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f154cfa341
Replaced `pathlib.Path.rglob("*")` with `os.walk(topdown=True)` in `scripts/ci_validate_repo.py` to prune large directories like `.git` and `node_modules` in-place. This avoids unnecessary traversal and syscalls, improving CI validation performance.
Also:
- Fixed indentation logic in file scanning loop.
- Renamed inner loop variable to avoid shadowing.
- Documented learning in `.jules/bolt.md`.
28 lines
3 KiB
Markdown
28 lines
3 KiB
Markdown
# Bolt's Journal ⚡
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This journal is for CRITICAL, non-routine performance learnings ONLY.
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- Codebase-specific bottlenecks
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- Failed optimizations (and why)
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- Surprising performance patterns
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- Rejected changes with valuable lessons
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## 2024-07-25 - MQL5 Native Functions vs. Scripted Loops
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**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.
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**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.
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## 2026-01-23 - Python File System Checks
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**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.
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**Action:** Use `os.stat()` when both existence and metadata are needed, wrapping it in a `try...except OSError` block.
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## 2026-01-26 - yfinance Bulk Download
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**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.
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**Action:** Always prefer `yf.download(tickers)` over iterating `yf.Ticker(t)` when fetching data for multiple symbols.
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## 2026-02-09 - Git Command Performance
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**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.
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**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.
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## 2026-02-12 - Python Recursive File Scanning
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**Learning:** `pathlib.Path.rglob("*")` exhaustively traverses all subdirectories (including huge ones like `.git`, `node_modules`) *before* yielding paths, making post-filtering inefficient O(N). `os.walk(topdown=True)` allows pruning entire directory trees *in-place* by modifying `dirs`, reducing complexity to O(Relevant Files) and skipping thousands of syscalls.
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**Action:** Prefer `os.walk` with in-place `dirs` pruning over `rglob` + filtering when scanning repositories with large ignored directories.
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