⚡️ Speed up method AsyncValidatorService.async_partial_validate
by 498%
#47
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
📄 498% (4.98x) speedup for
AsyncValidatorService.async_partial_validate
inguardrails/validator_service/async_validator_service.py
⏱️ Runtime :
22.1 microseconds
→3.69 microseconds
(best of6
runs)📝 Explanation and details
The optimization achieves a 497% speedup through two key improvements in
async_partial_validate
:What was optimized:
Early return optimization: Added an explicit check
if not validators: return []
after the dictionary lookup, avoiding unnecessary list comprehension andasyncio.gather()
calls when no validators exist for the reference path.List comprehension replacement: Replaced the for-loop with
append()
calls with a direct list comprehension, eliminating the overhead of repeated method calls and intermediate list growth.Why it's faster:
The early return is the primary performance driver - when
validator_map.get(reference_path)
returnsNone
or an empty list, the original code still created an empty coroutines list and calledasyncio.gather(*[])
. The optimized version immediately returns[]
, avoiding these unnecessary operations.List comprehensions are implemented in C and avoid the Python bytecode overhead of repeated
append()
calls in loops, making collection building more efficient.Test case performance:
Based on the annotated tests, this optimization is particularly effective for:
test_async_partial_validate_empty_validator_map
)test_async_partial_validate_no_validators_for_path
)These scenarios benefit most from the early return path, while cases with actual validators still see modest improvements from the list comprehension optimization. The 0% throughput improvement indicates that when validators are present and actually executing, the bottleneck remains in the validator execution itself rather than the orchestration code.
✅ Correctness verification report:
🌀 Generated Regression Tests and Runtime
To edit these changes
git checkout codeflash/optimize-AsyncValidatorService.async_partial_validate-mh1j6ge7
and push.