If you are looking to delayĪ task, for example running a daily task at 2AM, look into the Start_date are offset in a way that their execution_date don’t line To note that different tasks’ dependencies Note that Airflow simply looks at the latestĮxecution_date and adds the schedule_interval to determine Some day at 00:00:00, hourly jobs have their start_date at 00:00 The execution_date for the first task instance. Start_date ( datetime.datetime) – The start_date for the task, determines Max_retry_delay ( datetime.timedelta) – maximum delay interval between retries Retries by using exponential backoff algorithm on retry delay (delay Retry_exponential_backoff ( bool) – allow progressive longer waits between Retry_delay ( datetime.timedelta) – delay between retries Retries ( int) – the number of retries that should be performed before Multiple addresses can be specified as aĬomma or semi-colon separated string or by passing a list of strings.Įmail_on_retry ( bool) – Indicates whether email alerts should be sent when aĮmail_on_failure ( bool) – Indicates whether email alerts should be sent when Owner ( str) – the owner of the task, using the unix username is recommendedĮmail ( str or list ) – the ‘to’ email address(es) used in email alerts. Task_id ( str) – a unique, meaningful id for the task Task dependencies shouldīe set by using the set_upstream and/or set_downstream methods. Which ultimately becomes a node in DAG objects. Instantiating aĬlass derived from this one results in the creation of a task object, This class is abstract and shouldn’t be instantiated. Operators (tasks) target specific operations, running specific scripts, Moves data from Hive to MySQL (Hive2MySqlOperator). Waits for a partition to land in Hive (HiveSensorOperator), or one that Operator that runs a Pig job (PigOperator), a sensor operator that Operators derived from this class should perform or trigger certain tasks The constructor as well as the ‘execute’ method. To derive this class, you are expected to override Since operators create objects thatīecome nodes in the dag, BaseOperator contains many recursive methods forĭag crawling behavior. BaseOperator ( task_id, owner = conf.get('operators', 'DEFAULT_OWNER'), email = None, email_on_retry = True, email_on_failure = True, retries = conf.getint('core', 'default_task_retries', fallback=0), retry_delay = timedelta(seconds=300), retry_exponential_backoff = False, max_retry_delay = None, start_date = None, end_date = None, schedule_interval = None, depends_on_past = False, wait_for_downstream = False, dag = None, params = None, default_args = None, priority_weight = 1, weight_rule = WeightRule.DOWNSTREAM, queue = conf.get('celery', 'default_queue'), pool = Pool.DEFAULT_POOL_NAME, pool_slots = 1, sla = None, execution_timeout = None, on_failure_callback = None, on_success_callback = None, on_retry_callback = None, trigger_rule = TriggerRule.ALL_SUCCESS, resources = None, run_as_user = None, task_concurrency = None, executor_config = None, do_xcom_push = True, inlets = None, outlets = None, * args, ** kwargs ) ¶īases: _mixin.LoggingMixinĪbstract base class for all operators. Package Contents ¶ class airflow.operators. The named parameters will take precedence and override the top level json keys. In the case where both the json parameter AND the named parametersĪre provided, they will be merged together. Pipeline_task - may refer to either a pipeline_id or pipeline_name * existing_cluster_id - ID for existing cluster on which to run this task * new_cluster - specs for a new cluster on which this task will be run Pipeline_task - parameters needed to run a Delta Live Tables pipelineĭbt_task - parameters needed to run a dbt projectĬluster specification - it should be one of: Spark_submit_task - parameters needed to run a spark-submit command Spark_python_task - python file path and parameters to run the python file with Notebook_task - notebook path and parameters for the task Spark_jar_task - main class and parameters for the JAR task Task specification - it should be one of: When using named parameters you must to specify following: One named parameter for each top level parameter in the runs/submit endpoint. The second way to accomplish the same thing is to use the named parameters of the DatabricksSubmitRunOperator directly. Json = notebook_run = DatabricksSubmitRunOperator ( task_id = "notebook_run", json = json )
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