line-by-line processing is effective for newline-delimited JSON files, such as log entries.</li><li>Memory-mapped files provide fast random access to specific parts of JSON files.</li><li>Chunked processing divides files into manageable parts, optimizing memory usage for database operations.</li><li>Working with compressed JSON files reduces disk I/O and memory usage while maintaining fast access.</li><li>JSON Path expressions allow targeted data extraction from complex JSON structures.</li><li>Parallel processing on multi-core machines significantly accelerates data processing.</li><li>Combining techniques like streaming, batching, and parallel processing enhances efficiency for processing massive datasets.</li><li>Generator functions are recommended for transforming large datasets with minimal memory consumption.</li></ul>

How to Process Multi-Gigabyte JSON Files in Python: Memory-Efficient Techniques That Work

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