⚡️ Speed up function _is_unrequested_hidden_file_or_is_inside_unrequested_hidden_dir by 11%#125
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This optimization achieves an **11% runtime improvement** by eliminating unnecessary memory allocations in a path-filtering function. The key changes are:
**What Changed:**
1. **Replaced list comprehensions with counting loops**: Instead of building two intermediate lists (`hidden_directories_in_path` and `hidden_directories_in_pattern`), the code now uses simple integer counters that increment as hidden parts are found.
2. **Eliminated set allocations**: The original code used `set(part) == {"."}` to check if a part consists only of dots. The optimized version uses `part.strip(".") != ""` instead, avoiding the overhead of creating a set object for every path component.
**Why It's Faster:**
- **Reduced memory allocations**: In Python, creating lists and sets has significant overhead. By counting directly, we avoid allocating memory for intermediate data structures that are only used to determine their length.
- **Lower Python interpreter overhead**: Integer increments are much cheaper than list append operations, which require bounds checking and potential memory reallocation.
- **Faster character check**: String operations like `strip()` are implemented in optimized C code and avoid the overhead of set creation, hashing, and comparison.
**Performance Context:**
Based on the `function_references`, this function is called from `resolve_pattern()` in a list comprehension that filters file paths during glob operations. This means it's invoked **once per matched file** when resolving data file patterns. In workflows that scan directories with many files (especially those with hidden files/directories like `.git/` or `.venv/`), this optimization compounds:
- For 1,000 files checked: saves ~0.92ms
- The test results show consistent 10-25% speedups across various path structures, with the best gains (27-41%) on deeply nested hidden directories
**Test Case Performance:**
The optimization excels with:
- Paths with multiple hidden parts (27-41% faster for 50+ nested hidden directories)
- Alternating hidden/regular structures (16-17% faster)
- Unicode hidden directories (11% faster)
- All test patterns show improvement, indicating robust performance across diverse real-world scenarios
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📄 11% (0.11x) speedup for
_is_unrequested_hidden_file_or_is_inside_unrequested_hidden_dirinsrc/datasets/data_files.py⏱️ Runtime :
9.22 milliseconds→8.30 milliseconds(best of6runs)📝 Explanation and details
This optimization achieves an 11% runtime improvement by eliminating unnecessary memory allocations in a path-filtering function. The key changes are:
What Changed:
Replaced list comprehensions with counting loops: Instead of building two intermediate lists (
hidden_directories_in_pathandhidden_directories_in_pattern), the code now uses simple integer counters that increment as hidden parts are found.Eliminated set allocations: The original code used
set(part) == {"."}to check if a part consists only of dots. The optimized version usespart.strip(".") != ""instead, avoiding the overhead of creating a set object for every path component.Why It's Faster:
strip()are implemented in optimized C code and avoid the overhead of set creation, hashing, and comparison.Performance Context:
Based on the
function_references, this function is called fromresolve_pattern()in a list comprehension that filters file paths during glob operations. This means it's invoked once per matched file when resolving data file patterns. In workflows that scan directories with many files (especially those with hidden files/directories like.git/or.venv/), this optimization compounds:Test Case Performance:
The optimization excels with:
✅ Correctness verification report:
⚙️ Click to see Existing Unit Tests
🌀 Click to see Generated Regression Tests
To edit these changes
git checkout codeflash/optimize-_is_unrequested_hidden_file_or_is_inside_unrequested_hidden_dir-mlcm8ck0and push.