d4c3853645
Reuse `markdown.Markdown` instances via `threading.local` to avoid initialization overhead on every call. This provides a ~2x speedup for Markdown rendering and reduces memory churn. - Implements `_md_local` thread-local storage in `scripts/web_dashboard.py` - Reuses `Markdown` instance with `reset()` for subsequent conversions - Preserves thread safety for Flask/Werkzeug environments
3 KiB
Bolt's Journal ⚡
This journal is for CRITICAL, non-routine performance learnings ONLY.
- Codebase-specific bottlenecks
- Failed optimizations (and why)
- Surprising performance patterns
- Rejected changes with valuable lessons
2024-07-25 - MQL5 Native Functions vs. Scripted Loops
Learning: My assumption that a manual MQL5 loop over a pre-cached array would be faster than built-in functions like iHighest() and iLowest() was incorrect. The code review pointed out that MQL5's native, built-in functions are implemented in highly optimized C++ and are significantly faster than loops executed in the MQL5 scripting layer. The original comment stating this was correct.
Action: Always prefer using MQL5's built-in, native functions for calculations like finding highs/lows over manual loops, even if the data is already in a local array. The performance gain from the native implementation outweighs the overhead of the function call.
2026-01-23 - Python File System Checks
Learning: Checking for file existence (os.path.exists) before getting metadata (os.path.getmtime) introduces a redundant syscall. os.stat() provides both pieces of information in a single syscall and uses the EAFP (Easier to Ask for Forgiveness than Permission) pattern, which is more Pythonic and slightly faster, especially in high-frequency loops or handlers.
Action: Use os.stat() when both existence and metadata are needed, wrapping it in a try...except OSError block.
2026-01-26 - yfinance Bulk Download
Learning: yfinance Ticker.history in a loop is significantly slower than yf.download with a list of tickers due to sequential HTTP requests. yf.download with group_by='ticker' provides a consistent MultiIndex structure even for single tickers, simplifying bulk processing.
Action: Always prefer yf.download(tickers) over iterating yf.Ticker(t) when fetching data for multiple symbols.
2026-02-09 - Git Command Performance
Learning: git for-each-ref is a powerful tool for batch data retrieval, but without filtering, it processes all refs, including thousands of stale merged branches in older repositories. Calculating ahead-behind counts for these stale branches is O(N) where N is total branches, which can be significantly slower than O(M) where M is active branches.
Action: Always filter git for-each-ref with --no-merged (or --merged depending on use case) when only interested in a subset of branches, especially when expensive formatting options like ahead-behind are used.
2026-02-17 - Python Markdown Performance
Learning: Reusing markdown.Markdown instances with threading.local is significantly faster (~2x for small docs, saves initialization) than the helper markdown.markdown() function, which re-initializes the parser on every call. md.reset() is critical to clear state.
Action: Use threading.local to store markdown.Markdown instances in threaded web apps (like Flask/Werkzeug) instead of creating new instances per request.