amoghrajesh opened a new pull request, #76295:
URL: https://github.com/apache/airflow/pull/76295
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`DatabricksNotebookOperator` and `DatabricksTaskOperator` now reconnect to a
run they already submitted instead of submitting a new one after a worker crash.
Before this change, a worker that died during the poll loop left the
Databricks run executing with no Airflow task watching it, and the retry called
`runs/submit` again. Two runs, two bills, one of them orphaned.
`DatabricksSubmitRunOperator` and `DatabricksRunNowOperator` already guard
against this through `ResumableJobMixin`; these two did not.
`DatabricksTaskBaseOperator` now mixes in `ResumableJobMixin` and implements
the six hooks it needs. The run id goes to the task state store before polling
starts, so on retry the operator asks Databricks about the stored
run and either reconnects to it, returns early if it already succeeded, or
submits fresh if it failed terminally.
### Scope
Only the standalone path. Inside a `DatabricksWorkflowTaskGroup` the run is
submitted by the `.launch` task and the run id reaches each leaf through xcom,
so a retry already reconnects to the shared run. That branch of `execute()`
keeps its existing behaviour, and a test pins it.
Two sentences in `notebook.rst` and `task.rst` claimed that "each retry
submits a new Databricks run" for a standalone operator. Both now describe what
the operator does.
### Compatibility
`durable` defaults to `True`, so existing Dags pick up reconnection with no
change. Pass `durable=False` to keep submitting a new run on every retry.
On Airflow below 3.3 the `common.compat` stand-in takes over and the
operators behave as they did before, since the task state store does not exist
there. The provider already imports the mixin through `common.compat.sdk`, so
nothing new enters the dependency surface.
### Testing
Using this Dag:
```python
from __future__ import annotations
from datetime import datetime, timedelta
from airflow.providers.databricks.operators.databricks import (
DatabricksNotebookOperator,
DatabricksTaskOperator,
)
from airflow.sdk import DAG
CONN_ID = "databricks_default"
NOTEBOOK_PATH = "/Users/[email protected]/sleepy"
with DAG(
dag_id="databricks_durable_manual",
schedule=None,
start_date=datetime(2024, 1, 1),
catchup=False,
default_args={"retries": 2, "retry_delay": timedelta(seconds=30)},
tags=["manual", "durable"],
):
DatabricksNotebookOperator(
task_id="notebook",
databricks_conn_id=CONN_ID,
notebook_path=NOTEBOOK_PATH,
source="WORKSPACE",
polling_period_seconds=10,
)
DatabricksTaskOperator(
task_id="task",
databricks_conn_id=CONN_ID,
task_config={"notebook_task": {"notebook_path": NOTEBOOK_PATH,
"source": "WORKSPACE"}},
polling_period_seconds=10,
)
```
For both the tasks try 1:
<img width="2555" height="1159" alt="image"
src="https://github.com/user-attachments/assets/b8aa044b-513c-4681-80dd-f37ae3513d5a";
/>
<img width="2555" height="1159" alt="image"
src="https://github.com/user-attachments/assets/7a1d8f3b-6521-47f7-a66b-bdac0c607559";
/>
Killed the worker right after
State store for both:
<img width="2555" height="1159" alt="image"
src="https://github.com/user-attachments/assets/2898cee0-904f-480e-99b9-cceb7e6af206";
/>
<img width="2555" height="1159" alt="image"
src="https://github.com/user-attachments/assets/245c7826-dac5-4d6b-a2ff-93828f001ca2";
/>
Try 2 for both
<img width="2555" height="1159" alt="image"
src="https://github.com/user-attachments/assets/736e0d04-26b0-4d05-b6d6-670e7a661b4b";
/>
<img width="2555" height="1159" alt="image"
src="https://github.com/user-attachments/assets/a8640b9c-4593-46c3-8b03-dfe601934fba";
/>
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