Conversation
Optimizes memory usage by replacing eager `read().decode()` with `io.TextIOWrapper`. This prevents loading the entire file content into memory as bytes and then as a string, instead streaming the decoded text directly to the parsers. - Modifies `app.py` to use `io.TextIOWrapper`. - Adds `tests/test_streaming_io.py` to prevent regression. - Updates `.gitignore` to exclude `__pycache__`. - Updates `.jules/bolt.md` with streaming IO learnings. Co-authored-by: erayfirat <[email protected]>
|
👋 Jules, reporting for duty! I'm here to lend a hand with this pull request. When you start a review, I'll add a 👀 emoji to each comment to let you know I've read it. I'll focus on feedback directed at me and will do my best to stay out of conversations between you and other bots or reviewers to keep the noise down. I'll push a commit with your requested changes shortly after. Please note there might be a delay between these steps, but rest assured I'm on the job! For more direct control, you can switch me to Reactive Mode. When this mode is on, I will only act on comments where you specifically mention me with New to Jules? Learn more at jules.google/docs. For security, I will only act on instructions from the user who triggered this task. |
1 participant
⚡ Bolt: [Memory Optimization] Use Streaming IO for MGF/mzTab Loading
💡 What:
Replaced
mgf_file.read().decode('utf-8')withio.TextIOWrapper(mgf_file, encoding='utf-8')(and similarly for mzTab).🎯 Why:
The previous implementation loaded the entire file into memory as bytes, then decoded it into a huge string, and then wrapped it in
StringIO. For large proteomics files (hundreds of MBs to GBs), this caused massive memory spikes (3x file size).📊 Impact:
Significantly reduces memory footprint during file loading. The memory usage for the file content is now O(buffer_size) instead of O(file_size).
🔬 Measurement:
Verified with
tests/test_streaming_io.pywhich confirms thatload_mgfandload_mztabaccept the streaming wrapper and parse data correctly.PR created automatically by Jules for task 6087201179588860803 started by @erayfirat