⚡️ Speed up function is_valid_field_name by 26% in pydantic/_internal/_fields.py

[codeflash-ai]

📄 is_valid_field_name() in pydantic/_internal/_fields.py

📈 Performance improved by 26% (0.26x faster)

⏱️ Runtime went down from 43.2 microseconds to 34.3 microseconds

Explanation and details

Your original function is already quite efficient for its purpose, as it makes use of Python's built-in startswith method which is implemented in C and is highly optimized. However, for the sake of minor optimizations, we can use the fact that strings are iterable, and we can check the first character directly.

This slight change avoids the overhead of the method call by directly comparing the first character of the string. Note that this also handles the case where the string might be empty, as name and will return False for empty strings, preventing an IndexError.

Correctness verification

The new optimized code was tested for correctness. The results are listed below.

🔘 (none found) − ⚙️ Existing Unit Tests

✅ 30 Passed − 🌀 Generated Regression Tests

(click to show generated tests)

# imports
import pytest  # used for our unit tests
from pydantic._internal._fields import is_valid_field_name


# unit tests
def test_basic_valid_cases():
    # Simple valid names
    assert is_valid_field_name("field") == True
    assert is_valid_field_name("name") == True
    assert is_valid_field_name("validFieldName") == True

def test_basic_invalid_cases():
    # Names starting with an underscore
    assert is_valid_field_name("_field") == False
    assert is_valid_field_name("_name") == False
    assert is_valid_field_name("_invalidFieldName") == False

def test_edge_cases():
    # Empty string
    assert is_valid_field_name("") == True
    
    # Single character
    assert is_valid_field_name("a") == True
    assert is_valid_field_name("_") == False
    
    # Multiple underscores
    assert is_valid_field_name("___") == False
    assert is_valid_field_name("__field") == False
    assert is_valid_field_name("field__") == True

def test_mixed_cases():
    # Names containing underscores but not starting with them
    assert is_valid_field_name("field_name") == True
    assert is_valid_field_name("name_with_underscores") == True
    assert is_valid_field_name("valid_field_name") == True

def test_case_sensitivity():
    # Names with uppercase letters
    assert is_valid_field_name("Field") == True
    assert is_valid_field_name("_Field") == False
    assert is_valid_field_name("FIELD_NAME") == True

def test_special_characters():
    # Names with special characters
    assert is_valid_field_name("field-name") == True
    assert is_valid_field_name("field@name") == True
    assert is_valid_field_name("_field-name") == False

def test_numerical_cases():
    # Names with numbers
    assert is_valid_field_name("field1") == True
    assert is_valid_field_name("1field") == True
    assert is_valid_field_name("_1field") == False

def test_unicode_and_non_ascii_characters():
    # Names with non-ASCII characters
    assert is_valid_field_name("名前") == True  # Japanese for "name"
    assert is_valid_field_name("_名前") == False
    assert is_valid_field_name("имя") == True  # Russian for "name"
    assert is_valid_field_name("_имя") == False

def test_large_scale_cases():
    # Very long names
    assert is_valid_field_name("a" * 1000) == True  # 1000 'a' characters
    assert is_valid_field_name("_" + "a" * 999) == False  # 1000 characters starting with '_'

def test_performance_and_scalability():
    # Stress test with large number of checks
    valid_names = ["field" + str(i) for i in range(10000)]
    invalid_names = ["_field" + str(i) for i in range(10000)]
    
    for name in valid_names:
        assert is_valid_field_name(name) == True
    
    for name in invalid_names:
        assert is_valid_field_name(name) == False

# Run the tests if this file is executed directly
if __name__ == "__main__":
    pytest.main()

✅ 100 Passed − ⏪ Replay Tests

[iusedmyimagination]

Is this correct? Absolutely, and quite clever. Is this actually faster? I'd like to see some statistical proof along with a look at the actual metric, since Python string methods are usually highly optimized in C. Is this a hot path? Good question!