GitHub user GayathriSrividya added a comment to the discussion: Airflow cli response times
I ran both suggested commands inside the same `docker.io/apache/airflow:3.2.2` image for comparison. **Timing:** ```bash $ docker run --rm --entrypoint=bash docker.io/apache/airflow:3.2.2 -lc 'time airflow --help' ``` ``` real 0m2.113s user 0m1.751s sys 0m0.096s ``` **Import profiling:** ```bash $ docker run --rm docker.io/apache/airflow:3.2.2 bash -lc 'python -X importtime -m airflow --help 2> importtime.log' ``` Top cumulative import costs (µs): | cumulative | module | |---|---| | 1,705,609 | `airflow` | | 1,011,218 | `airflow.configuration` | | 484,378 | `airflow.sdk.execution_time.task_runner` | | 270,752 | `airflow.sdk.bases.operator` | | 220,732 | `sqlalchemy` | | 192,835 | `sqlalchemy.engine` | | 142,358 | `libcst` | **Conclusion:** The ~2.1s I see here is essentially all Python import and initialization overhead — `airflow.configuration` alone accounts for ~1s of it. So 4–8s in a slower environment is plausible purely from the same import chain running on constrained I/O or CPU. There is no actual database connection happening during `--help`; the traceback you saw earlier was config-parsing failing fast on the empty `AIRFLOW__DATABASE__SQL_ALCHEMY_CONN` you deliberately set. GitHub link: https://github.com/apache/airflow/discussions/68707#discussioncomment-17406967 ---- This is an automatically sent email for [email protected]. To unsubscribe, please send an email to: [email protected]
