Memory usage can be hard to keep under control in Python projects. The language doesn’t make it explicit where memory is allocated, module imports can have signficant costs, and it’s all too easy to create a global data structure that accidentally grows unbounded, leaking memory. Django projects can be particularly susceptible to memory bloat, as they may import many large dependencies like numpy, even if they’re only used in a few places.

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Adam Johnson

Memray is a memory profiler for Python that helps track memory allocation and deallocation through flame graphs. To profile a Django project, start by running `memray run manage.py check` to measure startup memory usage, then generate a flame graph with `memray flamegraph`. The visualization shows memory allocations over time, with frame width proportional to memory used. Common optimizations include deferring expensive imports like numpy until needed, using lazy imports, or replacing heavy dependencies with lighter alternatives. A practical example demonstrates reducing startup memory from 25.2 MB to 19.4 MB by replacing numpy's shuffle with Python's built-in random.shuffle().

Django: profile memory usage with Memray

A Zsh one-liner to speed up checking results