ancient-wolf-19325
08/28/2024, 9:22 AMDatabricksRunNowOperator and other databricks operators of airflow, in flyte: https://airflow.apache.org/docs/apache-airflow-providers-databricks/stable/operators/run_now.html?
I tried going through examples/docs - couldn’t understand it.
# Answer
Flyte does not natively support the DatabricksRunNowOperator or other Airflow-specific operators directly. However, you can integrate Airflow tasks into Flyte workflows using the Flyte Airflow plugin. This allows you to leverage the Airflow ecosystem within Flyte.
To use Airflow tasks in Flyte, follow these steps:
1. Install the Airflow Plugin:
pip install flytekitplugins-airflow
2. Define Airflow Tasks in a Flyte Workflow:
You can define your Airflow tasks within a Flyte workflow. Here is an example of how to integrate an Airflow task:
from flytekit import task, workflow
from airflow.providers.databricks.operators.databricks import DatabricksRunNowOperator
@task
def say_hello() -> str:
return "Hello, World!"
@workflow
def airflow_wf():
flyte_task = say_hello()
airflow_task = DatabricksRunNowOperator(
task_id="run_now",
databricks_conn_id="databricks_default",
job_id=1
)
airflow_task >> flyte_task
if __name__ == "__main__":
print(f"Running airflow_wf() {airflow_wf()}")
3. Test Locally:
Before running your workflow on a Flyte cluster, you can test it locally:
AIRFLOW_CONN_DATABRICKS_DEFAULT='databricks://token:YOUR_TOKEN@YOUR_HOST' pyflyte run workflows.py airflow_wf
4. Run on Flyte Cluster:
After testing locally, you can execute the workflow on a Flyte cluster:
pyflyte run --remote workflows.py airflow_wf
For more details, refer to the Airflow agent documentation.
Would you like more detailed steps or examples?
Sources:
- Airflow agent documentation
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