An async-aware sample can attribute execution to coroutine tasks rather than showing only thread frames.
Understanding the Concept
An async-aware sample can attribute execution to coroutine tasks rather than showing only thread frames. Waiting coroutines may need different analysis from CPU-heavy functions.
Requires Python 3.15; Tachyon reports statistical estimates. Exact flags can be checked with python -m profiling.sampling --help.
How to interpret the measurement
Before optimizing, establish an objective measurement and a representative workload. Memory size, function-call counts, elapsed time and samples are different metrics: knowing one does not determine the others. Repeat observations before drawing a conclusion about a performance regression.
Profiling output is evidence for finding a bottleneck, not a performance guarantee. Check that the program still produces the correct result after any optimization and note version, input size and measurement conditions.
Version and Environment Requirements
Python 3.15 or newer is required for the profiling package and Tachyon commands; the local test environment uses Python 3.13.
Worked Example
Read the code from the first line to the last. Identify which values are inputs, which operation is being demonstrated and what should happen before running it.
# Run in a terminal with Python 3.15 or newer:
python -m profiling.sampling run --async-aware sample.py
# Replace sample.py or PID 12345 with your own authorized target.Walkthrough: What Each Part Does
- These are terminal commands, not Python statements. Replace the example script or process ID with one you control and verify command-line options on the installed interpreter.
- Read the profiler output as an observation of execution rather than a promise of a particular duration or percentage.
This demonstration focuses on one practical behavior. Before changing several lines, vary one input or one configuration choice and check whether the result changes for the reason you expected.
The demonstrated API requires a newer interpreter than the Python 3.13 verification environment. Its documented behavior and version requirement must be checked on the intended Python installation.
Important Behavior and Edge Cases
Assuming a slow await always indicates CPU-bound Python code; it may be waiting for I/O.
Requires Python 3.15; Tachyon reports statistical estimates. Exact flags can be checked with python -m profiling.sampling --help.
Practice Exercise
Profile a local, non-sensitive script and explain what is shown in the resulting view.
Write down the result you expect before running your changed code. Check at least one normal case and one boundary or failure case. If your example starts workers or opens resources, verify how they are cleaned up.
Further reading: official documentation and API reference.
Common Beginner Mistakes
- Assuming a slow await always indicates CPU-bound Python code; it may be waiting for I/O.
- Comparing results from different workloads or environments as if the measurements were directly interchangeable.
Key Takeaways
- An async-aware sample can attribute execution to coroutine tasks rather than showing only thread frames.
- Requires Python 3.15; Tachyon reports statistical estimates. Exact flags can be checked with python -m profiling.sampling --help.
- Practice: Profile a local, non-sensitive script and explain what is shown in the resulting view.