How to Find and Fix Memory Leaks in Python

Arthur explains that a Python application may appear to use more and more memory despite normal operation. This is often caused by a memory leak where objects remain in memory longer than necessary. Common causes include storing large lists of requests or data, using unlimited caches, retaining large objects, background tasks accumulating data, and holding unnecessary references.

To find and fix memory leaks, Arthur recommends using Python's built-in tracemalloc module to identify which parts of the code are allocating the most memory. By taking snapshots of memory usage before and after running a workload, developers can pinpoint which code sections cause the biggest allocations. Comparing snapshots can reveal problematic areas to investigate.

For caches, Arthur suggests using limited size caches like functools.lru_cache to prevent an unlimited cache from growing endlessly. Regularly monitoring the Python process's RAM usage with modules like psutil is also advised. By measuring RSS memory (resident set size) at intervals, developers can see if memory usage steadily increases under the same workload, indicating a potential leak.

Arthur emphasizes that memory leaks are particularly problematic for long-running services and applications on virtual private servers (VPS). While adding more RAM might temporarily mask the issue, it doesn't resolve the underlying cause. Instead, developers should investigate the source of the leak through careful measurement and analysis. By tracking memory usage and code allocations, developers can identify and fix memory leaks, ensuring their Python applications run smoothly and efficiently.