⚡️ Speed up function list_pop_range by 9%
#113
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📄 9% (0.09x) speedup for
list_pop_rangeinaerospike_helpers/operations/list_operations.py⏱️ Runtime :
365 microseconds→334 microseconds(best of5runs)📝 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:The optimized version builds the complete dictionary in one expression based on the branch:
This eliminates the dictionary mutation overhead (the
op_dict[CTX_KEY] = ctxassignment), 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_RANGEis cached at module load time. This avoids the repeatedaerospike.OP_LIST_POP_RANGEattribute 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:
test_basic_pop_range_single_item: 19% faster)test_includes_ctx_when_nonempty_list_provided: 43.9% faster)The optimization is particularly effective when
ctxis 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:
⚙️ Click to see Existing Unit Tests
test_nested_cdt_ctx.py::TestCTXOperations.test_ctx_list_pop_rangetest_nested_cdt_ctx.py::TestCTXOperations.test_ctx_list_pop_range_negative🌀 Click to see Generated Regression Tests
To edit these changes
git checkout codeflash/optimize-list_pop_range-ml0m31ttand push.