⚡️ Speed up function get_analyzer_for_file by 32% in PR #1384 (non-unicode-pytest-fail)#1385
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The optimized code achieves a **31% runtime improvement** by introducing `@lru_cache` to cache `TreeSitterAnalyzer` instances based on file extensions, eliminating redundant object creation. ## Key Optimization **Added LRU caching**: The new `_analyzer_for_suffix()` helper function uses `@lru_cache(maxsize=16)` to cache analyzer instances. When the same file extension is encountered multiple times, the cached analyzer is returned instead of creating a new `TreeSitterAnalyzer` object. ## Why This Improves Runtime 1. **Eliminates repeated object instantiation**: The original code created a new `TreeSitterAnalyzer` every time `get_analyzer_for_file()` was called, even for the same file type. Line profiler shows that in the original version, `TreeSitterAnalyzer.__init__` was called **1,082 times**, consuming 1.15ms. In the optimized version, it's only called **38 times** (cache misses), consuming just 55μs - a **95% reduction**. 2. **Fast dictionary lookup vs object creation**: The LRU cache uses a fast dictionary lookup (O(1)) to return cached analyzers. This is significantly faster than the original flow which required: - Creating a new object - Running `isinstance()` check - Assigning attributes (`self.language`, `self._parser`) 3. **Reduced memory allocation overhead**: Each new `TreeSitterAnalyzer` instance requires memory allocation and initialization. Reusing cached instances eliminates this overhead for repeated file extensions. ## Impact on Hot Path Usage The function references show `get_analyzer_for_file()` is called extensively in test discovery code across multiple test files. The function is invoked **within loops** for processing JavaScript/TypeScript test files, making it a hot path. For example: - Processing 100+ files in `test_multiple_ts_files_consistent_results` - Called repeatedly in test batches and nested loops Since the same file extensions (.ts, .tsx, .js) are processed repeatedly in these loops, the cache hit rate is very high, maximizing the optimization's benefit. ## Test Case Performance The annotated tests confirm this optimization excels when: - **Processing the same extension multiple times**: Tests like `test_multiple_ts_files_consistent_results` show 33.8% speedup - **Common extensions** (.ts, .tsx, .js): 35-48% faster on individual calls - **Batch operations**: Processing lists of files with repeated extensions sees consistent 30-40% improvements Edge cases with uncommon extensions (.txt, .py) may show slight regression (12-19% slower) due to cache lookup overhead, but these are rare in practice given the function's usage for JavaScript/TypeScript file analysis.
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⚡️ This pull request contains optimizations for PR #1384
If you approve this dependent PR, these changes will be merged into the original PR branch
non-unicode-pytest-fail.📄 32% (0.32x) speedup for
get_analyzer_for_fileincodeflash/languages/treesitter_utils.py⏱️ Runtime :
1.10 milliseconds→834 microseconds(best of235runs)📝 Explanation and details
The optimized code achieves a 31% runtime improvement by introducing
@lru_cacheto cacheTreeSitterAnalyzerinstances based on file extensions, eliminating redundant object creation.Key Optimization
Added LRU caching: The new
_analyzer_for_suffix()helper function uses@lru_cache(maxsize=16)to cache analyzer instances. When the same file extension is encountered multiple times, the cached analyzer is returned instead of creating a newTreeSitterAnalyzerobject.Why This Improves Runtime
Eliminates repeated object instantiation: The original code created a new
TreeSitterAnalyzerevery timeget_analyzer_for_file()was called, even for the same file type. Line profiler shows that in the original version,TreeSitterAnalyzer.__init__was called 1,082 times, consuming 1.15ms. In the optimized version, it's only called 38 times (cache misses), consuming just 55μs - a 95% reduction.Fast dictionary lookup vs object creation: The LRU cache uses a fast dictionary lookup (O(1)) to return cached analyzers. This is significantly faster than the original flow which required:
isinstance()checkself.language,self._parser)Reduced memory allocation overhead: Each new
TreeSitterAnalyzerinstance requires memory allocation and initialization. Reusing cached instances eliminates this overhead for repeated file extensions.Impact on Hot Path Usage
The function references show
get_analyzer_for_file()is called extensively in test discovery code across multiple test files. The function is invoked within loops for processing JavaScript/TypeScript test files, making it a hot path. For example:test_multiple_ts_files_consistent_resultsSince the same file extensions (.ts, .tsx, .js) are processed repeatedly in these loops, the cache hit rate is very high, maximizing the optimization's benefit.
Test Case Performance
The annotated tests confirm this optimization excels when:
test_multiple_ts_files_consistent_resultsshow 33.8% speedupEdge cases with uncommon extensions (.txt, .py) may show slight regression (12-19% slower) due to cache lookup overhead, but these are rare in practice given the function's usage for JavaScript/TypeScript file analysis.
✅ Correctness verification report:
🌀 Click to see Generated Regression Tests
To edit these changes
git checkout codeflash/optimize-pr1384-2026-02-04T17.03.31and push.