The GitHub Actions job "Tests (AMD)" on airflow.git/databricks-repair-from-airflow-3 has succeeded. Run started by GitHub user PrakshiGoyal10 (triggered by eladkal).
Head commit for run: b621d4306cf289698091d687d27ac2ab2f58b5cd / PrakshiGoyal10 <[email protected]> Fix Databricks workflow repair failing on retried tasks and explicit task keys Two defects made the repair button fail against real Databricks runs while unit tests stayed green. get_run_failed_task_keys iterated the per-attempt task list returned by the Databricks API, so a retried or already-repaired task produced its task_key more than once. Databricks rejects rerun_tasks with duplicate keys, so "Repair All Failed Tasks" failed with a 502 for any run with retries, and a task whose latest attempt had succeeded was still repaired. It now keeps only the latest attempt per key before judging state, matching how the operator resolves a task. An explicit databricks_task_key does not survive Dag serialization, so the API server reconstructed md5(dag_id__task_id) and mismatched the real key: single-task repair targeted a non-existent task and repair-all skipped clearing that task's instance. The launch task now records the Airflow task_id to Databricks task_key mapping in its run-metadata XCom, which the endpoint reads server-side; runs launched before this fall back to the md5 derivation. Report URL: https://github.com/apache/airflow/actions/runs/31714826151 With regards, GitHub Actions via GitBox --------------------------------------------------------------------- To unsubscribe, e-mail: [email protected] For additional commands, e-mail: [email protected]
