Airflow api how to#
Airflow api code#

retry_delay ( timedelta) – delay between retries.

retries ( int) – the number of retries that should be performed before.

owner ( string) – the owner of the task, using the unix username is recommended.

task_id ( string) – 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 the 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 node in the dag, BaseOperator contains many recursive methods forĭag crawling behavior. BaseOperator ( task_id, owner='Airflow', email=None, email_on_retry=True, email_on_failure=True, retries=0, retry_delay=datetime.timedelta(0, 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, adhoc=False, priority_weight=1, queue='default', pool=None, sla=None, execution_timeout=None, on_failure_callback=None, on_success_callback=None, on_retry_callback=None, trigger_rule=u'all_success', resources=None, run_as_user=None, task_concurrency=None, *args, **kwargs ) ¶Ībstract base class for all operators.

To understand the primitive features that can be leveraged in yourĭAGs. It’s worth taking the time to understand the parameters of BaseOperator That’s right, it’s an XComArg will be used to pull data from extract into transform_a and transform_b.All operators are derived from BaseOperator and acquire muchįunctionality through inheritance. Let me show you an example: from _arg import XComArg

Airflow api how to#

How to use XComArgs?Īn XComArg represents an XCom push from a previous operator.

Custom XCom Backends will have a dedicated tutorial. In this tutorial, we will see XComArgs and Decorators.

Custom XCom Backends to use another storage than the Airflow metadatabase to store your XCOMs (data).

Decorators that automatically create tasks for some Operators such as the PythonOperator, BranchPythonOperator, etc.

XComArgs that allow task dependencies to be abstracted and inferred as a result of the Python function invocation (no worries, you are going to understand that in a minute).

The Airflow Taskflow API has three main components: In addition, the Taskflow API makes it easy to share data between tasks as you don’t need to use xcom_pull and xcom_push methods anymore. I’m not saying it becomes a pleasure to write DAGs, but almost. Therefore, you get a sort of natural flow to define tasks and dependencies. The Airflow Taskflow API makes it easier to author DAGs without extra code. Say welcome to the Airflow Taskflow API 👋 What is the Airflow Taskflow API? Therefore, if you want to make implicit dependencies explicit and write DAGs at ease while making them more readable… This example is simple, but imagine how hard it can be to debug your DAG if you have implicit dependencies with thousands of tasks. A task needs data from another task, but you can’t see that on the dependencies. However, if you look at the dependencies, you see that:ĭon’t you see a problem here? Indeed, task c needs data from a and b, but it’s impossible to know that by looking at the dependencies. Task c needs data that tasks a and b return to print on the standard output. Look at the following DAG: from airflow import DAG Here is another use case of why the Taskflow API is incredible!

Airflow api code#

What if I tell you that you can get the same DAG with half less code making it more readable and faster to build? This way of writing DAGs was the only one for a long time, and I’m happy to tell you that this time is over. Those three tasks call three python functions _extract, _transform, and _load. You have a DAG with three tasks that share data using XCOMs. Your DAGs would be way more complex, but you get the idea. What does that mean? Well, I guess your DAGs usually look like that: from airflow import DAGįrom import PythonOperator The Airflow Taskflow API is not available before Apache Airflow 2.0.