Identifying Memory Bottlenecks in a Python Application on Ubuntu 22.04
I'm maintaining legacy code that Hey everyone, I'm running into an issue that's driving me crazy. Currently profiling a Python application running on Ubuntu 22.04, I need to find out why memory usage spikes significantly during certain operations. The app uses Flask for the backend and SQLite for the database. I've been using the `memory_profiler` package to track memory consumption, but the results aren't leading me to a clear culprit. Here's a snippet of the code where the memory spike occurs: ```python @app.route('/process', methods=['POST']) def process_data(): data = request.json result = complex_calculation(data) return jsonify(result) ``` The function `complex_calculation` processes a large dataset and seems to consume more memory than expected. I tried breaking down the dataset into smaller chunks, and while this helps, it complicates the logic significantly. Additionally, I've enabled Flask's built-in debugger and used `objgraph` to visualize object allocations, but the output is overwhelming. Here’s what I’ve done so far: - Ran the application with `mprof run` and analyzed with `mprof plot`, which shows a steep increase in memory usage during the `complex_calculation` call. - I also checked for circular references, but the garbage collector doesn't seem to be holding onto objects unnecessarily. Any suggestions on best practices or tools for pinpointing memory issues in such scenarios? Should I consider using different profiling techniques or perhaps switch to a more robust database solution like PostgreSQL? Would love to hear thoughts on optimizing this part of the application without major rewrites. Could someone point me to the right documentation? Thanks for your help in advance!