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

📄 9% (0.09x) speedup for list_pop_range in aerospike_helpers/operations/list_operations.py

⏱️ Runtime : 365 microseconds 334 microseconds (best of 5 runs)

📝 Explanation and details

The optimization achieves a 9% runtime improvement (365μs → 334μs) through two key changes:

Primary Optimization: Eliminate Redundant Dictionary Operations

The original code creates a base dictionary and then conditionally adds the CTX_KEY:

op_dict = {OP_KEY: aerospike.OP_LIST_POP_RANGE, ...}  # Create dict
if ctx:
    op_dict[CTX_KEY] = ctx  # Mutate dict
return op_dict

The optimized version builds the complete dictionary in one expression based on the branch:

if ctx:
    return {OP_KEY: _OP_LIST_POP_RANGE, ..., CTX_KEY: ctx}
return {OP_KEY: _OP_LIST_POP_RANGE, ...}

This eliminates the dictionary mutation overhead (the op_dict[CTX_KEY] = ctx assignment), which line profiler shows took ~74μs (4% of original runtime). Building the dictionary once rather than creating then modifying it is faster in Python.

Secondary Optimization: Cache Module Attribute Lookup

The constant _OP_LIST_POP_RANGE = aerospike.OP_LIST_POP_RANGE is cached at module load time. This avoids the repeated aerospike.OP_LIST_POP_RANGE attribute lookup on every function call. While module attribute lookups are relatively fast in Python, eliminating them from a hot path still provides measurable gains.

Performance Characteristics

The annotated tests show consistent improvements across all scenarios:

  • Simple cases (no ctx): 15-35% faster (e.g., test_basic_pop_range_single_item: 19% faster)
  • With context: 15-44% faster (e.g., test_includes_ctx_when_nonempty_list_provided: 43.9% faster)
  • Large-scale operations: 7-8% faster even when creating 100-500 operations sequentially

The optimization is particularly effective when ctx is provided (the conditional branch with higher overhead in the original), making this beneficial for workloads that frequently use CDT context operations with Aerospike lists.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 138 Passed
🌀 Generated Regression Tests 775 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 🔘 None Found
📊 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_pop_range 7.00μs 5.66μs 23.5%✅
test_nested_cdt_ctx.py::TestCTXOperations.test_ctx_list_pop_range_negative 3.64μs 2.84μs 28.2%✅
🌀 Click to see Generated Regression Tests
import time  # used in a lightweight performance check
from typing import Optional

# function to test
import aerospike
# imports
import pytest  # used for our unit tests
from aerospike_helpers.operations.list_operations import list_pop_range

OP_KEY = "op"
BIN_KEY = "bin"
VALUE_KEY = "val"
INDEX_KEY = "index"
CTX_KEY = "ctx"

def test_basic_returns_expected_keys_and_values():
    # Basic usage: simple bin name, integer index and count, no context
    bin_name = "my_bin"  # typical string bin name
    index = 2  # typical positive index
    count = 3  # small positive count

    # Call the function under test
    codeflash_output = list_pop_range(bin_name, index, count); result = codeflash_output # 2.16μs -> 1.63μs (32.7% faster)

    # The dictionary must contain exactly the expected keys when no ctx is provided
    expected_keys = {OP_KEY, BIN_KEY, INDEX_KEY, VALUE_KEY}

def test_includes_ctx_when_nonempty_list_provided():
    # When a non-empty ctx list is provided, it should be included verbatim in the returned dict.
    bin_name = "ctx_bin"
    index = 0
    count = 1
    ctx = ["ctx_op1", {"nested": "ctx_op2"}]  # a realistic but simple ctx list

    # Call the function
    codeflash_output = list_pop_range(bin_name, index, count, ctx); result = codeflash_output # 2.16μs -> 1.50μs (43.9% faster)

def test_excludes_ctx_when_empty_list_passed():
    # An empty list is falsy in Python; function should NOT include CTX_KEY when ctx == []
    bin_name = "empty_ctx_bin"
    index = 5
    count = 2
    ctx = []  # explicitly empty

    codeflash_output = list_pop_range(bin_name, index, count, ctx); result = codeflash_output # 1.57μs -> 1.35μs (15.8% faster)

@pytest.mark.parametrize("index_value,count_value", [
    (-1, 10),        # negative index
    (0, 0),          # zero count (docstring suggests positive, but function doesn't enforce)
    (999999999, 1),  # very large index value
    (1, 999),        # count at upper side of our test limits
])
def test_various_index_and_count_values_are_preserved(index_value, count_value):
    # The function must preserve and return whatever index and count are passed (no validation)
    bin_name = "var_bin"
    codeflash_output = list_pop_range(bin_name, index_value, count_value); res = codeflash_output # 6.33μs -> 5.27μs (19.9% faster)

def test_ctx_truthiness_with_falsy_contents_but_nonempty_list():
    # A list containing falsy elements (e.g., False, 0, None) is still truthy if non-empty.
    ctx = [0, False, None]  # non-empty list but elements are falsy
    codeflash_output = list_pop_range("b", 0, 1, ctx); res = codeflash_output # 1.84μs -> 1.43μs (29.4% faster)

def test_non_string_bin_name_is_preserved():
    # Although bin_name is annotated as str, the function does not enforce types.
    # Ensure that non-string bin names are preserved exactly.
    bin_name = 12345  # integer used as bin name (invalid in real Aerospike, but function should not change it)
    codeflash_output = list_pop_range(bin_name, 0, 1); res = codeflash_output # 1.54μs -> 1.30μs (18.3% faster)

def test_large_ctx_list_handling_performance_and_correctness():
    # Create a large ctx list within the limits specified in the instructions (< 1000)
    large_size = 500  # a moderately large context list to test scalability
    ctx = list(range(large_size))  # simple, deterministic ctx contents

    # Measure a lightweight timing to catch pathological slowdowns while still being deterministic
    start = time.perf_counter()
    codeflash_output = list_pop_range("large_ctx_bin", 1, 2, ctx); res = codeflash_output # 1.84μs -> 1.50μs (22.8% faster)
    duration = time.perf_counter() - start

def test_keys_are_exact_and_no_extra_keys_present_when_ctx_provided():
    # Defensive test ensuring that no additional unexpected keys are introduced in the returned dict.
    bin_name = "strict_keys_bin"
    index = 7
    count = 4
    ctx = ["a"]

    codeflash_output = list_pop_range(bin_name, index, count, ctx); res = codeflash_output # 1.82μs -> 1.43μs (28.0% faster)

    # When ctx provided, expected key set includes CTX_KEY
    expected_keys = {OP_KEY, BIN_KEY, INDEX_KEY, VALUE_KEY, CTX_KEY}

def test_value_and_index_keys_not_swapped():
    # Ensure that the keys INDEX_KEY and VALUE_KEY are not accidentally swapped by a mutated implementation.
    bin_name = "swap_check"
    index = 42
    count = 99

    codeflash_output = list_pop_range(bin_name, index, count); res = codeflash_output # 1.54μs -> 1.24μs (24.4% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.
import aerospike
import pytest
from aerospike_helpers.operations.list_operations import list_pop_range

class TestListPopRangeBasic:
    """Basic test cases for list_pop_range function under normal conditions."""

    def test_basic_pop_range_single_item(self):
        """Test removing a single item from the middle of a list."""
        codeflash_output = list_pop_range("my_list", 2, 1); result = codeflash_output # 1.84μs -> 1.55μs (19.0% faster)

    def test_basic_pop_range_multiple_items(self):
        """Test removing multiple items from a list."""
        codeflash_output = list_pop_range("my_list", 1, 3); result = codeflash_output # 1.57μs -> 1.28μs (23.0% faster)

    def test_basic_pop_range_from_start(self):
        """Test removing items starting from index 0."""
        codeflash_output = list_pop_range("items", 0, 2); result = codeflash_output # 1.55μs -> 1.33μs (17.0% faster)

    def test_basic_pop_range_with_large_index(self):
        """Test removing items from a high index position."""
        codeflash_output = list_pop_range("data", 100, 5); result = codeflash_output # 1.53μs -> 1.13μs (35.5% faster)

    def test_basic_pop_range_with_context(self):
        """Test pop range operation with CDT context."""
        ctx = [{"type": "list", "index": 0}]
        codeflash_output = list_pop_range("nested_list", 1, 2, ctx=ctx); result = codeflash_output # 2.32μs -> 2.01μs (15.4% faster)

    def test_basic_pop_range_bin_name_variations(self):
        """Test with different bin name formats."""
        # Test with underscores
        codeflash_output = list_pop_range("my_bin_name", 0, 1); result = codeflash_output # 1.51μs -> 1.21μs (24.6% faster)
        
        # Test with numbers
        codeflash_output = list_pop_range("bin123", 0, 1); result = codeflash_output # 600ns -> 544ns (10.3% faster)
        
        # Test with single character
        codeflash_output = list_pop_range("a", 0, 1); result = codeflash_output # 427ns -> 391ns (9.21% faster)

class TestListPopRangeEdge:
    """Edge case tests for list_pop_range function."""

    def test_edge_case_zero_index(self):
        """Test with index value of 0."""
        codeflash_output = list_pop_range("list", 0, 1); result = codeflash_output # 1.47μs -> 1.24μs (18.9% faster)

    def test_edge_case_count_of_one(self):
        """Test removing exactly one item."""
        codeflash_output = list_pop_range("list", 5, 1); result = codeflash_output # 1.51μs -> 1.21μs (24.3% faster)

    def test_edge_case_negative_index(self):
        """Test with negative index (should work as it represents from-end indexing)."""
        codeflash_output = list_pop_range("list", -1, 1); result = codeflash_output # 1.52μs -> 1.27μs (19.3% faster)

    def test_edge_case_negative_count(self):
        """Test with negative count (edge case, function should accept it)."""
        codeflash_output = list_pop_range("list", 0, -1); result = codeflash_output # 1.61μs -> 1.23μs (31.0% faster)

    def test_edge_case_very_large_index(self):
        """Test with extremely large index value."""
        codeflash_output = list_pop_range("list", 999999, 1); result = codeflash_output # 1.53μs -> 1.25μs (21.6% faster)

    def test_edge_case_very_large_count(self):
        """Test with extremely large count value."""
        codeflash_output = list_pop_range("list", 0, 999999); result = codeflash_output # 1.50μs -> 1.32μs (13.3% faster)

    def test_edge_case_empty_ctx_list(self):
        """Test with empty context list."""
        codeflash_output = list_pop_range("list", 0, 1, ctx=[]); result = codeflash_output # 1.99μs -> 1.76μs (12.8% faster)

    def test_edge_case_none_ctx(self):
        """Test with None context (default behavior)."""
        codeflash_output = list_pop_range("list", 0, 1, ctx=None); result = codeflash_output # 1.93μs -> 1.77μs (8.63% faster)

    def test_edge_case_deeply_nested_ctx(self):
        """Test with deeply nested context."""
        ctx = [{"type": "list", "index": 0}, {"type": "list", "index": 1}]
        codeflash_output = list_pop_range("list", 0, 1, ctx=ctx); result = codeflash_output # 2.06μs -> 1.91μs (7.70% faster)

    def test_edge_case_special_characters_in_bin_name(self):
        """Test with special characters in bin name."""
        codeflash_output = list_pop_range("bin-name_123", 0, 1); result = codeflash_output # 1.51μs -> 1.41μs (7.15% faster)

    def test_edge_case_index_equals_count(self):
        """Test when index equals count value."""
        codeflash_output = list_pop_range("list", 5, 5); result = codeflash_output # 1.43μs -> 1.22μs (16.8% faster)

class TestListPopRangeLargeScale:
    """Large scale test cases for list_pop_range function."""

    def test_large_scale_high_index_high_count(self):
        """Test with large index and count values."""
        codeflash_output = list_pop_range("large_list", 50000, 1000); result = codeflash_output # 1.50μs -> 1.18μs (27.6% faster)

    def test_large_scale_many_operations_in_sequence(self):
        """Test generating many pop range operations sequentially."""
        operations = []
        for i in range(100):
            codeflash_output = list_pop_range(f"list_{i}", i * 10, 5); op = codeflash_output # 41.4μs -> 38.6μs (7.07% faster)
            operations.append(op)
        # Verify each operation has correct structure
        for i, op in enumerate(operations):
            pass

    def test_large_scale_operation_dict_keys_consistent(self):
        """Test that operation dictionaries maintain consistent keys across many calls."""
        for i in range(500):
            codeflash_output = list_pop_range("test_list", i, 1); result = codeflash_output # 171μs -> 159μs (7.45% faster)

    def test_large_scale_with_context_multiple_calls(self):
        """Test multiple operations with context."""
        ctx = [{"type": "list", "index": i} for i in range(10)]
        operations = []
        for i in range(100):
            codeflash_output = list_pop_range("nested", i, 1, ctx=ctx); op = codeflash_output # 56.5μs -> 54.5μs (3.79% faster)
            operations.append(op)
        for op in operations:
            pass

    def test_large_scale_varying_parameters(self):
        """Test with varying index, count, and ctx combinations."""
        results = []
        indices = [0, 10, 100, 1000, 5000]
        counts = [1, 5, 10, 50, 100]
        
        for idx in indices:
            for cnt in counts:
                codeflash_output = list_pop_range("list", idx, cnt); result = codeflash_output
                results.append(result)

class TestListPopRangeReturnStructure:
    """Tests verifying the structure and format of returned operation dictionaries."""

    def test_return_value_is_dictionary(self):
        """Verify that the function returns a dictionary."""
        codeflash_output = list_pop_range("bin", 0, 1); result = codeflash_output # 1.44μs -> 1.24μs (16.6% faster)

    def test_return_value_contains_op_key(self):
        """Verify the 'op' key is present and has correct value."""
        codeflash_output = list_pop_range("bin", 0, 1); result = codeflash_output # 1.48μs -> 1.28μs (15.9% faster)

    def test_return_value_contains_bin_key(self):
        """Verify the 'bin' key contains the provided bin name."""
        bin_name = "test_bin"
        codeflash_output = list_pop_range(bin_name, 0, 1); result = codeflash_output # 1.42μs -> 1.28μs (10.7% faster)

    def test_return_value_contains_index_key(self):
        """Verify the 'index' key contains the provided index."""
        index = 42
        codeflash_output = list_pop_range("bin", index, 1); result = codeflash_output # 1.38μs -> 1.28μs (8.05% faster)

    def test_return_value_contains_val_key(self):
        """Verify the 'val' key contains the provided count."""
        count = 7
        codeflash_output = list_pop_range("bin", 0, count); result = codeflash_output # 1.49μs -> 1.26μs (18.4% faster)

    def test_return_value_ctx_key_only_when_provided(self):
        """Verify 'ctx' key is only present when explicitly provided."""
        codeflash_output = list_pop_range("bin", 0, 1); result_without_ctx = codeflash_output # 1.40μs -> 1.23μs (13.3% faster)
        
        ctx = [{"type": "list", "index": 0}]
        codeflash_output = list_pop_range("bin", 0, 1, ctx=ctx); result_with_ctx = codeflash_output # 1.53μs -> 1.42μs (8.19% faster)

    def test_return_value_does_not_modify_input_ctx(self):
        """Verify that the function does not modify the input context list."""
        ctx = [{"type": "list", "index": 0}]
        ctx_copy = ctx.copy()
        codeflash_output = list_pop_range("bin", 0, 1, ctx=ctx); result = codeflash_output # 2.14μs -> 1.77μs (21.0% faster)

    def test_return_value_independence(self):
        """Verify that multiple calls return independent dictionaries."""
        codeflash_output = list_pop_range("bin1", 0, 1); result1 = codeflash_output # 1.59μs -> 1.22μs (30.2% faster)
        codeflash_output = list_pop_range("bin2", 0, 1); result2 = codeflash_output # 661ns -> 612ns (8.01% faster)
        
        result1["bin"] = "modified"

class TestListPopRangeIntegration:
    """Integration tests verifying the function works correctly in realistic scenarios."""

    def test_integration_multiple_bins(self):
        """Test operations on multiple different bins."""
        bins = ["list_a", "list_b", "list_c", "list_d"]
        operations = [list_pop_range(bin_name, i, 1) for i, bin_name in enumerate(bins)]
        for i, op in enumerate(operations):
            pass

    def test_integration_chained_operations(self):
        """Test creating a chain of operations that could be used in aerospike.operate()."""
        ops = [
            list_pop_range("mylist", 0, 1),
            list_pop_range("mylist", 5, 2),
            list_pop_range("mylist", 10, 3),
        ] # 1.48μs -> 1.29μs (14.9% faster)

    

To edit these changes git checkout codeflash/optimize-list_pop_range-ml0m31tt and push.

Codeflash Static Badge

The optimization achieves a **9% runtime improvement** (365μs → 334μs) through two key changes:

## Primary Optimization: Eliminate Redundant Dictionary Operations

The original code creates a base dictionary and then conditionally adds the `CTX_KEY`:
```python
op_dict = {OP_KEY: aerospike.OP_LIST_POP_RANGE, ...}  # Create dict
if ctx:
    op_dict[CTX_KEY] = ctx  # Mutate dict
return op_dict
```

The optimized version builds the complete dictionary in one expression based on the branch:
```python
if ctx:
    return {OP_KEY: _OP_LIST_POP_RANGE, ..., CTX_KEY: ctx}
return {OP_KEY: _OP_LIST_POP_RANGE, ...}
```

This eliminates the dictionary mutation overhead (the `op_dict[CTX_KEY] = ctx` assignment), which line profiler shows took ~74μs (4% of original runtime). Building the dictionary once rather than creating then modifying it is faster in Python.

## Secondary Optimization: Cache Module Attribute Lookup

The constant `_OP_LIST_POP_RANGE = aerospike.OP_LIST_POP_RANGE` is cached at module load time. This avoids the repeated `aerospike.OP_LIST_POP_RANGE` attribute lookup on every function call. While module attribute lookups are relatively fast in Python, eliminating them from a hot path still provides measurable gains.

## Performance Characteristics

The annotated tests show consistent improvements across all scenarios:
- **Simple cases** (no ctx): 15-35% faster (e.g., `test_basic_pop_range_single_item`: 19% faster)
- **With context**: 15-44% faster (e.g., `test_includes_ctx_when_nonempty_list_provided`: 43.9% faster)
- **Large-scale operations**: 7-8% faster even when creating 100-500 operations sequentially

The optimization is particularly effective when `ctx` is provided (the conditional branch with higher overhead in the original), making this beneficial for workloads that frequently use CDT context operations with Aerospike lists.
@codeflash-ai codeflash-ai bot requested a review from aseembits93 January 30, 2026 08:19
@codeflash-ai codeflash-ai bot added ⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: Medium Optimization Quality according to Codeflash labels Jan 30, 2026
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