⚡️ Speed up function merge_with by 22%

[codeflash-ai]

📄 22% (0.22x) speedup for merge_with in python/ccxt/static_dependencies/toolz/dicttoolz.py

⏱️ Runtime : 1.87 milliseconds → 1.54 milliseconds (best of 243 runs)

📝 Explanation and details

The optimization replaces an inefficient defaultdict pattern with a cleaner, more performant approach that achieves a 21% speedup.

Key Changes:

1. Replaced inefficient defaultdict pattern: Changed collections.defaultdict(lambda: [].append) to collections.defaultdict(list). The original creates a new empty list and binds its .append method for each key, which is both memory-wasteful and computationally expensive.

2. Direct method calls: Changed values[k](v) to values[k].append(v), eliminating the function call overhead of invoking the bound .append method through the lambda.

3. Simplified variable naming: Renamed the loop variable from v to vlist in the final loop for clarity, and removed the .__self__ attribute access on the bound method.

Why This is Faster:

The original code's lambda: [].append pattern creates overhead in two ways:

• Memory allocation: A new empty list is created for every unique key
• Method binding: The .append method is bound to each temporary list, creating function objects that must be called indirectly

The optimized version uses Python's built-in defaultdict(list) which:

• Avoids temporary objects: Lists are created only when needed and reused efficiently
• Direct method access: values[k].append(v) is a direct method call without function object overhead

Performance Impact:

From the line profiler, the critical bottleneck was in values[k](v) (40.4% of runtime in original vs 32.8% in optimized). The optimization shows consistent 10-35% improvements across test cases, with larger gains on bigger datasets where the overhead compounds.

Context Impact:

The function reference shows merge_with is used in the curried module, suggesting it may be called frequently in functional programming contexts where this 21% improvement would be significant in aggregate performance.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	🔘 None Found
🌀 Generated Regression Tests	✅ 46 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

🌀 Generated Regression Tests and Runtime

import collections
from collections.abc import Mapping

# imports
import pytest  # used for our unit tests
from ccxt.static_dependencies.toolz.dicttoolz import merge_with


# helper functions for tests
def first(lst):
    # Returns the first element of a list
    return lst[0]

def to_tuple(lst):
    # Converts a list to a tuple
    return tuple(lst)

def concat_strs(lst):
    # Concatenates a list of strings
    return ''.join(lst)

# unit tests

# ---------------- BASIC TEST CASES ----------------

def test_merge_with_sum_basic():
    # Test merging two dicts with sum function
    d1 = {1: 1, 2: 2}
    d2 = {1: 10, 2: 20}
    codeflash_output = merge_with(sum, d1, d2); result = codeflash_output # 3.80μs -> 3.28μs (15.8% faster)

def test_merge_with_first_basic():
    # Test merging two dicts with first function
    d1 = {1: 1, 2: 2}
    d2 = {2: 20, 3: 30}
    codeflash_output = merge_with(first, d1, d2); result = codeflash_output # 3.90μs -> 3.37μs (15.8% faster)

def test_merge_with_to_tuple_basic():
    # Test merging with a function that returns a tuple of values
    d1 = {'a': 1, 'b': 2}
    d2 = {'a': 3, 'c': 4}
    codeflash_output = merge_with(to_tuple, d1, d2); result = codeflash_output # 3.96μs -> 3.57μs (10.7% faster)

def test_merge_with_concat_strs_basic():
    # Test merging with string concatenation
    d1 = {'x': 'foo', 'y': 'bar'}
    d2 = {'x': 'baz', 'z': 'qux'}
    codeflash_output = merge_with(concat_strs, d1, d2); result = codeflash_output # 4.17μs -> 3.44μs (21.1% faster)

def test_merge_with_single_dict():
    # Test with a single dictionary, should just apply func to each value in a single-element list
    d = {'a': 1, 'b': 2}
    codeflash_output = merge_with(sum, d); result = codeflash_output # 4.17μs -> 3.57μs (16.7% faster)

# ---------------- EDGE TEST CASES ----------------

def test_merge_with_empty_dicts():
    # Test merging empty dicts
    codeflash_output = merge_with(sum, {}, {}); result = codeflash_output # 1.92μs -> 1.85μs (3.84% faster)

def test_merge_with_one_empty_one_nonempty():
    # Test merging one empty and one non-empty dict
    codeflash_output = merge_with(sum, {}, {'a': 1}); result = codeflash_output # 3.01μs -> 2.60μs (15.5% faster)



def test_merge_with_different_types():
    # Test with values of different types
    d1 = {'a': 1, 'b': 'foo'}
    d2 = {'a': 2, 'b': 'bar'}
    codeflash_output = merge_with(to_tuple, d1, d2); result = codeflash_output # 5.23μs -> 4.44μs (17.8% faster)

def test_merge_with_factory_kwarg():
    # Test using a custom factory (e.g., collections.OrderedDict)
    d1 = {'a': 1}
    d2 = {'a': 2, 'b': 3}
    codeflash_output = merge_with(sum, d1, d2, factory=collections.OrderedDict); result = codeflash_output # 4.81μs -> 4.33μs (11.1% faster)

def test_merge_with_unexpected_kwarg():
    # Test passing an unexpected keyword argument (should raise TypeError)
    d1 = {'a': 1}
    d2 = {'a': 2}
    with pytest.raises(TypeError):
        merge_with(sum, d1, d2, unexpected_kwarg=42) # 2.98μs -> 3.06μs (2.58% slower)

def test_merge_with_duplicate_keys():
    # Test merging dicts with duplicate keys and multiple values
    d1 = {'x': 1, 'y': 2}
    d2 = {'x': 3, 'y': 4}
    d3 = {'x': 5}
    codeflash_output = merge_with(list, d1, d2, d3); result = codeflash_output # 4.13μs -> 3.62μs (14.2% faster)

def test_merge_with_mutable_values():
    # Test merging dicts with mutable values (lists)
    d1 = {'a': [1], 'b': [2]}
    d2 = {'a': [3], 'b': [4]}
    codeflash_output = merge_with(lambda lst: sum(sum(x) for x in lst), d1, d2); result = codeflash_output # 5.16μs -> 4.58μs (12.6% faster)

def test_merge_with_none_values():
    # Test merging dicts with None values
    d1 = {'a': None}
    d2 = {'a': 5}
    codeflash_output = merge_with(to_tuple, d1, d2); result = codeflash_output # 3.18μs -> 2.91μs (9.28% faster)

def test_merge_with_key_in_one_dict_only():
    # Test merging where a key appears in only one dict
    d1 = {'a': 1}
    d2 = {'b': 2}
    codeflash_output = merge_with(list, d1, d2); result = codeflash_output # 3.41μs -> 2.91μs (17.5% faster)

def test_merge_with_empty_list_values():
    # Test merging dicts with empty list values
    d1 = {'a': []}
    d2 = {'a': [1, 2]}
    codeflash_output = merge_with(list, d1, d2); result = codeflash_output # 2.95μs -> 2.63μs (12.3% faster)

# ---------------- LARGE SCALE TEST CASES ----------------

def test_merge_with_large_number_of_dicts():
    # Test merging 100 dicts with one key, values 0..99
    dicts = [{ 'x': i } for i in range(100)]
    codeflash_output = merge_with(list, *dicts); result = codeflash_output # 12.0μs -> 12.3μs (2.92% slower)

def test_merge_with_large_dict_size():
    # Test merging two dicts with 1000 unique keys
    d1 = {i: i for i in range(1000)}
    d2 = {i: i * 2 for i in range(1000)}
    codeflash_output = merge_with(to_tuple, d1, d2); result = codeflash_output # 251μs -> 205μs (22.4% faster)
    # Each key should have a tuple (i, i*2)
    for i in range(1000):
        pass

def test_merge_with_large_dicts_and_overlap():
    # Test merging two large dicts, half overlapping keys
    d1 = {i: i for i in range(500)}
    d2 = {i: i*10 for i in range(250, 750)}
    codeflash_output = merge_with(list, d1, d2); result = codeflash_output # 156μs -> 119μs (30.8% faster)
    # Keys 0-249: only in d1; 250-499: in both; 500-749: only in d2
    for i in range(0, 250):
        pass
    for i in range(250, 500):
        pass
    for i in range(500, 750):
        pass

def test_merge_with_large_dicts_and_custom_factory():
    # Test large merge with a custom factory
    d1 = {i: i for i in range(500)}
    d2 = {i: i*2 for i in range(500)}
    codeflash_output = merge_with(sum, d1, d2, factory=dict); result = codeflash_output # 115μs -> 93.4μs (24.1% faster)
    for i in range(500):
        pass

def test_merge_with_large_number_of_keys_and_values():
    # Test merging dicts with many keys and values, using string concatenation
    d1 = {str(i): str(i) for i in range(500)}
    d2 = {str(i): str(i*2) for i in range(250, 750)}
    codeflash_output = merge_with(concat_strs, d1, d2); result = codeflash_output # 192μs -> 153μs (25.4% faster)
    # Keys 0-249: only in d1; 250-499: in both; 500-749: only in d2
    for i in range(0, 250):
        pass
    for i in range(250, 500):
        pass
    for i in range(500, 750):
        pass
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

import collections
from collections.abc import Mapping

# imports
import pytest  # used for our unit tests
from ccxt.static_dependencies.toolz.dicttoolz import merge_with

# unit tests

# Helper functions for testing
def first(lst):
    """Returns the first element of a list."""
    return lst[0]

def to_tuple(lst):
    """Returns the values as a tuple for testing."""
    return tuple(lst)

def concat_strs(lst):
    """Concatenates list of strings."""
    return ''.join(lst)

def as_set(lst):
    """Returns values as a set."""
    return set(lst)

# 1. Basic Test Cases

def test_merge_with_sum_basic():
    # Test merging two dicts with integer values using sum
    d1 = {1: 1, 2: 2}
    d2 = {1: 10, 2: 20}
    codeflash_output = merge_with(sum, d1, d2); result = codeflash_output # 4.95μs -> 3.97μs (24.7% faster)

def test_merge_with_first_basic():
    # Test merging where 'first' function is used
    d1 = {1: 1, 2: 2}
    d2 = {2: 20, 3: 30}
    codeflash_output = merge_with(first, d1, d2); result = codeflash_output # 4.08μs -> 3.44μs (18.7% faster)

def test_merge_with_concat_strs_basic():
    # Test merging dicts with string values
    d1 = {'a': 'foo', 'b': 'bar'}
    d2 = {'a': 'baz', 'c': 'qux'}
    codeflash_output = merge_with(concat_strs, d1, d2); result = codeflash_output # 4.14μs -> 3.63μs (14.0% faster)

def test_merge_with_tuple_basic():
    # Test merging dicts with tuple conversion
    d1 = {'x': 1, 'y': 2}
    d2 = {'x': 3, 'z': 4}
    codeflash_output = merge_with(to_tuple, d1, d2); result = codeflash_output # 4.17μs -> 3.60μs (15.8% faster)

# 2. Edge Test Cases

def test_merge_with_empty_dicts():
    # Merging multiple empty dicts should return empty dict
    codeflash_output = merge_with(sum, {}, {}) # 1.99μs -> 1.89μs (4.92% faster)

def test_merge_with_one_empty_one_nonempty():
    # Merging empty and non-empty dict
    d1 = {}
    d2 = {'a': 1}
    codeflash_output = merge_with(sum, d1, d2) # 3.14μs -> 2.73μs (15.2% faster)

def test_merge_with_single_dict():
    # Merging a single dict should apply func to each value as a list of one
    d = {'a': 10, 'b': 20}
    codeflash_output = merge_with(sum, d); result = codeflash_output # 4.14μs -> 3.62μs (14.2% faster)


def test_merge_with_non_mapping_in_varargs():
    # If a single non-mapping is passed, should treat as list of dicts
    d1 = {'a': 1}
    d2 = {'a': 2}
    dicts = [d1, d2]
    codeflash_output = merge_with(sum, dicts); result = codeflash_output # 5.69μs -> 4.79μs (18.9% faster)

def test_merge_with_different_types():
    # Dicts with mixed value types
    d1 = {'a': 1, 'b': 'foo'}
    d2 = {'a': 2, 'b': 'bar'}
    codeflash_output = merge_with(lambda x: str(x[0]) + str(x[1]), d1, d2); result = codeflash_output # 4.72μs -> 4.27μs (10.6% faster)

def test_merge_with_factory_kwarg():
    # Test using a custom factory (e.g., collections.OrderedDict)
    d1 = {'a': 1}
    d2 = {'b': 2}
    codeflash_output = merge_with(sum, d1, d2, factory=collections.OrderedDict); result = codeflash_output # 4.66μs -> 4.12μs (13.2% faster)

def test_merge_with_unexpected_kwarg():
    # Should raise TypeError if unexpected kwarg is passed
    d1 = {'a': 1}
    with pytest.raises(TypeError):
        merge_with(sum, d1, not_a_kwarg=123) # 3.70μs -> 3.64μs (1.68% faster)

def test_merge_with_key_collision_and_empty():
    # Key collision with empty dict
    d1 = {'x': 1}
    d2 = {}
    d3 = {'x': 2}
    codeflash_output = merge_with(sum, d1, d2, d3); result = codeflash_output # 3.49μs -> 3.27μs (6.60% faster)

def test_merge_with_set_function():
    # Using a function that returns set of values
    d1 = {'a': 1, 'b': 2}
    d2 = {'a': 2, 'c': 3}
    codeflash_output = merge_with(as_set, d1, d2); result = codeflash_output # 4.33μs -> 3.78μs (14.6% faster)

def test_merge_with_none_values():
    # Dicts with None values
    d1 = {'a': None}
    d2 = {'a': 2}
    codeflash_output = merge_with(lambda x: [v for v in x if v is not None], d1, d2); result = codeflash_output # 3.65μs -> 3.24μs (12.6% faster)

def test_merge_with_duplicate_keys_in_single_dict():
    # Python dicts cannot have duplicate keys, but test that func gets a list of one value
    d1 = {'a': 1, 'b': 2}
    codeflash_output = merge_with(list, d1); result = codeflash_output # 4.12μs -> 3.56μs (15.9% faster)

def test_merge_with_non_dict_input():
    # Passing a non-dict mapping (e.g., collections.OrderedDict)
    d1 = collections.OrderedDict([('a', 1), ('b', 2)])
    d2 = {'a': 3}
    codeflash_output = merge_with(sum, d1, d2); result = codeflash_output # 4.12μs -> 3.62μs (13.8% faster)

def test_merge_with_keys_only_in_one_dict():
    # Keys only present in one dict
    d1 = {'a': 1}
    d2 = {'b': 2}
    codeflash_output = merge_with(sum, d1, d2); result = codeflash_output # 3.40μs -> 2.87μs (18.8% faster)

def test_merge_with_func_returns_dict():
    # Function returns a dict
    d1 = {'a': 1, 'b': 2}
    d2 = {'a': 3, 'b': 4}
    codeflash_output = merge_with(lambda x: {'sum': sum(x)}, d1, d2); result = codeflash_output # 3.90μs -> 3.46μs (12.6% faster)

# 3. Large Scale Test Cases

def test_merge_with_large_dicts_sum():
    # Merge two large dicts with integer values
    d1 = {i: i for i in range(500)}
    d2 = {i: 2*i for i in range(500)}
    codeflash_output = merge_with(sum, d1, d2); result = codeflash_output # 118μs -> 94.3μs (25.7% faster)
    for i in range(500):
        pass

def test_merge_with_large_dicts_unique_keys():
    # Merge two large dicts with disjoint keys
    d1 = {i: i for i in range(500)}
    d2 = {i+500: i for i in range(500)}
    codeflash_output = merge_with(sum, d1, d2); result = codeflash_output # 192μs -> 142μs (34.4% faster)
    for i in range(500):
        pass

def test_merge_with_large_dicts_all_collisions():
    # Merge 10 dicts with all keys colliding
    dicts = [{i: i for i in range(100)} for _ in range(10)]
    codeflash_output = merge_with(sum, *dicts); result = codeflash_output # 56.1μs -> 54.6μs (2.80% faster)
    for i in range(100):
        pass

def test_merge_with_large_dicts_strings():
    # Merge dicts with string values
    dicts = [{'a': 'x'*i} for i in range(1, 11)]
    codeflash_output = merge_with(concat_strs, *dicts); result = codeflash_output # 4.45μs -> 4.14μs (7.64% faster)
    expected = ''.join(['x'*i for i in range(1, 11)])

def test_merge_with_large_dicts_sets():
    # Merge dicts with set function
    dicts = [{i: i for i in range(500)} for _ in range(2)]
    codeflash_output = merge_with(as_set, *dicts); result = codeflash_output # 139μs -> 115μs (20.6% faster)
    for i in range(500):
        pass

def test_merge_with_large_dicts_factory():
    # Use custom factory for large dict
    dicts = [{i: i for i in range(1000)}]
    codeflash_output = merge_with(sum, *dicts, factory=dict); result = codeflash_output # 196μs -> 145μs (34.7% faster)
    for i in range(1000):
        pass

def test_merge_with_large_dicts_mixed_types():
    # Mix integer and string values
    dicts = [{i: str(i) if i % 2 else i for i in range(500)} for _ in range(2)]
    codeflash_output = merge_with(lambda x: ','.join(str(v) for v in x), *dicts); result = codeflash_output # 258μs -> 231μs (11.7% faster)
    for i in range(500):
        expected = ','.join([str(dicts[0][i]), str(dicts[1][i])])

def test_merge_with_large_dicts_many_dicts():
    # Merge 20 dicts, each with 50 keys
    dicts = [{i: i for i in range(50)} for _ in range(20)]
    codeflash_output = merge_with(sum, *dicts); result = codeflash_output # 46.9μs -> 48.9μs (3.92% slower)
    for i in range(50):
        pass
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

To edit these changes git checkout codeflash/optimize-merge_with-mhy6uioq and push.