Source code for airflow.providers.microsoft.azure.operators.synapse
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#
# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
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from __future__ import annotations
from typing import TYPE_CHECKING, Sequence
from azure.synapse.spark.models import SparkBatchJobOptions
from airflow.models import BaseOperator
from airflow.providers.microsoft.azure.hooks.synapse import AzureSynapseHook, AzureSynapseSparkBatchRunStatus
if TYPE_CHECKING:
from airflow.utils.context import Context
[docs]class AzureSynapseRunSparkBatchOperator(BaseOperator):
"""
Executes a Spark job on Azure Synapse.
.. see also::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:AzureSynapseRunSparkBatchOperator`
:param azure_synapse_conn_id: The connection identifier for connecting to Azure Synapse.
:param wait_for_termination: Flag to wait on a job run's termination.
:param spark_pool: The target synapse spark pool used to submit the job
:param payload: Livy compatible payload which represents the spark job that a user wants to submit
:param timeout: Time in seconds to wait for a job to reach a terminal status for non-asynchronous
waits. Used only if ``wait_for_termination`` is True.
:param check_interval: Time in seconds to check on a job run's status for non-asynchronous waits.
Used only if ``wait_for_termination`` is True.
"""
[docs] template_fields: Sequence[str] = (
"azure_synapse_conn_id",
"spark_pool",
)
[docs] template_fields_renderers = {"parameters": "json"}
def __init__(
self,
*,
azure_synapse_conn_id: str = AzureSynapseHook.default_conn_name,
wait_for_termination: bool = True,
spark_pool: str = "",
payload: SparkBatchJobOptions,
timeout: int = 60 * 60 * 24 * 7,
check_interval: int = 60,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.job_id = None
self.azure_synapse_conn_id = azure_synapse_conn_id
self.wait_for_termination = wait_for_termination
self.spark_pool = spark_pool
self.payload = payload
self.timeout = timeout
self.check_interval = check_interval
[docs] def execute(self, context: Context) -> None:
self.hook = AzureSynapseHook(
azure_synapse_conn_id=self.azure_synapse_conn_id, spark_pool=self.spark_pool
)
self.log.info("Executing the Synapse spark job.")
response = self.hook.run_spark_job(payload=self.payload)
self.log.info(response)
self.job_id = vars(response)["id"]
# Push the ``job_id`` value to XCom regardless of what happens during execution. This allows for
# retrieval the executed job's ``id`` for downstream tasks especially if performing an
# asynchronous wait.
context["ti"].xcom_push(key="job_id", value=self.job_id)
if self.wait_for_termination:
self.log.info("Waiting for job run %s to terminate.", self.job_id)
if self.hook.wait_for_job_run_status(
job_id=self.job_id,
expected_statuses=AzureSynapseSparkBatchRunStatus.SUCCESS,
check_interval=self.check_interval,
timeout=self.timeout,
):
self.log.info("Job run %s has completed successfully.", self.job_id)
else:
raise Exception(f"Job run {self.job_id} has failed or has been cancelled.")
[docs] def on_kill(self) -> None:
if self.job_id:
self.hook.cancel_job_run(
job_id=self.job_id,
)
self.log.info("Job run %s has been cancelled successfully.", self.job_id)
