Skip to content

Conversation

@codeflash-ai
Copy link

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

📄 7% (0.07x) speedup for list_remove in aerospike_helpers/operations/list_operations.py

⏱️ Runtime : 803 microseconds 751 microseconds (best of 5 runs)

📝 Explanation and details

The optimized code achieves a 7% runtime improvement through two key changes:

1. Eliminated redundant dictionary variable creation
The original code creates op_dict unconditionally, then conditionally adds the ctx key, and finally returns it. The optimized version uses early returns with inline dictionary literals:

  • When ctx is truthy: returns a 4-key dictionary immediately
  • Otherwise: returns a 3-key dictionary immediately

This eliminates the intermediate variable and reduces memory operations.

2. Replaced constant lookups with string literals
The original code references module-level constants (OP_KEY, BIN_KEY, INDEX_KEY, CTX_KEY) for dictionary keys. The optimized version uses direct string literals ("op", "bin", "index", "ctx"). While this seems minor, it removes ~4 attribute lookups per function call, which compounds when the function is called frequently.

Why this matters:
From the line profiler data:

  • Original: op_dict = {OP_KEY: ...} takes 35.8% of total time (1.797ms)
  • Optimized: The conditional check if ctx: only takes 37.1% of total time (1.288ms), but performs more efficiently overall

The optimization is particularly effective for the common case (no ctx provided, which occurs in 2297 of 2312 calls = 99.4%). Test results confirm this:

  • Tests without ctx show 6-27% speedup (most common path)
  • Tests with ctx show 10-42% speedup
  • Large-scale stress tests (creating 100-1000 operations) show consistent 4-8% improvements

This function appears to be a factory for operation dictionaries in the Aerospike client library. Since database operations often batch multiple commands, even small per-call savings multiply across workloads that create many list operations.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 139 Passed
🌀 Generated Regression Tests 2256 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_remove 5.72μs 4.82μs 18.9%✅
test_nested_cdt_ctx.py::TestCTXOperations.test_ctx_list_remove_negative 4.39μs 3.47μs 26.5%✅
🌀 Click to see Generated Regression Tests
import aerospike  # used to verify the Aerospike operation constant used by the function
# imports
import pytest  # used for our unit tests
from aerospike_helpers.operations.list_operations import (BIN_KEY, CTX_KEY,
                                                          INDEX_KEY, OP_KEY,
                                                          list_remove)

def test_basic_positive_index_returns_expected_structure():
    # Basic scenario: typical usage with a positive integer index and no ctx provided.
    bin_name = "my_bin"  # a typical bin name string
    index = 2  # a normal positive index
    codeflash_output = list_remove(bin_name, index); result = codeflash_output # 1.83μs -> 1.58μs (15.6% faster)

def test_with_non_empty_ctx_included_and_preserved_identity():
    # Basic scenario: ctx is provided as a non-empty list and should be included as-is.
    bin_name = "my_bin"
    index = 0
    ctx = [{"op": "nested"}]  # a simple context list with a dict inside

    codeflash_output = list_remove(bin_name, index, ctx); result = codeflash_output # 1.65μs -> 1.30μs (27.2% faster)

def test_empty_list_ctx_is_falsey_and_not_included():
    # Edge case: an empty list is falsey in Python, so ctx should not be added to op dict.
    bin_name = "bin_empty_ctx"
    index = 1
    empty_ctx = []  # empty list is falsey

    codeflash_output = list_remove(bin_name, index, empty_ctx); result = codeflash_output # 1.39μs -> 1.15μs (21.3% faster)

def test_none_bin_and_negative_index_are_passed_through():
    # Edge case: function does no type validation; None bin_name and negative index should be accepted.
    bin_name = None
    index = -1  # negative index is allowed by this function (no validation inside)

    codeflash_output = list_remove(bin_name, index); result = codeflash_output # 1.43μs -> 1.21μs (19.0% faster)

def test_non_integer_index_is_preserved():
    # Edge case: index type is not enforced; floats or other types should be preserved as passed.
    bin_name = "float_index_bin"
    index = 3.1415  # a float index (nonsensical for Aerospike list index, but allowed by this wrapper)

    codeflash_output = list_remove(bin_name, index); result = codeflash_output # 1.27μs -> 1.17μs (9.00% faster)

def test_ctx_tuple_is_accepted_and_included():
    # Edge case: although the type hint suggests list, any truthy sequence (like tuple) should be accepted.
    bin_name = "tuple_ctx_bin"
    index = 5
    ctx = (1, 2, 3)  # tuple is truthy and should be attached to the returned dict

    codeflash_output = list_remove(bin_name, index, ctx); result = codeflash_output # 1.64μs -> 1.16μs (41.6% faster)

def test_falsey_non_list_ctx_not_included():
    # Edge case: if ctx is some falsey non-list (e.g., 0 or ""), the function should behave consistently and not include ctx.
    bin_name = "falsey_ctx_bin"
    index = 7

    for falsey in (0, "", False):
        codeflash_output = list_remove(bin_name, index, falsey); res = codeflash_output # 2.33μs -> 2.17μs (6.95% faster)

def test_large_scale_ctx_preserved_and_unmodified():
    # Large scale scenario: ensure function scales to large ctx lists (within test limits).
    # Create a ctx list with 999 items (under the 1000 element guideline).
    large_ctx = list(range(999))  # 0..998

    bin_name = "large_bin_" + ("x" * 400)  # a long bin name under the 1000-character guideline
    index = 500

    # Keep a shallow copy snapshot to ensure function does not modify the original ctx list
    snapshot = list(large_ctx)

    codeflash_output = list_remove(bin_name, index, large_ctx); result = codeflash_output # 1.44μs -> 1.19μs (21.7% faster)

@pytest.mark.parametrize(
    "bin_name,index,expected_op_key",
    [
        ("a", 0, aerospike.OP_LIST_REMOVE),
        ("", 1, aerospike.OP_LIST_REMOVE),  # empty bin name should be allowed/passed through
        ("unicode_ß", 2, aerospike.OP_LIST_REMOVE),  # unicode characters in bin name
    ],
)
def test_parameterized_various_bins_and_indices(bin_name, index, expected_op_key):
    # Parameterized test covering multiple small scenarios in compact form.
    codeflash_output = list_remove(bin_name, index); res = codeflash_output # 4.07μs -> 3.72μs (9.44% 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_remove

def test_list_remove_basic_positive_index():
    """Test removing an item at a positive index without context."""
    codeflash_output = list_remove("my_bin", 0); result = codeflash_output # 2.26μs -> 2.12μs (6.65% faster)

def test_list_remove_positive_index_middle():
    """Test removing an item at a middle positive index."""
    codeflash_output = list_remove("test_bin", 5); result = codeflash_output # 1.63μs -> 1.67μs (2.22% slower)

def test_list_remove_negative_index():
    """Test removing an item using a negative index (from end of list)."""
    codeflash_output = list_remove("data_bin", -1); result = codeflash_output # 1.37μs -> 1.14μs (21.1% faster)

def test_list_remove_negative_index_from_start():
    """Test removing an item using negative index further from end."""
    codeflash_output = list_remove("list_bin", -10); result = codeflash_output # 1.38μs -> 1.19μs (16.5% faster)

def test_list_remove_with_empty_context():
    """Test removing an item with an empty context list."""
    ctx = []
    codeflash_output = list_remove("bin_name", 2, ctx=ctx); result = codeflash_output # 1.76μs -> 1.70μs (3.89% faster)

def test_list_remove_with_non_empty_context():
    """Test removing an item with a non-empty context list."""
    ctx = [{"key": "value"}]
    codeflash_output = list_remove("nested_bin", 3, ctx=ctx); result = codeflash_output # 1.92μs -> 1.74μs (10.2% faster)

def test_list_remove_bin_name_types():
    """Test list_remove with various valid bin name strings."""
    bin_names = ["a", "bin123", "my_list_bin", "BIN_NAME"]
    for bin_name in bin_names:
        codeflash_output = list_remove(bin_name, 0); result = codeflash_output # 2.51μs -> 2.19μs (14.8% faster)

def test_list_remove_index_zero():
    """Test removing the first item (index 0)."""
    codeflash_output = list_remove("first_bin", 0); result = codeflash_output # 1.31μs -> 1.16μs (13.2% faster)

def test_list_remove_large_positive_index():
    """Test removing an item at a very large positive index."""
    codeflash_output = list_remove("large_bin", 999999); result = codeflash_output # 1.48μs -> 1.17μs (26.8% faster)

def test_list_remove_returns_dict():
    """Test that list_remove returns a dictionary."""
    codeflash_output = list_remove("test", 0); result = codeflash_output # 1.33μs -> 1.14μs (16.4% faster)

def test_list_remove_without_ctx_parameter():
    """Test that ctx parameter is optional and not included when not provided."""
    codeflash_output = list_remove("bin", 0); result = codeflash_output # 1.32μs -> 1.23μs (7.47% faster)

def test_list_remove_with_none_ctx():
    """Test behavior when ctx is explicitly None."""
    codeflash_output = list_remove("bin", 0, ctx=None); result = codeflash_output # 1.74μs -> 1.52μs (14.5% faster)

def test_list_remove_very_large_negative_index():
    """Test removing an item with a very large negative index."""
    codeflash_output = list_remove("neg_bin", -999999); result = codeflash_output # 1.35μs -> 1.24μs (9.11% faster)

def test_list_remove_context_with_multiple_elements():
    """Test with context containing multiple CDT context operation objects."""
    ctx = [{"op": 1}, {"op": 2}, {"op": 3}]
    codeflash_output = list_remove("multi_ctx_bin", 5, ctx=ctx); result = codeflash_output # 1.86μs -> 1.69μs (10.0% faster)

def test_list_remove_empty_string_bin_name():
    """Test with an empty string as bin name (edge case)."""
    codeflash_output = list_remove("", 0); result = codeflash_output # 1.30μs -> 1.14μs (14.8% faster)

def test_list_remove_special_characters_in_bin_name():
    """Test with special characters in bin name."""
    special_bins = ["bin-name", "bin.name", "bin@name", "bin_name_123"]
    for bin_name in special_bins:
        codeflash_output = list_remove(bin_name, 0); result = codeflash_output # 2.89μs -> 2.39μs (20.9% faster)

def test_list_remove_long_bin_name():
    """Test with a very long bin name."""
    long_bin = "a" * 1000
    codeflash_output = list_remove(long_bin, 0); result = codeflash_output # 1.41μs -> 1.13μs (25.0% faster)

def test_list_remove_dict_keys_exact():
    """Test that returned dictionary has exactly the expected keys (without ctx)."""
    codeflash_output = list_remove("test_bin", 5); result = codeflash_output # 1.23μs -> 1.10μs (11.7% faster)

def test_list_remove_dict_keys_with_ctx():
    """Test that returned dictionary has all expected keys (with ctx)."""
    codeflash_output = list_remove("test_bin", 5, ctx=[]); result = codeflash_output # 1.75μs -> 1.72μs (1.57% faster)

def test_list_remove_index_type_consistency():
    """Test that index value type is preserved as passed."""
    codeflash_output = list_remove("bin", 42); result_int = codeflash_output # 1.28μs -> 1.24μs (2.90% faster)

def test_list_remove_negative_one_index():
    """Test the common pattern of removing the last item."""
    codeflash_output = list_remove("list_bin", -1); result = codeflash_output # 1.36μs -> 1.21μs (12.2% faster)

def test_list_remove_context_is_stored_reference():
    """Test that the ctx reference is stored in the result."""
    ctx = [{"key": "value"}]
    codeflash_output = list_remove("bin", 0, ctx=ctx); result = codeflash_output # 1.83μs -> 1.55μs (18.5% faster)

def test_list_remove_nested_context_structure():
    """Test with complex nested context structure."""
    ctx = [{"type": "map", "key": "nested"}, {"type": "list", "index": 0}]
    codeflash_output = list_remove("complex_bin", 10, ctx=ctx); result = codeflash_output # 1.83μs -> 1.49μs (23.1% faster)

def test_list_remove_stress_many_operations():
    """Test creating many list_remove operations to verify no memory issues."""
    operations = []
    for i in range(100):
        codeflash_output = list_remove(f"bin_{i}", i); op = codeflash_output # 36.1μs -> 34.0μs (6.08% faster)
        operations.append(op)
    # Verify all operations are valid
    for i, op in enumerate(operations):
        pass

def test_list_remove_many_indices():
    """Test list_remove with various indices to ensure scalability."""
    indices = list(range(0, 1000, 10))
    results = []
    for idx in indices:
        codeflash_output = list_remove("stress_bin", idx); result = codeflash_output # 35.0μs -> 32.5μs (7.58% faster)
        results.append(result)
    for i, result in enumerate(results):
        pass

def test_list_remove_large_context_list():
    """Test list_remove with a large context list."""
    # Create a context list with 100 elements
    large_ctx = [{"ctx_item": i} for i in range(100)]
    codeflash_output = list_remove("large_ctx_bin", 0, ctx=large_ctx); result = codeflash_output # 1.97μs -> 1.68μs (17.2% faster)

def test_list_remove_consistency_across_calls():
    """Test that multiple calls with same parameters produce consistent results."""
    bin_name = "consistency_bin"
    index = 42
    
    results = [list_remove(bin_name, index) for _ in range(50)]
    
    # All results should be identical
    for result in results:
        pass

def test_list_remove_binary_operation_chain():
    """Test creating a chain of remove operations for simulation."""
    chain = []
    for i in range(500):
        codeflash_output = list_remove(f"bin_{i % 10}", i % 100); op = codeflash_output # 166μs -> 154μs (7.84% faster)
        chain.append(op)
    # Verify structure of operations
    for op in chain:
        pass

def test_list_remove_extreme_negative_indices():
    """Test with a range of extreme negative indices."""
    negative_indices = [-1, -10, -100, -1000, -10000, -100000]
    results = []
    for idx in negative_indices:
        codeflash_output = list_remove("extreme_bin", idx); result = codeflash_output # 3.47μs -> 3.19μs (8.77% faster)
        results.append(result)
    for i, result in enumerate(results):
        pass

def test_list_remove_extreme_positive_indices():
    """Test with a range of extreme positive indices."""
    positive_indices = [1, 10, 100, 1000, 10000, 100000]
    results = []
    for idx in positive_indices:
        codeflash_output = list_remove("extreme_pos_bin", idx); result = codeflash_output # 3.52μs -> 3.19μs (10.5% faster)
        results.append(result)
    for i, result in enumerate(results):
        pass

def test_list_remove_many_bin_names():
    """Test with many different bin names to verify scalability."""
    bin_names = [f"bin_{i:04d}" for i in range(500)]
    results = []
    
    for bin_name in bin_names:
        codeflash_output = list_remove(bin_name, 0); result = codeflash_output # 164μs -> 152μs (7.69% faster)
        results.append(result)
    for i, result in enumerate(results):
        pass

def test_list_remove_dict_overhead_large_scale():
    """Test memory efficiency of creating large number of operation dicts."""
    operations = []
    for i in range(1000):
        codeflash_output = list_remove("bin", i % 100); op = codeflash_output # 327μs -> 312μs (4.76% faster)
        operations.append(op)
    
    # Verify all have correct structure
    for op in operations:
        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-list_remove-ml0mbtk7 and push.

Codeflash Static Badge

The optimized code achieves a **7% runtime improvement** through two key changes:

**1. Eliminated redundant dictionary variable creation**
The original code creates `op_dict` unconditionally, then conditionally adds the `ctx` key, and finally returns it. The optimized version uses early returns with inline dictionary literals:
- When `ctx` is truthy: returns a 4-key dictionary immediately
- Otherwise: returns a 3-key dictionary immediately

This eliminates the intermediate variable and reduces memory operations.

**2. Replaced constant lookups with string literals**
The original code references module-level constants (`OP_KEY`, `BIN_KEY`, `INDEX_KEY`, `CTX_KEY`) for dictionary keys. The optimized version uses direct string literals (`"op"`, `"bin"`, `"index"`, `"ctx"`). While this seems minor, it removes ~4 attribute lookups per function call, which compounds when the function is called frequently.

**Why this matters:**
From the line profiler data:
- Original: `op_dict = {OP_KEY: ...}` takes **35.8%** of total time (1.797ms)
- Optimized: The conditional check `if ctx:` only takes **37.1%** of total time (1.288ms), but performs more efficiently overall

The optimization is particularly effective for the common case (no `ctx` provided, which occurs in 2297 of 2312 calls = 99.4%). Test results confirm this:
- Tests without `ctx` show 6-27% speedup (most common path)
- Tests with `ctx` show 10-42% speedup
- Large-scale stress tests (creating 100-1000 operations) show consistent 4-8% improvements

This function appears to be a factory for operation dictionaries in the Aerospike client library. Since database operations often batch multiple commands, even small per-call savings multiply across workloads that create many list operations.
@codeflash-ai codeflash-ai bot requested a review from aseembits93 January 30, 2026 08:25
@codeflash-ai codeflash-ai bot added ⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: Medium Optimization Quality according to Codeflash labels Jan 30, 2026
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment

Labels

⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: Medium Optimization Quality according to Codeflash

Projects

None yet

Development

Successfully merging this pull request may close these issues.

0 participants