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@codeflash-ai codeflash-ai bot commented Jan 30, 2026

📄 24% (0.24x) speedup for _create_operator_expression in aerospike_helpers/expressions/resources.py

⏱️ Runtime : 109 microseconds 88.5 microseconds (best of 8 runs)

📝 Explanation and details

The optimization achieves a 23% runtime improvement (109μs → 88.5μs) through two key changes:

1. Stack-Based Traversal in compile() (Primary Speedup)

The original code used itertools.chain() with generator-based iteration, calling next() repeatedly. The optimized version replaces this with a list-based stack that uses .pop() and .extend() operations. This eliminates:

  • Generator overhead from chain()
  • Exception handling overhead from StopIteration
  • Function call overhead from next()

Python's list operations (.pop(), .extend()) are implemented in C and are significantly faster than generator-based iteration for this use case. The children are reversed when pushed onto the stack to maintain proper processing order.

2. Explicit Tuple Conversion in _create_operator_expression()

The original unpacking syntax (*left_children, *right_children) performs implicit type conversion and creates intermediate objects. The optimized version:

  • Uses explicit isinstance() checks (fast type checks in Python)
  • Only converts to tuple when needed (most calls pass tuples directly from _overload_op* methods)
  • Uses direct tuple concatenation (+) which is a C-level operation

Looking at function_references, _create_operator_expression() is called from operator overload methods (__add__, __sub__, __mul__, etc.) which are likely in hot paths when building complex expression trees. The optimization reduces allocation overhead when chaining multiple operators.

Test Results

The optimization shows significant gains (36-92% faster) for large-scale scenarios with many children (100-600 elements), while maintaining correctness for all edge cases. Small regressions in some micro-benchmarks (mostly < 7%) are overwhelmed by the substantial gains in realistic workloads, resulting in the overall 23% runtime improvement.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 44 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 100.0%
🌀 Click to see Generated Regression Tests
from typing import (Dict, Optional,  # used to mirror the original annotations
                    Tuple, Union)

# imports
import pytest  # used for our unit tests
from aerospike_helpers.expressions.resources import _create_operator_expression

# function to test (preserved EXACT implementation and signature)
# NOTE: We define minimal supporting classes (_AtomExpr and _BaseExpr) above the type aliases
# so that the type aliases and the function can be evaluated at import-time in this test file.
class _AtomExpr:
    """Minimal, concrete Atom-like expression used only for testing purposes.

    This small class exists so that the type hints and runtime references in the
    original function can be resolved in the test module. It intentionally does
    not mimic any external library - it simply provides an object whose identity
    can be placed in children and later verified.
    """
    def __init__(self, value=None):
        self.value = value

    def __repr__(self):
        return f"_AtomExpr({self.value!r})"

def test_basic_operator_expression_order_and_types():
    # Basic scenario: ensure operator and children are set and order preserved
    left = (1, "left", 3.14)  # tuple with mixed basic types
    right = (b"bytes", "right")  # tuple with bytes and string
    op = 42  # arbitrary op_type

    codeflash_output = _create_operator_expression(left, right, op); expr = codeflash_output # 1.83μs -> 1.73μs (5.71% faster)

def test_children_can_include_atom_instances_and_identity_preserved():
    # Edge case: children containing _AtomExpr instances should preserve identity
    atom1 = _AtomExpr("a")
    atom2 = _AtomExpr("b")

    left = (atom1,)
    right = (atom2,)

    codeflash_output = _create_operator_expression(left, right, op_type=7); expr = codeflash_output # 2.11μs -> 1.99μs (6.14% faster)

    # Mutating an attribute on the Atom instance should be visible through expr._children
    atom1.value = "changed"

def test_empty_children_and_various_op_values():
    # Edge case: both left and right children empty
    codeflash_output = _create_operator_expression(tuple(), tuple(), op_type=0); expr_empty = codeflash_output # 2.04μs -> 1.95μs (4.83% faster)

    # Also check with negative and very large op_type integers
    codeflash_output = _create_operator_expression((), (), op_type=-1); expr_negative = codeflash_output # 750ns -> 805ns (6.83% slower)

    large_op = 2**31  # large integer value (still an int)
    codeflash_output = _create_operator_expression((), (), op_type=large_op); expr_large = codeflash_output # 917ns -> 929ns (1.29% slower)

def test_mutable_children_elements_reflect_mutation_after_creation():
    # Edge scenario: children contain mutable objects such as dict; the expression
    # should store a reference to that object (not a deep copy), so modifications
    # after creation are visible via expr._children.
    mutable = {"count": 1}
    left = (mutable,)
    right = ()

    codeflash_output = _create_operator_expression(left, right, op_type=5); expr = codeflash_output # 1.89μs -> 1.96μs (3.27% slower)

    # Mutate the original dictionary
    mutable["count"] = 999

def test_returns_new_independent_instance_each_call():
    # Create two expressions with identical inputs and ensure they are different objects.
    left = (1,)
    right = (2,)
    codeflash_output = _create_operator_expression(left, right, op_type=3); a = codeflash_output # 2.07μs -> 1.99μs (4.02% faster)
    codeflash_output = _create_operator_expression(left, right, op_type=3); b = codeflash_output # 816ns -> 879ns (7.17% slower)

    # Mutating one instance's attributes should not affect the other instance.
    a._op = 7
    a._children = ("changed",)

def test_large_scale_many_children_under_limit():
    # Large-scale test: create many children but keep total under 1000 elements per instructions.
    left = tuple(range(500))  # 500 elements
    right = tuple(range(500, 999))  # 499 elements, total 999
    codeflash_output = _create_operator_expression(left, right, op_type=99); expr = codeflash_output # 8.00μs -> 4.16μs (92.3% faster)

@pytest.mark.parametrize("left,right,expected", [
    # Various element types ensure TypeFixedEle allowed types behave correctly
    ((1,), (2,), (1, 2)),
    ((3.14,), (b"x",), (3.14, b"x")),
    (("s",), ({"k": "v"},), ("s", {"k": "v"})),
])
def test_various_element_types(left, right, expected):
    # Parametrized test to check common allowed element types are preserved
    codeflash_output = _create_operator_expression(left, right, op_type=1); expr = codeflash_output # 5.95μs -> 5.85μs (1.81% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.
import pytest
from aerospike_helpers.expressions.resources import (
    _BaseExpr, _create_operator_expression)

def test_basic_operator_expression_with_empty_children():
    """Test creating an operator expression with empty left and right children."""
    left_children = ()
    right_children = ()
    op_type = 1
    
    codeflash_output = _create_operator_expression(left_children, right_children, op_type); result = codeflash_output # 2.08μs -> 2.24μs (7.09% slower)

def test_basic_operator_expression_with_single_children():
    """Test creating an operator expression with single child in each side."""
    left_children = (1,)
    right_children = (2,)
    op_type = 5
    
    codeflash_output = _create_operator_expression(left_children, right_children, op_type); result = codeflash_output # 1.56μs -> 1.85μs (15.7% slower)

def test_basic_operator_expression_with_string_children():
    """Test creating an operator expression with string children."""
    left_children = ("a", "b")
    right_children = ("c", "d")
    op_type = 10
    
    codeflash_output = _create_operator_expression(left_children, right_children, op_type); result = codeflash_output # 1.74μs -> 1.62μs (7.47% faster)

def test_basic_operator_expression_with_float_children():
    """Test creating an operator expression with float children."""
    left_children = (1.5, 2.5)
    right_children = (3.5,)
    op_type = 15
    
    codeflash_output = _create_operator_expression(left_children, right_children, op_type); result = codeflash_output # 1.74μs -> 1.66μs (4.51% faster)

def test_basic_operator_expression_with_bytes_children():
    """Test creating an operator expression with bytes children."""
    left_children = (b"hello",)
    right_children = (b"world",)
    op_type = 20
    
    codeflash_output = _create_operator_expression(left_children, right_children, op_type); result = codeflash_output # 1.50μs -> 1.52μs (1.31% slower)

def test_basic_operator_expression_with_mixed_type_children():
    """Test creating an operator expression with mixed type children."""
    left_children = (1, "test", 3.14)
    right_children = (b"bytes", 42)
    op_type = 25
    
    codeflash_output = _create_operator_expression(left_children, right_children, op_type); result = codeflash_output # 1.84μs -> 1.64μs (12.6% faster)

def test_basic_operator_expression_with_zero_op_type():
    """Test creating an operator expression with op_type of 0."""
    left_children = (1,)
    right_children = (2,)
    op_type = 0
    
    codeflash_output = _create_operator_expression(left_children, right_children, op_type); result = codeflash_output # 1.53μs -> 1.44μs (6.18% faster)

def test_edge_case_large_op_type():
    """Test creating an operator expression with a very large op_type value."""
    left_children = (1,)
    right_children = (2,)
    op_type = 999999999
    
    codeflash_output = _create_operator_expression(left_children, right_children, op_type); result = codeflash_output # 1.52μs -> 1.52μs (0.000% faster)

def test_edge_case_negative_op_type():
    """Test creating an operator expression with negative op_type."""
    left_children = (1,)
    right_children = (2,)
    op_type = -1
    
    codeflash_output = _create_operator_expression(left_children, right_children, op_type); result = codeflash_output # 1.60μs -> 1.57μs (2.10% faster)

def test_edge_case_only_left_children():
    """Test creating an operator expression with only left children."""
    left_children = (1, 2, 3, 4, 5)
    right_children = ()
    op_type = 7
    
    codeflash_output = _create_operator_expression(left_children, right_children, op_type); result = codeflash_output # 1.64μs -> 1.54μs (6.02% faster)

def test_edge_case_only_right_children():
    """Test creating an operator expression with only right children."""
    left_children = ()
    right_children = (10, 20, 30)
    op_type = 8
    
    codeflash_output = _create_operator_expression(left_children, right_children, op_type); result = codeflash_output # 1.67μs -> 1.55μs (7.75% faster)

def test_edge_case_single_element_on_each_side():
    """Test creating an operator expression with single element on each side."""
    left_children = (100,)
    right_children = (200,)
    op_type = 50
    
    codeflash_output = _create_operator_expression(left_children, right_children, op_type); result = codeflash_output # 1.58μs -> 1.56μs (1.47% faster)

def test_edge_case_special_string_values():
    """Test creating an operator expression with special string values."""
    left_children = ("", "special!@#$%")
    right_children = ("unicode_😀",)
    op_type = 60
    
    codeflash_output = _create_operator_expression(left_children, right_children, op_type); result = codeflash_output # 1.72μs -> 1.70μs (1.71% faster)

def test_edge_case_empty_string_in_children():
    """Test creating an operator expression with empty strings in children."""
    left_children = ("", "nonempty")
    right_children = ("", "")
    op_type = 61
    
    codeflash_output = _create_operator_expression(left_children, right_children, op_type); result = codeflash_output # 1.70μs -> 1.65μs (3.28% faster)

def test_edge_case_empty_bytes_in_children():
    """Test creating an operator expression with empty bytes in children."""
    left_children = (b"", b"nonempty")
    right_children = (b"",)
    op_type = 62
    
    codeflash_output = _create_operator_expression(left_children, right_children, op_type); result = codeflash_output # 1.70μs -> 1.64μs (3.67% faster)

def test_edge_case_zero_float_values():
    """Test creating an operator expression with zero float values."""
    left_children = (0.0, -0.0)
    right_children = (0.0,)
    op_type = 63
    
    codeflash_output = _create_operator_expression(left_children, right_children, op_type); result = codeflash_output # 1.64μs -> 1.55μs (5.87% faster)

def test_edge_case_negative_numbers():
    """Test creating an operator expression with negative numbers."""
    left_children = (-1, -2, -3)
    right_children = (-100, -999)
    op_type = 64
    
    codeflash_output = _create_operator_expression(left_children, right_children, op_type); result = codeflash_output # 1.78μs -> 1.66μs (7.54% faster)

def test_edge_case_very_large_numbers():
    """Test creating an operator expression with very large numbers."""
    left_children = (10**15, 10**20)
    right_children = (10**100,)
    op_type = 65
    
    codeflash_output = _create_operator_expression(left_children, right_children, op_type); result = codeflash_output # 1.66μs -> 1.67μs (0.120% slower)

def test_edge_case_very_small_float_values():
    """Test creating an operator expression with very small float values."""
    left_children = (1e-100, 1e-300)
    right_children = (1e-15,)
    op_type = 66
    
    codeflash_output = _create_operator_expression(left_children, right_children, op_type); result = codeflash_output # 1.69μs -> 1.56μs (7.87% faster)

def test_edge_case_duplicate_values():
    """Test creating an operator expression with duplicate values."""
    left_children = (5, 5, 5)
    right_children = (5, 5)
    op_type = 67
    
    codeflash_output = _create_operator_expression(left_children, right_children, op_type); result = codeflash_output # 1.82μs -> 1.66μs (9.27% faster)

def test_edge_case_boolean_like_values():
    """Test creating an operator expression with integer values that could be interpreted as booleans."""
    left_children = (0, 1)
    right_children = (1, 0)
    op_type = 68
    
    codeflash_output = _create_operator_expression(left_children, right_children, op_type); result = codeflash_output # 1.79μs -> 1.57μs (14.2% faster)

def test_large_scale_many_left_children():
    """Test creating an operator expression with many left children."""
    left_children = tuple(range(100))
    right_children = (999,)
    op_type = 100
    
    codeflash_output = _create_operator_expression(left_children, right_children, op_type); result = codeflash_output # 2.78μs -> 2.03μs (36.9% faster)

def test_large_scale_many_right_children():
    """Test creating an operator expression with many right children."""
    left_children = (1,)
    right_children = tuple(range(100, 200))
    op_type = 101
    
    codeflash_output = _create_operator_expression(left_children, right_children, op_type); result = codeflash_output # 2.36μs -> 1.97μs (19.7% faster)

def test_large_scale_balanced_children():
    """Test creating an operator expression with balanced large children tuples."""
    left_children = tuple(range(250))
    right_children = tuple(range(250, 500))
    op_type = 102
    
    codeflash_output = _create_operator_expression(left_children, right_children, op_type); result = codeflash_output # 5.14μs -> 2.80μs (83.4% faster)

def test_large_scale_many_string_children():
    """Test creating an operator expression with many string children."""
    left_children = tuple(f"left_{i}" for i in range(150))
    right_children = tuple(f"right_{i}" for i in range(150))
    op_type = 103
    
    codeflash_output = _create_operator_expression(left_children, right_children, op_type); result = codeflash_output # 4.40μs -> 2.85μs (54.2% faster)

def test_large_scale_many_float_children():
    """Test creating an operator expression with many float children."""
    left_children = tuple(float(i) * 1.5 for i in range(200))
    right_children = tuple(float(i) * 2.5 for i in range(200))
    op_type = 104
    
    codeflash_output = _create_operator_expression(left_children, right_children, op_type); result = codeflash_output # 4.92μs -> 2.77μs (77.8% faster)

def test_large_scale_many_bytes_children():
    """Test creating an operator expression with many bytes children."""
    left_children = tuple(f"bytes_{i}".encode() for i in range(100))
    right_children = tuple(f"data_{i}".encode() for i in range(100))
    op_type = 105
    
    codeflash_output = _create_operator_expression(left_children, right_children, op_type); result = codeflash_output # 3.56μs -> 2.47μs (44.5% faster)

def test_large_scale_heterogeneous_large_children():
    """Test creating an operator expression with large heterogeneous children."""
    left_children = tuple([i, f"str_{i}", float(i), bytes(f"b_{i}", 'utf-8')] for i in range(50))
    left_children = tuple(elem for sublist in left_children for elem in sublist)
    right_children = tuple([i * 10, f"right_{i}", float(i) / 2.0] for i in range(50))
    right_children = tuple(elem for sublist in right_children for elem in sublist)
    op_type = 106
    
    codeflash_output = _create_operator_expression(left_children, right_children, op_type); result = codeflash_output # 4.58μs -> 2.80μs (63.8% faster)

def test_large_scale_deeply_nested_structure():
    """Test creating an operator expression with maximum safe nesting."""
    left_children = tuple(i for i in range(300))
    right_children = tuple(i * 2 for i in range(300))
    op_type = 107
    
    codeflash_output = _create_operator_expression(left_children, right_children, op_type); result = codeflash_output # 5.42μs -> 2.83μs (91.7% faster)

def test_large_scale_children_preservation():
    """Test that large children are correctly preserved without modification."""
    # Create large tuples with specific values
    large_left = tuple(range(0, 500, 2))
    large_right = tuple(range(1, 500, 2))
    op_type = 108
    
    codeflash_output = _create_operator_expression(large_left, large_right, op_type); result = codeflash_output # 5.05μs -> 2.95μs (71.0% faster)
    # Check that children match exactly
    for i, val in enumerate(large_left):
        pass
    for i, val in enumerate(large_right):
        pass

def test_large_scale_alternating_pattern():
    """Test with alternating pattern of large children."""
    left_children = tuple(i if i % 2 == 0 else f"odd_{i}" for i in range(200))
    right_children = tuple(i * 100 if i % 3 == 0 else f"non_div_{i}" for i in range(200))
    op_type = 109
    
    codeflash_output = _create_operator_expression(left_children, right_children, op_type); result = codeflash_output # 4.83μs -> 2.92μs (65.6% faster)
# 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-_create_operator_expression-ml0hyh3r and push.

Codeflash Static Badge

The optimization achieves a **23% runtime improvement** (109μs → 88.5μs) through two key changes:

## 1. Stack-Based Traversal in `compile()` (Primary Speedup)
The original code used `itertools.chain()` with generator-based iteration, calling `next()` repeatedly. The optimized version replaces this with a **list-based stack** that uses `.pop()` and `.extend()` operations. This eliminates:
- Generator overhead from `chain()`
- Exception handling overhead from `StopIteration`
- Function call overhead from `next()`

Python's list operations (`.pop()`, `.extend()`) are implemented in C and are significantly faster than generator-based iteration for this use case. The children are reversed when pushed onto the stack to maintain proper processing order.

## 2. Explicit Tuple Conversion in `_create_operator_expression()`
The original unpacking syntax `(*left_children, *right_children)` performs implicit type conversion and creates intermediate objects. The optimized version:
- Uses explicit `isinstance()` checks (fast type checks in Python)
- Only converts to tuple when needed (most calls pass tuples directly from `_overload_op*` methods)
- Uses direct tuple concatenation (`+`) which is a C-level operation

Looking at `function_references`, `_create_operator_expression()` is called from operator overload methods (`__add__`, `__sub__`, `__mul__`, etc.) which are likely in hot paths when building complex expression trees. The optimization reduces allocation overhead when chaining multiple operators.

## Test Results
The optimization shows **significant gains (36-92% faster) for large-scale scenarios** with many children (100-600 elements), while maintaining correctness for all edge cases. Small regressions in some micro-benchmarks (mostly < 7%) are overwhelmed by the substantial gains in realistic workloads, resulting in the overall 23% runtime improvement.
@codeflash-ai codeflash-ai bot requested a review from aseembits93 January 30, 2026 06:23
@codeflash-ai codeflash-ai bot added ⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: High Optimization Quality according to Codeflash labels Jan 30, 2026
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