⚡️ Speed up function `remove_uncoercible` by 31% by codeflash-ai[bot] · Pull Request #17 · aseembits93/datadog-api-client-python

codeflash-ai · 2025-10-04T22:10:31Z

📄 31% (0.31x) speedup for `remove_uncoercible` in `src/datadog_api_client/model_utils.py`

⏱️ Runtime : 26.1 milliseconds → 20.0 milliseconds (best of 39 runs)

📝 Explanation and details

The optimized code achieves a 30% speedup through several key performance improvements:

1. Local Variable Caching for Built-ins

Stores isinstance, issubclass, and type as local variables (_isinstance, _issubclass, _type) to eliminate repeated global lookups, which are slower in Python.

2. Optimized Type Checking Order in get_simple_class

Reorders type checks to prioritize common data types (tuple, list, dict, bool, int) before rare types like file_type
This reduces the number of isinstance calls for typical inputs, as shown in the line profiler where file_type checks dropped from 27.9% to 0.8% of total time

3. Set-Based Lookup Optimization in remove_uncoercible

Converts tuple lookups (COERCIBLE_TYPE_PAIRS and UPCONVERSION_TYPE_PAIRS) to sets for O(1) membership testing instead of O(N) tuple scanning
Pre-computes these sets once per function call rather than repeatedly accessing the original tuples

Test Case Performance:

Large-scale scenarios benefit most: Tests with many required types show dramatic improvements (131-191% faster) due to set-based lookups
Simple cases show modest slowdowns: Small test cases are 2-31% slower due to the overhead of set creation, but this is offset by gains in realistic usage
None-type handling significantly improved: 42.4% faster for large-scale None processing due to optimized type checking order

The optimizations are particularly effective for workloads with many type conversions or large numbers of required types, which is typical in API client libraries processing diverse data structures.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 7296 Passed
⏪ Replay Tests	✅ 255 Passed
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	86.4%

🌀 Generated Regression Tests and Runtime

import io
from datetime import date, datetime
from types import MappingProxyType
from uuid import UUID

# imports
import pytest  # used for our unit tests
from src.datadog_api_client.model_utils import remove_uncoercible


# Dummy model classes to simulate OpenAPI models
class ModelComposed:
    pass

class ModelNormal:
    pass

class ModelSimple:
    pass
file_type = io.IOBase
empty_dict = MappingProxyType({})  # type: ignore

UPCONVERSION_TYPE_PAIRS = (
    (str, datetime),
    (str, date),
    (int, float),
    (list, ModelComposed),
    (dict, ModelComposed),
    (bool, ModelComposed),
    (str, ModelComposed),
    (int, ModelComposed),
    (float, ModelComposed),
    (list, ModelComposed),
    (list, ModelNormal),
    (dict, ModelNormal),
    (bool, ModelSimple),
    (str, ModelSimple),
    (int, ModelSimple),
    (float, ModelSimple),
    (list, ModelSimple),
)

COERCIBLE_TYPE_PAIRS = {
    False: (
        # Empty for client instantiation
    ),
    True: (
        (dict, ModelComposed),
        (list, ModelComposed),
        (dict, ModelNormal),
        (list, ModelNormal),
        (bool, ModelSimple),
        (str, ModelSimple),
        (int, ModelSimple),
        (float, ModelSimple),
        (list, ModelSimple),
        (str, datetime),
        (str, date),
        (str, UUID),
        (str, file_type),
    ),
}
from src.datadog_api_client.model_utils import remove_uncoercible

# ------------------ UNIT TESTS ------------------

# Basic Test Cases

def test_basic_str_to_datetime():
    # str can be coerced to datetime
    codeflash_output = remove_uncoercible((datetime, str), "2024-01-01T12:00:00", True); result = codeflash_output # 5.97μs -> 6.86μs (12.9% slower)

def test_basic_str_to_date():
    # str can be coerced to date
    codeflash_output = remove_uncoercible((date, str), "2024-01-01", True); result = codeflash_output # 5.83μs -> 6.82μs (14.5% slower)

def test_basic_int_to_float():
    # int can be coerced to float
    codeflash_output = remove_uncoercible((float, int), 1, True); result = codeflash_output # 5.90μs -> 6.74μs (12.4% slower)

def test_basic_str_to_uuid():
    # str can be coerced to UUID
    codeflash_output = remove_uncoercible((UUID, str), "12345678-1234-5678-1234-567812345678", True); result = codeflash_output # 6.15μs -> 6.86μs (10.3% slower)

def test_basic_str_to_file():
    # str can be coerced to file_type
    codeflash_output = remove_uncoercible((file_type, str), "filename.txt", True); result = codeflash_output # 6.21μs -> 6.88μs (9.77% slower)

def test_basic_dict_to_modelcomposed():
    # dict can be coerced to ModelComposed
    codeflash_output = remove_uncoercible((ModelComposed, dict), {"key": "value"}, True); result = codeflash_output # 4.71μs -> 6.51μs (27.7% slower)

def test_basic_list_to_modelnormal():
    # list can be coerced to ModelNormal
    codeflash_output = remove_uncoercible((ModelNormal, list), [1, 2, 3], True); result = codeflash_output # 4.37μs -> 6.42μs (31.9% slower)

def test_basic_bool_to_modelsimple():
    # bool can be coerced to ModelSimple
    codeflash_output = remove_uncoercible((ModelSimple, bool), True, True); result = codeflash_output # 6.55μs -> 6.87μs (4.69% slower)

def test_basic_str_to_modelsimple():
    # str can be coerced to ModelSimple
    codeflash_output = remove_uncoercible((ModelSimple, str), "simple", True); result = codeflash_output # 6.80μs -> 6.94μs (2.02% slower)

def test_basic_int_to_modelsimple():
    # int can be coerced to ModelSimple
    codeflash_output = remove_uncoercible((ModelSimple, int), 42, True); result = codeflash_output # 6.62μs -> 6.58μs (0.501% faster)

def test_basic_float_to_modelsimple():
    # float can be coerced to ModelSimple
    codeflash_output = remove_uncoercible((ModelSimple, float), 3.14, True); result = codeflash_output # 6.87μs -> 8.85μs (22.5% slower)

# Edge Test Cases

def test_edge_same_type_excluded():
    # Should not return the same type as current_item
    codeflash_output = remove_uncoercible((str, datetime), "abc", True); result = codeflash_output # 5.77μs -> 6.86μs (15.9% slower)

def test_edge_none_type():
    # None type should not be coerced to anything
    codeflash_output = remove_uncoercible((str, int, float), None, True); result = codeflash_output # 6.04μs -> 6.97μs (13.3% slower)

def test_edge_empty_list():
    # Empty list should be considered as list type
    codeflash_output = remove_uncoercible((ModelComposed, ModelNormal), [], True); result = codeflash_output # 5.15μs -> 6.29μs (18.1% slower)

def test_edge_empty_dict():
    # Empty dict should be considered as dict type
    codeflash_output = remove_uncoercible((ModelComposed, ModelNormal), {}, True); result = codeflash_output # 5.64μs -> 6.63μs (14.9% slower)

def test_edge_file_type():
    # file_type should not be coerced to anything except itself (excluded)
    file_obj = io.StringIO("test")
    codeflash_output = remove_uncoercible((file_type, str), file_obj, True); result = codeflash_output # 7.02μs -> 8.90μs (21.2% slower)

def test_edge_uuid_type():
    # UUID should not be coerced to anything except itself (excluded)
    uuid_obj = UUID("12345678-1234-5678-1234-567812345678")
    codeflash_output = remove_uncoercible((UUID, str), uuid_obj, True); result = codeflash_output # 7.62μs -> 6.73μs (13.2% faster)

def test_edge_tuple_type():
    # tuple type is not in any coercion pair
    codeflash_output = remove_uncoercible((ModelComposed, ModelNormal), (1, 2), True); result = codeflash_output # 5.14μs -> 6.21μs (17.2% slower)

def test_edge_mappingproxytype():
    # MappingProxyType is not in any coercion pair
    codeflash_output = remove_uncoercible((ModelComposed, ModelNormal), empty_dict, True); result = codeflash_output # 7.82μs -> 9.61μs (18.6% slower)

def test_edge_model_type_as_current():
    # If current_item is a ModelComposed instance, should not include ModelComposed in result
    class DummyComposed(ModelComposed): pass
    dummy = DummyComposed()
    codeflash_output = remove_uncoercible((ModelComposed, ModelNormal), dummy, True); result = codeflash_output # 87.4μs -> 100μs (13.2% slower)

def test_edge_model_type_as_required():
    # If required_types_classes contains subclasses, should upconvert to base class
    class DummyNormal(ModelNormal): pass
    codeflash_output = remove_uncoercible((DummyNormal, ModelNormal), {}, True); result = codeflash_output # 5.64μs -> 6.78μs (16.9% slower)

def test_edge_must_convert_false():
    # If must_convert is False, only upconversion pairs apply
    codeflash_output = remove_uncoercible((datetime, date, str), "2024-01-01", True, must_convert=False); result = codeflash_output # 6.41μs -> 6.79μs (5.50% slower)

def test_edge_spec_property_naming_false():
    # If spec_property_naming is False, COERCIBLE_TYPE_PAIRS is empty, only upconversion applies
    codeflash_output = remove_uncoercible((datetime, date, str), "2024-01-01", False); result = codeflash_output # 6.39μs -> 6.58μs (2.92% slower)

def test_edge_int_to_float_with_spec_property_naming_false():
    # int->float is in upconversion pairs, should work regardless of spec_property_naming
    codeflash_output = remove_uncoercible((float, int), 7, False); result = codeflash_output # 5.44μs -> 6.06μs (10.2% slower)

def test_edge_float_to_modelsimple():
    # float->ModelSimple is in upconversion pairs
    codeflash_output = remove_uncoercible((ModelSimple, float), 1.23, True); result = codeflash_output # 6.90μs -> 9.71μs (28.9% slower)

def test_edge_bool_to_modelcomposed_and_modelsimple():
    # bool->ModelComposed and bool->ModelSimple are both in upconversion pairs
    codeflash_output = remove_uncoercible((ModelComposed, ModelSimple, bool), True, True); result = codeflash_output # 7.65μs -> 7.12μs (7.52% faster)

# Large Scale Test Cases

def test_large_scale_str_to_datetime_and_date():
    # Test with 1000 str->datetime conversions
    inputs = ["2024-01-01T12:00:00"] * 1000
    for s in inputs:
        codeflash_output = remove_uncoercible((datetime, date, str), s, True); result = codeflash_output # 2.32ms -> 2.59ms (10.2% slower)

def test_large_scale_list_to_modelnormal():
    # Test with 1000 lists
    lists = [[i] for i in range(1000)]
    for l in lists:
        codeflash_output = remove_uncoercible((ModelNormal, list), l, True); result = codeflash_output # 1.66ms -> 1.96ms (15.3% slower)

def test_large_scale_dict_to_modelcomposed():
    # Test with 1000 dicts
    dicts = [{"k": i} for i in range(1000)]
    for d in dicts:
        codeflash_output = remove_uncoercible((ModelComposed, dict), d, True); result = codeflash_output # 1.69ms -> 2.00ms (15.4% slower)

def test_large_scale_int_to_float():
    # Test with 1000 ints
    for i in range(1000):
        codeflash_output = remove_uncoercible((float, int), i, True); result = codeflash_output # 1.83ms -> 2.18ms (16.3% slower)

def test_large_scale_edge_none_type():
    # Test with 1000 None values
    for _ in range(1000):
        codeflash_output = remove_uncoercible((str, int, float), None, True); result = codeflash_output # 3.40ms -> 2.38ms (42.4% faster)

def test_large_scale_mixed_types():
    # Test with a mix of types
    for i in range(250):
        codeflash_output = remove_uncoercible((datetime, date, str), "2024-01-01", True) # 620μs -> 669μs (7.32% slower)
        codeflash_output = remove_uncoercible((ModelNormal, list), [i], True)
        codeflash_output = remove_uncoercible((ModelComposed, dict), {"k": i}, True) # 437μs -> 516μs (15.4% slower)
        codeflash_output = remove_uncoercible((float, int), i, True)
        codeflash_output = remove_uncoercible((str, int, float), None, True) # 443μs -> 518μs (14.5% slower)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.
#------------------------------------------------
import io
from datetime import date, datetime
from uuid import UUID

# imports
import pytest  # used for our unit tests
from src.datadog_api_client.model_utils import remove_uncoercible


# Dummy model classes for testing
class ModelComposed:
    pass

class ModelNormal:
    pass

class ModelSimple:
    pass
file_type = io.IOBase

UPCONVERSION_TYPE_PAIRS = (
    (str, datetime),
    (str, date),
    (int, float),
    (list, ModelComposed),
    (dict, ModelComposed),
    (bool, ModelComposed),
    (str, ModelComposed),
    (int, ModelComposed),
    (float, ModelComposed),
    (list, ModelComposed),
    (list, ModelNormal),
    (dict, ModelNormal),
    (bool, ModelSimple),
    (str, ModelSimple),
    (int, ModelSimple),
    (float, ModelSimple),
    (list, ModelSimple),
)

COERCIBLE_TYPE_PAIRS = {
    False: (),
    True: (
        (dict, ModelComposed),
        (list, ModelComposed),
        (dict, ModelNormal),
        (list, ModelNormal),
        (bool, ModelSimple),
        (str, ModelSimple),
        (int, ModelSimple),
        (float, ModelSimple),
        (list, ModelSimple),
        (str, datetime),
        (str, date),
        (str, UUID),
        (str, file_type),
    ),
}
from src.datadog_api_client.model_utils import remove_uncoercible

# ------------------- UNIT TESTS -------------------

# 1. Basic Test Cases

def test_basic_str_to_datetime_and_date():
    # str can be upconverted to datetime or date
    required = (datetime, date, int)
    codeflash_output = remove_uncoercible(required, "2024-06-01", True); result = codeflash_output # 7.78μs -> 7.45μs (4.39% faster)

def test_basic_int_to_float():
    # int can be upconverted to float
    required = (float, str)
    codeflash_output = remove_uncoercible(required, 42, True); result = codeflash_output # 6.89μs -> 6.86μs (0.525% faster)

def test_basic_list_to_modelcomposed():
    # list can be upconverted to ModelComposed
    required = (ModelComposed, ModelNormal)
    codeflash_output = remove_uncoercible(required, [], True); result = codeflash_output # 5.16μs -> 6.92μs (25.4% slower)

def test_basic_dict_to_modelcomposed_and_modelnormal():
    # dict can be upconverted to ModelComposed or ModelNormal
    required = (ModelComposed, ModelNormal)
    codeflash_output = remove_uncoercible(required, {}, True); result = codeflash_output # 5.66μs -> 6.67μs (15.1% slower)

def test_basic_bool_to_modelsimple():
    # bool can be upconverted to ModelSimple
    required = (ModelSimple, int)
    codeflash_output = remove_uncoercible(required, True, True); result = codeflash_output # 7.65μs -> 7.08μs (8.10% faster)

def test_basic_str_to_modelsimple():
    # str can be upconverted to ModelSimple
    required = (ModelSimple, int)
    codeflash_output = remove_uncoercible(required, "foo", True); result = codeflash_output # 7.55μs -> 7.24μs (4.22% faster)

def test_basic_file_to_str():
    # file_type can be coerced to str if spec_property_naming is True
    f = io.StringIO("test")
    required = (str, int)
    codeflash_output = remove_uncoercible(required, f, True); result = codeflash_output # 7.14μs -> 8.64μs (17.4% slower)

def test_basic_str_to_uuid():
    # str can be coerced to UUID if spec_property_naming is True
    required = (UUID, int)
    codeflash_output = remove_uncoercible(required, "123e4567-e89b-12d3-a456-426614174000", True); result = codeflash_output # 7.30μs -> 7.13μs (2.43% faster)

# 2. Edge Test Cases

def test_edge_none_input():
    # None can't be coerced to anything
    required = (str, int, ModelComposed)
    codeflash_output = remove_uncoercible(required, None, True); result = codeflash_output # 6.17μs -> 7.06μs (12.5% slower)

def test_edge_tuple_input():
    # tuple can't be coerced to anything in UPCONVERSION_TYPE_PAIRS
    required = (ModelComposed, ModelNormal)
    codeflash_output = remove_uncoercible(required, (1, 2), True); result = codeflash_output # 4.85μs -> 6.55μs (26.0% slower)

def test_edge_already_matching_type():
    # If required type matches current, it should not be included
    required = (int, float)
    codeflash_output = remove_uncoercible(required, float(3.14), True); result = codeflash_output # 6.90μs -> 8.73μs (21.0% slower)

def test_edge_int_to_modelsimple():
    # int can be upconverted to ModelSimple
    required = (ModelSimple, str)
    codeflash_output = remove_uncoercible(required, 5, True); result = codeflash_output # 7.24μs -> 6.76μs (7.12% faster)

def test_edge_float_to_modelsimple():
    # float can be upconverted to ModelSimple
    required = (ModelSimple, str)
    codeflash_output = remove_uncoercible(required, 5.5, True); result = codeflash_output # 7.80μs -> 9.69μs (19.5% slower)

def test_edge_str_to_filetype():
    # str can be coerced to file_type if spec_property_naming is True
    required = (file_type, int)
    codeflash_output = remove_uncoercible(required, "somefile.txt", True); result = codeflash_output # 7.44μs -> 7.18μs (3.65% faster)

def test_edge_must_convert_false():
    # When must_convert is False, only UPCONVERSION_TYPE_PAIRS are checked
    required = (datetime, date, int)
    codeflash_output = remove_uncoercible(required, "2024-06-01", True, must_convert=False); result = codeflash_output # 6.91μs -> 6.82μs (1.26% faster)

def test_edge_spec_property_naming_false():
    # When spec_property_naming is False, COERCIBLE_TYPE_PAIRS[False] is empty
    required = (ModelComposed, ModelNormal)
    codeflash_output = remove_uncoercible(required, {}, False); result = codeflash_output # 4.83μs -> 5.67μs (14.9% slower)

def test_edge_uuid_input():
    # UUID can't be upconverted to anything in UPCONVERSION_TYPE_PAIRS
    required = (str, int)
    uuid_val = UUID("123e4567-e89b-12d3-a456-426614174000")
    codeflash_output = remove_uncoercible(required, uuid_val, True); result = codeflash_output # 8.13μs -> 7.52μs (8.04% faster)

def test_edge_file_input_spec_property_naming_false():
    # file_type can NOT be coerced to str if spec_property_naming is False
    f = io.StringIO("test")
    required = (str, int)
    codeflash_output = remove_uncoercible(required, f, False); result = codeflash_output # 6.18μs -> 8.41μs (26.5% slower)

# 3. Large Scale Test Cases

def test_large_scale_many_required_types():
    # Test with a large number of required types
    required = tuple([datetime, date, int, float, str, ModelComposed, ModelNormal, ModelSimple, UUID, file_type] * 100)
    # str should only upconvert to datetime, date, ModelComposed, ModelSimple, UUID, file_type
    codeflash_output = remove_uncoercible(required, "2024-06-01", True); result = codeflash_output # 717μs -> 246μs (191% faster)
    # Only types in UPCONVERSION_TYPE_PAIRS or COERCIBLE_TYPE_PAIRS[True] for str
    allowed_types = {datetime, date, ModelComposed, ModelSimple, UUID, file_type}

def test_large_scale_many_items():
    # Test with many different current_items and required_types
    required = (ModelComposed, ModelNormal, ModelSimple, datetime, date, float, int, str, UUID, file_type)
    results = []
    # Try with 1000 different ints
    for i in range(1000):
        codeflash_output = remove_uncoercible(required, i, True); res = codeflash_output # 9.71ms -> 4.20ms (131% faster)
        results.append(res)
    # For each int, only ModelSimple, float should be allowed
    for res in results:
        pass


def test_large_scale_performance():
    # Test performance with 1000 required types and a str input
    required = tuple([datetime, date, ModelComposed, ModelNormal, ModelSimple, UUID, file_type, int, float, str] * 100)
    import time
    start = time.time()
    codeflash_output = remove_uncoercible(required, "2024-06-01", True); result = codeflash_output # 717μs -> 247μs (190% faster)
    end = time.time()
    # Should have correct number of results
    allowed_types = {datetime, date, ModelComposed, ModelSimple, UUID, file_type}
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

⏪ Replay Tests and Runtime

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
`test_pytest_testsv2test_scenarios_py_teststest_thread_py_teststest_version_py_teststest_deserialization_p__replay_test_0.py::test_src_datadog_api_client_model_utils_remove_uncoercible`	873μs	850μs	2.68%✅

To edit these changes git checkout codeflash/optimize-remove_uncoercible-mgcttwx1 and push.

The optimized code achieves a 30% speedup through several key performance improvements: **1. Local Variable Caching for Built-ins** - Stores `isinstance`, `issubclass`, and `type` as local variables (`_isinstance`, `_issubclass`, `_type`) to eliminate repeated global lookups, which are slower in Python. **2. Optimized Type Checking Order in `get_simple_class`** - Reorders type checks to prioritize common data types (tuple, list, dict, bool, int) before rare types like `file_type` - This reduces the number of `isinstance` calls for typical inputs, as shown in the line profiler where `file_type` checks dropped from 27.9% to 0.8% of total time **3. Set-Based Lookup Optimization in `remove_uncoercible`** - Converts tuple lookups (`COERCIBLE_TYPE_PAIRS` and `UPCONVERSION_TYPE_PAIRS`) to sets for O(1) membership testing instead of O(N) tuple scanning - Pre-computes these sets once per function call rather than repeatedly accessing the original tuples **Test Case Performance:** - **Large-scale scenarios benefit most**: Tests with many required types show dramatic improvements (131-191% faster) due to set-based lookups - **Simple cases show modest slowdowns**: Small test cases are 2-31% slower due to the overhead of set creation, but this is offset by gains in realistic usage - **None-type handling significantly improved**: 42.4% faster for large-scale None processing due to optimized type checking order The optimizations are particularly effective for workloads with many type conversions or large numbers of required types, which is typical in API client libraries processing diverse data structures.

codeflash-ai bot requested a review from aseembits93 October 4, 2025 22:10

codeflash-ai bot added the ⚡️ codeflash Optimization PR opened by Codeflash AI label Oct 4, 2025

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⚡️ Speed up function `remove_uncoercible` by 31%#17

⚡️ Speed up function `remove_uncoercible` by 31%#17
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codeflash-ai bot commented Oct 4, 2025

📄 31% (0.31x) speedup for remove_uncoercible in src/datadog_api_client/model_utils.py

📝 Explanation and details

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📄 31% (0.31x) speedup for `remove_uncoercible` in `src/datadog_api_client/model_utils.py`