⚡️ Speed up method _VectorIndex.hnsw by 7%
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The optimization removes an unnecessary keyword argument in the `warnings.warn()` call. In the original code, the warning message was passed using `message=`, but `warnings.warn()` accepts the message as the first positional argument. By removing the `message=` keyword and passing the warning text directly as a positional argument, the optimized version eliminates the overhead of keyword argument processing in Python's function call mechanism. This micro-optimization reduces the cost of parameter binding during function calls. When Python processes keyword arguments, it must perform dictionary lookups and parameter matching, whereas positional arguments are passed more directly. While the performance gain is small (6% speedup, reducing runtime from 29.7μs to 27.8μs), it's meaningful for frequently called deprecation warnings in library code. The optimization is particularly effective for test cases that trigger the deprecation warning path (when `multi_vector` is not None), as shown in the test suite where the warning is issued during HNSW configuration creation.
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📄 7% (0.07x) speedup for
_VectorIndex.hnswinweaviate/collections/classes/config_vector_index.py⏱️ Runtime :
29.7 microseconds→27.8 microseconds(best of85runs)📝 Explanation and details
The optimization removes an unnecessary keyword argument in the
warnings.warn()call. In the original code, the warning message was passed usingmessage=, butwarnings.warn()accepts the message as the first positional argument. By removing themessage=keyword and passing the warning text directly as a positional argument, the optimized version eliminates the overhead of keyword argument processing in Python's function call mechanism.This micro-optimization reduces the cost of parameter binding during function calls. When Python processes keyword arguments, it must perform dictionary lookups and parameter matching, whereas positional arguments are passed more directly. While the performance gain is small (6% speedup, reducing runtime from 29.7μs to 27.8μs), it's meaningful for frequently called deprecation warnings in library code.
The optimization is particularly effective for test cases that trigger the deprecation warning path (when
multi_vectoris not None), as shown in the test suite where the warning is issued during HNSW configuration creation.✅ Correctness verification report:
⏪ Replay Tests and Runtime
test_pytest_testcollectiontest_batch_py_testcollectiontest_classes_generative_py_testcollectiontest_confi__replay_test_0.py::test_weaviate_collections_classes_config_vector_index__VectorIndex_hnswTo edit these changes
git checkout codeflash/optimize-_VectorIndex.hnsw-mh31sw1nand push.