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@codeflash-ai codeflash-ai bot commented Oct 23, 2025

📄 13% (0.13x) speedup for _CollectionConfigCreate.validate_vector_names in weaviate/collections/classes/config.py

⏱️ Runtime : 81.0 microseconds 71.5 microseconds (best of 61 runs)

📝 Explanation and details

The optimization replaces an O(n²) duplicate detection algorithm with an O(n) single-pass approach.

Key Change:

  • Original: Used names.count(name) > 1 inside a set comprehension, which calls count() for each name - requiring a full list scan for every element
  • Optimized: Uses a single loop with two sets (seen and dups) to track visited names and duplicates in one pass

Why it's faster:

  • The original approach has O(n²) complexity because list.count() is O(n) and gets called for each of the n elements
  • The optimized version is O(n) - each name is processed exactly once, with O(1) set operations for lookups and additions
  • For validation scenarios with many vector configs, this eliminates redundant scanning of the names list

Performance characteristics:

  • 13% speedup on the test case suggests moderate list sizes where the O(n²) vs O(n) difference is noticeable but not dramatic
  • Benefits scale significantly with larger lists - the more vector configs being validated, the greater the performance improvement
  • Memory usage remains similar (both approaches create sets for duplicate detection)

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 🔘 None Found
⏪ Replay Tests 211 Passed
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 66.7%
⏪ Replay Tests and Runtime
Test File::Test Function Original ⏱️ Optimized ⏱️ Speedup
test_pytest_testcollectiontest_batch_py_testcollectiontest_classes_generative_py_testcollectiontest_confi__replay_test_0.py::test_weaviate_collections_classes_config__CollectionConfigCreate_validate_vector_names 81.0μs 71.5μs 13.3%✅

To edit these changes git checkout codeflash/optimize-_CollectionConfigCreate.validate_vector_names-mh35zunc and push.

Codeflash

The optimization replaces an O(n²) duplicate detection algorithm with an O(n) single-pass approach.

**Key Change:**
- **Original:** Used `names.count(name) > 1` inside a set comprehension, which calls `count()` for each name - requiring a full list scan for every element
- **Optimized:** Uses a single loop with two sets (`seen` and `dups`) to track visited names and duplicates in one pass

**Why it's faster:**
- The original approach has O(n²) complexity because `list.count()` is O(n) and gets called for each of the n elements
- The optimized version is O(n) - each name is processed exactly once, with O(1) set operations for lookups and additions
- For validation scenarios with many vector configs, this eliminates redundant scanning of the names list

**Performance characteristics:**
- 13% speedup on the test case suggests moderate list sizes where the O(n²) vs O(n) difference is noticeable but not dramatic
- Benefits scale significantly with larger lists - the more vector configs being validated, the greater the performance improvement
- Memory usage remains similar (both approaches create sets for duplicate detection)
@codeflash-ai codeflash-ai bot requested a review from mashraf-222 October 23, 2025 08:33
@codeflash-ai codeflash-ai bot added the ⚡️ codeflash Optimization PR opened by Codeflash AI label Oct 23, 2025
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