airflow.providers.microsoft.azure.operators.synapse

Module Contents

Classes

AzureSynapseRunSparkBatchOperator

Executes a Spark job on Azure Synapse.
class airflow.providers.microsoft.azure.operators.synapse.AzureSynapseRunSparkBatchOperator(*, azure_synapse_conn_id=AzureSynapseHook.default_conn_name, wait_for_termination=True, spark_pool='', payload, timeout=60 * 60 * 24 * 7, check_interval=60, **kwargs)[source]

Bases: airflow.models.BaseOperator

Executes a Spark job on Azure Synapse.

Parameters

  • azure_synapse_conn_id (str) – The connection identifier for connecting to Azure Synapse.

  • wait_for_termination (bool) – Flag to wait on a job run’s termination.

  • spark_pool (str) – The target synapse spark pool used to submit the job

  • payload (azure.synapse.spark.models.SparkBatchJobOptions) – Livy compatible payload which represents the spark job that a user wants to submit

  • timeout (int) – 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.

  • check_interval (int) – Time in seconds to check on a job run’s status for non-asynchronous waits. Used only if wait_for_termination is True.

template_fields: Sequence[str] = ('azure_synapse_conn_id', 'spark_pool')[source]
template_fields_renderers[source]
ui_color = '#0678d4'[source]
execute(context)[source]

This is the main method to derive when creating an operator. Context is the same dictionary used as when rendering jinja templates.

Refer to get_template_context for more context.

on_kill()[source]

Override this method to clean up subprocesses when a task instance gets killed. Any use of the threading, subprocess or multiprocessing module within an operator needs to be cleaned up, or it will leave ghost processes behind.

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