⚡️ Speed up function list_size by 5%

[codeflash-ai]

📄 5% (0.05x) speedup for list_size in aerospike_helpers/operations/list_operations.py

⏱️ Runtime : 94.5 microseconds → 89.7 microseconds (best of 5 runs)

📝 Explanation and details

The optimization achieves a 5% runtime improvement by eliminating unnecessary dictionary mutations and reducing operations. Here's what changed and why it's faster:

Key Optimization

Conditional Dictionary Construction: Instead of creating a dictionary and conditionally mutating it, the optimized code branches early and returns the appropriate dictionary in one step.

Original Approach (3 operations):

1. Create base dict with OP_KEY and BIN_KEY
2. Check if ctx exists
3. If true, mutate dict by adding CTX_KEY
4. Return dict

Optimized Approach (1-2 operations):

1. Check if ctx exists
2. Return either a 3-key dict (with ctx) or 2-key dict (without ctx) directly

Why This Is Faster

1. Eliminates Dict Mutation: The original code performs op_dict[CTX_KEY] = ctx, which requires a dictionary insertion operation after the dict is already created. The optimized version includes all keys at construction time, which is faster in Python.

2. Reduces Memory Operations: Creating the final dictionary structure in one literal expression is more efficient than creating a base dict and then modifying it, as Python can optimize dict literal construction.

3. Branch Prediction: The early conditional check allows the common path (ctx=None) to return immediately without any extra work.

Test Results Analysis

The optimization performs particularly well for:

• Basic calls without context (~5-14% faster): Most common use case benefits significantly
• Calls with complex/large context (~6-17% faster): Eliminates overhead even when ctx is large
• Repeated calls (~7-13% faster): Consistent improvement across iterations

Some edge cases with falsy values (empty strings, False, 0) show minor regressions (1-8% slower) due to the truthiness check happening earlier, but these are uncommon scenarios that don't impact the typical usage pattern.

Import Reordering

The optimization also reorders imports (typing before aerospike) to follow PEP 8 conventions, which has no performance impact but improves code organization.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 180 Passed
🌀 Generated Regression Tests	✅ 81 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	✅ 1 Passed
📊 Tests Coverage	100.0%

⚙️ Click to see Existing Unit Tests

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
test_nested_cdt_ctx.py::TestCTXOperations.test_ctx_list_size	4.07μs	3.63μs	12.2%✅
test_nested_cdt_ctx.py::TestCTXOperations.test_ctx_list_size_duplicate	6.17μs	5.12μs	20.6%✅
test_operate_helpers.py::TestOperate.test_pos_operate_with_list_size	1.52μs	1.67μs	-8.88%⚠️

🌀 Click to see Generated Regression Tests

import sys
import types
from typing import Optional

# function to test
# The following block is the ORIGINAL function implementation verbatim.
import aerospike
# imports
import pytest  # used for our unit tests
from aerospike_helpers.operations.list_operations import list_size

# Ensure an 'aerospike' module with OP_LIST_SIZE exists for the function under test.
# If the real 'aerospike' package is available in the environment, use it.
# Otherwise, insert a lightweight module into sys.modules so that the function can import it.
try:
    import aerospike as _aero_temp  # try to use the real aerospike if available
except Exception:
    # Create a minimal replacement module only if the real one is missing.
    _aero_module = types.ModuleType("aerospike")
    # Use a deterministic sentinel value for OP_LIST_SIZE so tests can assert it.
    _aero_module.OP_LIST_SIZE = 9999
    sys.modules["aerospike"] = _aero_module

OP_KEY = "op"
BIN_KEY = "bin"
CTX_KEY = "ctx"

def test_basic_returns_expected_keys_and_values():
    # Basic case: when only bin_name is provided, the returned dict should have 'op' and 'bin' keys.
    bin_name = "my_bin"
    codeflash_output = list_size(bin_name); result = codeflash_output # 1.54μs -> 1.42μs (8.32% faster)

def test_ctx_is_included_when_non_empty_list_and_identity_preserved():
    # Edge case: when ctx is a non-empty list, it should be included in the returned dict.
    ctx = [{"op": "dummy"}, {"level": 2}]  # use real Python dicts inside the list
    codeflash_output = list_size("bin_with_ctx", ctx=ctx); result = codeflash_output # 2.10μs -> 1.96μs (6.93% faster)

    # Mutating the original ctx should be observable through the returned dict (since same object).
    ctx.append({"new": True})
    # Clean up mutation for test isolation (not strictly necessary but good practice).
    ctx.pop()

def test_ctx_not_included_for_empty_list_or_other_falsy_values():
    # Edge: empty list is falsy; the function should not add the 'ctx' key for an empty list.
    empty_ctx = []
    codeflash_output = list_size("bin_empty_ctx", ctx=empty_ctx); res_empty = codeflash_output # 1.72μs -> 1.59μs (7.65% faster)

    # Also test other falsy values for ctx: None, empty tuple, empty string
    codeflash_output = list_size("bin_none_ctx", ctx=None); res_none = codeflash_output # 867ns -> 843ns (2.85% faster)

    codeflash_output = list_size("bin_empty_tuple", ctx=()); res_empty_tuple = codeflash_output # 571ns -> 744ns (23.3% slower)

    codeflash_output = list_size("bin_empty_str", ctx=""); res_empty_str = codeflash_output # 464ns -> 573ns (19.0% slower)

def test_ctx_included_for_truthy_non_list_types_and_identity_preserved():
    # Although the signature expects a list, the implementation accepts any truthy ctx.
    # Verify with a tuple (truthy) and a custom object (truthy).
    tuple_ctx = (1, 2, 3)
    codeflash_output = list_size("bin_tuple", ctx=tuple_ctx); res_tuple = codeflash_output # 1.91μs -> 1.73μs (10.1% faster)

    # Using an integer (truthy) should also result in inclusion.
    int_ctx = 42
    codeflash_output = list_size("bin_int", ctx=int_ctx); res_int = codeflash_output # 998ns -> 918ns (8.71% faster)

def test_bin_name_variants_are_preserved_unmodified():
    # Validate behavior with various bin_name edge values: empty string, unicode, long string, None.
    # Empty string bin name.
    codeflash_output = list_size(""); res_empty_bin = codeflash_output # 1.15μs -> 1.21μs (4.81% slower)

    # Unicode bin name.
    unicode_name = "unicodé_名字_👍"
    codeflash_output = list_size(unicode_name); res_unicode = codeflash_output # 631ns -> 574ns (9.93% faster)

    # Long bin name (but keep under 1000 characters per instructions).
    long_name = "b" * 500
    codeflash_output = list_size(long_name); res_long = codeflash_output # 438ns -> 414ns (5.80% faster)

    # None as bin_name: the function does not enforce type checks, so it should include None as-is.
    codeflash_output = list_size(None); res_none_bin = codeflash_output # 300ns -> 324ns (7.41% slower)

def test_returns_new_dict_each_call_and_isolation():
    # Ensure that each call returns a new dict object (no accidental reuse of a mutable dict).
    params = ("same_bin", [1, 2, 3])
    codeflash_output = list_size(params[0], ctx=params[1]); first = codeflash_output # 1.75μs -> 1.66μs (5.80% faster)
    codeflash_output = list_size(params[0], ctx=params[1]); second = codeflash_output # 694ns -> 645ns (7.60% faster)

    # Mutate the top-level dict from the first result and confirm second is not affected.
    first["extra"] = "value"

def test_large_scale_ctx_handling_under_limits():
    # Large scale test: create a ctx list with 999 elements (under the 1000 limit).
    # This verifies performance/scalability for reasonably large inputs.
    large_ctx = list(range(999))  # create 999 integers
    codeflash_output = list_size("large_bin", ctx=large_ctx); res = codeflash_output # 1.86μs -> 1.69μs (10.1% faster)

def test_op_key_matches_aerospike_constant_and_is_consistent():
    # Confirm that the 'op' key in the returned dict always equals aerospike.OP_LIST_SIZE.
    # This ensures the function references the aerospike constant as intended.
    codeflash_output = list_size("a"); r1 = codeflash_output # 1.20μs -> 1.16μs (3.79% faster)
    codeflash_output = list_size("b", ctx=[1]); r2 = codeflash_output # 1.11μs -> 1.15μs (3.64% slower)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

import aerospike
# imports
import pytest
from aerospike_helpers.operations.list_operations import list_size

class TestListSizeBasicFunctionality:
    """Test suite for basic functionality of list_size function."""

    def test_list_size_with_bin_name_only(self):
        """Test list_size returns correct structure with just bin name."""
        bin_name = "my_list"
        codeflash_output = list_size(bin_name); result = codeflash_output # 1.25μs -> 1.21μs (2.64% faster)

    def test_list_size_returns_dict_type(self):
        """Test that list_size always returns a dictionary."""
        codeflash_output = list_size("test_bin"); result = codeflash_output # 1.25μs -> 1.14μs (10.1% faster)

    def test_list_size_operation_key_correct(self):
        """Test that the operation key matches aerospike.OP_LIST_SIZE."""
        codeflash_output = list_size("any_bin"); result = codeflash_output # 1.21μs -> 1.13μs (6.53% faster)

    def test_list_size_bin_key_present(self):
        """Test that the bin key is always present in result."""
        codeflash_output = list_size("my_bin"); result = codeflash_output # 1.23μs -> 1.08μs (14.1% faster)

    def test_list_size_preserves_bin_name_exactly(self):
        """Test that the bin name is stored exactly as provided."""
        bin_names = ["bin1", "bin_name", "MyBin", "list", "data"]
        for bin_name in bin_names:
            codeflash_output = list_size(bin_name); result = codeflash_output # 2.54μs -> 2.58μs (1.55% slower)

class TestListSizeWithContext:
    """Test suite for list_size function with context parameter."""

    def test_list_size_with_none_context(self):
        """Test list_size with ctx=None explicitly."""
        codeflash_output = list_size("bin_name", ctx=None); result = codeflash_output # 1.55μs -> 1.46μs (6.59% faster)

    def test_list_size_with_empty_context_list(self):
        """Test list_size with empty context list."""
        codeflash_output = list_size("bin_name", ctx=[]); result = codeflash_output # 1.64μs -> 1.54μs (6.15% faster)

    def test_list_size_with_single_context_item(self):
        """Test list_size with a single context item."""
        # Create a mock context item (using a simple dict as placeholder)
        ctx_item = {"type": "index", "value": 0}
        codeflash_output = list_size("bin_name", ctx=[ctx_item]); result = codeflash_output # 1.83μs -> 1.61μs (13.9% faster)

    def test_list_size_with_multiple_context_items(self):
        """Test list_size with multiple context items."""
        ctx_items = [
            {"type": "index", "value": 0},
            {"type": "rank", "value": 1},
            {"type": "key", "value": "nested_key"}
        ]
        codeflash_output = list_size("bin_name", ctx=ctx_items); result = codeflash_output # 1.78μs -> 1.58μs (13.0% faster)

    def test_list_size_with_context_preserves_all_fields(self):
        """Test that all required fields are present when context is provided."""
        ctx = [{"type": "index", "value": 0}]
        codeflash_output = list_size("test_bin", ctx=ctx); result = codeflash_output # 1.66μs -> 1.59μs (4.09% faster)

    def test_list_size_dict_keys_only_expected(self):
        """Test that returned dict only contains expected keys."""
        codeflash_output = list_size("bin_name"); result = codeflash_output # 1.20μs -> 1.18μs (1.78% faster)
        expected_keys = {"op", "bin"}

    def test_list_size_dict_keys_with_context(self):
        """Test that returned dict has correct keys when context is provided."""
        ctx = [{"type": "index"}]
        codeflash_output = list_size("bin_name", ctx=ctx); result = codeflash_output # 1.82μs -> 1.58μs (15.0% faster)
        expected_keys = {"op", "bin", "ctx"}

class TestListSizeEdgeCases:
    """Test suite for edge cases in list_size function."""

    def test_list_size_with_empty_bin_name(self):
        """Test list_size with an empty string as bin name."""
        codeflash_output = list_size(""); result = codeflash_output # 1.15μs -> 1.10μs (4.91% faster)

    def test_list_size_with_special_characters_in_bin_name(self):
        """Test list_size with special characters in bin name."""
        special_bin_names = [
            "bin_with_underscore",
            "bin-with-dash",
            "bin.with.dot",
            "bin:with:colon",
            "bin@with@at",
            "bin#with#hash",
            "bin$with$dollar",
        ]
        
        for bin_name in special_bin_names:
            codeflash_output = list_size(bin_name); result = codeflash_output # 3.27μs -> 3.24μs (0.833% faster)

    def test_list_size_with_whitespace_in_bin_name(self):
        """Test list_size with whitespace characters in bin name."""
        whitespace_names = [
            " leading_space",
            "trailing_space ",
            " both_spaces ",
            "bin\twith\ttabs",
            "bin\nwith\nnewlines",
        ]
        
        for bin_name in whitespace_names:
            codeflash_output = list_size(bin_name); result = codeflash_output # 2.41μs -> 2.42μs (0.372% slower)

    def test_list_size_with_numeric_bin_name(self):
        """Test list_size with numeric string as bin name."""
        codeflash_output = list_size("12345"); result = codeflash_output # 1.11μs -> 1.06μs (4.23% faster)

    def test_list_size_with_unicode_bin_name(self):
        """Test list_size with unicode characters in bin name."""
        unicode_names = [
            "bin_\u00e9",  # é
            "bin_\u4e2d\u6587",  # Chinese characters
            "bin_\u0627\u0644\u0639\u0631\u0628\u064a\u0629",  # Arabic
        ]
        
        for bin_name in unicode_names:
            codeflash_output = list_size(bin_name); result = codeflash_output # 2.01μs -> 1.93μs (4.14% faster)

    def test_list_size_context_with_nested_structures(self):
        """Test list_size with complex nested context structures."""
        complex_ctx = [
            {"type": "index", "value": 0, "nested": {"key": "value"}},
            {"type": "rank", "value": 1, "list": [1, 2, 3]},
        ]
        codeflash_output = list_size("bin_name", ctx=complex_ctx); result = codeflash_output # 1.69μs -> 1.66μs (1.69% faster)

    def test_list_size_multiple_calls_independent(self):
        """Test that multiple calls to list_size don't affect each other."""
        codeflash_output = list_size("bin1", ctx=[{"type": "index"}]); result1 = codeflash_output # 1.76μs -> 1.63μs (7.66% faster)
        codeflash_output = list_size("bin2", ctx=[{"type": "rank"}]); result2 = codeflash_output # 693ns -> 662ns (4.68% faster)
        codeflash_output = list_size("bin3"); result3 = codeflash_output # 574ns -> 683ns (16.0% slower)

    def test_list_size_returns_new_dict_each_time(self):
        """Test that list_size returns a new dict object each time."""
        codeflash_output = list_size("bin"); result1 = codeflash_output # 1.21μs -> 1.07μs (12.9% faster)
        codeflash_output = list_size("bin"); result2 = codeflash_output # 460ns -> 441ns (4.31% faster)

    def test_list_size_with_very_long_bin_name(self):
        """Test list_size with a very long bin name."""
        long_bin_name = "a" * 999
        codeflash_output = list_size(long_bin_name); result = codeflash_output # 1.18μs -> 1.13μs (4.87% faster)

    def test_list_size_context_false_value_not_added(self):
        """Test that falsy context values are not added to dict."""
        # Test with various falsy values
        codeflash_output = list_size("bin", ctx=None); result1 = codeflash_output # 1.45μs -> 1.48μs (2.36% slower)
        codeflash_output = list_size("bin", ctx=[]); result2 = codeflash_output # 875ns -> 774ns (13.0% faster)
        codeflash_output = list_size("bin", ctx=False); result3 = codeflash_output # 533ns -> 585ns (8.89% slower)
        codeflash_output = list_size("bin", ctx=0); result4 = codeflash_output # 456ns -> 465ns (1.94% slower)
        codeflash_output = list_size("bin", ctx=""); result5 = codeflash_output # 417ns -> 450ns (7.33% slower)
        
        # All falsy contexts should result in no ctx key
        for result in [result1, result2, result3, result4, result5]:
            pass

class TestListSizeLargeScale:
    """Test suite for large-scale scenarios with list_size function."""

    def test_list_size_with_large_context_list(self):
        """Test list_size with a large context list."""
        large_ctx = [
            {"type": "index", "value": i, "metadata": f"ctx_{i}"}
            for i in range(100)
        ]
        codeflash_output = list_size("bin_name", ctx=large_ctx); result = codeflash_output # 1.80μs -> 1.70μs (5.93% faster)

    def test_list_size_with_deeply_nested_context(self):
        """Test list_size with deeply nested context structures."""
        # Create deeply nested structure
        nested_ctx = {"level": 0}
        current = nested_ctx
        for i in range(50):
            current["nested"] = {"level": i + 1}
            current = current["nested"]
        
        codeflash_output = list_size("bin", ctx=[nested_ctx]); result = codeflash_output # 1.66μs -> 1.69μs (1.54% slower)

    def test_list_size_repeated_calls_performance(self):
        """Test that repeated calls to list_size work correctly."""
        bin_names = [f"bin_{i}" for i in range(100)]
        results = [list_size(bin_name) for bin_name in bin_names]
        
        # Verify all results are correct
        for i, result in enumerate(results):
            pass

    def test_list_size_with_large_complex_context(self):
        """Test list_size with large and complex context data."""
        complex_ctx = [
            {
                "type": "index",
                "value": i,
                "data": {
                    "nested_list": list(range(10)),
                    "nested_dict": {f"key_{j}": f"value_{j}" for j in range(10)},
                    "string": "test_value_" * 10,
                }
            }
            for i in range(50)
        ]
        codeflash_output = list_size("bin_name", ctx=complex_ctx); result = codeflash_output # 1.92μs -> 1.68μs (14.1% faster)

    def test_list_size_bin_name_at_max_practical_length(self):
        """Test list_size with bin name at practical maximum length."""
        # Aerospike typically has bin name limits around 16 bytes, but test practical large sizes
        large_bin_name = "x" * 500
        codeflash_output = list_size(large_bin_name); result = codeflash_output # 1.20μs -> 1.16μs (3.98% faster)

class TestListSizeConsistency:
    """Test suite for consistency and invariants in list_size function."""

    def test_list_size_always_includes_operation_key(self):
        """Test that operation key is always present."""
        test_cases = [
            ("bin1",),
            ("bin2", None),
            ("bin3", []),
            ("bin4", [{"type": "index"}]),
        ]
        
        for args in test_cases:
            if len(args) == 1:
                codeflash_output = list_size(args[0]); result = codeflash_output
            else:
                codeflash_output = list_size(args[0], ctx=args[1]); result = codeflash_output

    def test_list_size_always_includes_bin_key(self):
        """Test that bin key is always present."""
        test_cases = [
            ("bin1",),
            ("bin2", None),
            ("bin3", []),
            ("bin4", [{"type": "index"}]),
        ]
        
        for args in test_cases:
            if len(args) == 1:
                codeflash_output = list_size(args[0]); result = codeflash_output
            else:
                codeflash_output = list_size(args[0], ctx=args[1]); result = codeflash_output

    def test_list_size_no_unexpected_keys(self):
        """Test that result only contains expected keys."""
        codeflash_output = list_size("bin"); result1 = codeflash_output # 1.14μs -> 1.10μs (3.72% faster)
        allowed_keys_without_ctx = {"op", "bin"}
        
        codeflash_output = list_size("bin", ctx=[{"type": "index"}]); result2 = codeflash_output # 1.22μs -> 1.22μs (0.082% slower)

    def test_list_size_operation_value_immutable(self):
        """Test that the operation value is always the correct constant."""
        expected_op = aerospike.OP_LIST_SIZE
        
        results = [
            list_size("bin1"),
            list_size("bin2", ctx=None),
            list_size("bin3", ctx=[{"type": "index"}]),
        ] # 1.11μs -> 986ns (12.9% faster)
        
        for result in results:
            pass

    def test_list_size_preserves_context_order(self):
        """Test that context list order is preserved."""
        ctx = [
            {"id": 1},
            {"id": 2},
            {"id": 3},
            {"id": 4},
            {"id": 5},
        ]
        codeflash_output = list_size("bin", ctx=ctx); result = codeflash_output # 1.84μs -> 1.57μs (17.3% faster)
        
        # Verify order is preserved
        for i, item in enumerate(result["ctx"]):
            pass

    def test_list_size_with_default_parameter(self):
        """Test list_size using default parameter for ctx."""
        # Call with positional argument only
        codeflash_output = list_size("bin_name"); result1 = codeflash_output # 1.18μs -> 1.13μs (4.88% faster)
        
        # Call with keyword argument explicitly set to None
        codeflash_output = list_size("bin_name", ctx=None); result2 = codeflash_output # 935ns -> 866ns (7.97% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

from aerospike_helpers.operations.list_operations import list_size

def test_list_size():
    list_size('', ctx=[''])

🔎 Click to see Concolic Coverage Tests

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
codeflash_concolic_hw2hs1n8/tmpadhq8f88/test_concolic_coverage.py::test_list_size	2.16μs	2.16μs	-0.139%⚠️

To edit these changes git checkout codeflash/optimize-list_size-ml0nebk9 and push.