.. Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to you under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at .. http://www.apache.org/licenses/LICENSE-2.0 .. Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. .. _concepts: Concepts ######## The Airflow platform is a tool for describing, executing, and monitoring workflows. Core Ideas '''''''''' DAGs ==== In Airflow, a ``DAG`` -- or a Directed Acyclic Graph -- is a collection of all the tasks you want to run, organized in a way that reflects their relationships and dependencies. A DAG is defined in a Python script, which represents the DAGs structure (tasks and their dependencies) as code. For example, a simple DAG could consist of three tasks: A, B, and C. It could say that A has to run successfully before B can run, but C can run anytime. It could say that task A times out after 5 minutes, and B can be restarted up to 5 times in case it fails. It might also say that the workflow will run every night at 10pm, but shouldn't start until a certain date. In this way, a DAG describes *how* you want to carry out your workflow; but notice that we haven't said anything about *what* we actually want to do! A, B, and C could be anything. Maybe A prepares data for B to analyze while C sends an email. Or perhaps A monitors your location so B can open your garage door while C turns on your house lights. The important thing is that the DAG isn't concerned with what its constituent tasks do; its job is to make sure that whatever they do happens at the right time, or in the right order, or with the right handling of any unexpected issues. DAGs are defined in standard Python files that are placed in Airflow's ``DAG_FOLDER``. Airflow will execute the code in each file to dynamically build the ``DAG`` objects. You can have as many DAGs as you want, each describing an arbitrary number of tasks. In general, each one should correspond to a single logical workflow. .. note:: When searching for DAGs, Airflow only considers python files that contain the strings "airflow" and "DAG" by default. To consider all python files instead, disable the ``DAG_DISCOVERY_SAFE_MODE`` configuration flag. Scope ----- Airflow will load any ``DAG`` object it can import from a DAGfile. Critically, that means the DAG must appear in ``globals()``. Consider the following two DAGs. Only ``dag_1`` will be loaded; the other one only appears in a local scope. .. code:: python dag_1 = DAG('this_dag_will_be_discovered') def my_function(): dag_2 = DAG('but_this_dag_will_not') my_function() Sometimes this can be put to good use. For example, a common pattern with ``SubDagOperator`` is to define the subdag inside a function so that Airflow doesn't try to load it as a standalone DAG. .. _default-args: Default Arguments ----------------- If a dictionary of ``default_args`` is passed to a DAG, it will apply them to any of its operators. This makes it easy to apply a common parameter to many operators without having to type it many times. .. code:: python default_args = { 'start_date': datetime(2016, 1, 1), 'owner': 'airflow' } dag = DAG('my_dag', default_args=default_args) op = DummyOperator(task_id='dummy', dag=dag) print(op.owner) # Airflow Context Manager --------------- *Added in Airflow 1.8* DAGs can be used as context managers to automatically assign new operators to that DAG. .. code:: python with DAG('my_dag', start_date=datetime(2016, 1, 1)) as dag: op = DummyOperator('op') op.dag is dag # True .. _concepts:dagruns: DAG Runs ======== A DAG run is a physical instance of a DAG, containing task instances that run for a specific ``execution_date``. A DAG run is usually created by the Airflow scheduler, but can also be created by an external trigger. Multiple DAG runs may be running at once for a particular DAG, each of them having a different ``execution_date``. For example, we might currently have two DAG runs that are in progress for 2016-01-01 and 2016-01-02 respectively. .. _concepts:execution_date: execution_date -------------- The ``execution_date`` is the *logical* date and time which the DAG Run, and its task instances, are running for. This allows task instances to process data for the desired *logical* date & time. While a task_instance or DAG run might have a *physical* start date of now, their *logical* date might be 3 months ago because we are busy reloading something. In the prior example the ``execution_date`` was 2016-01-01 for the first DAG Run and 2016-01-02 for the second. A DAG run and all task instances created within it are instanced with the same ``execution_date``, so that logically you can think of a DAG run as simulating the DAG running all of its tasks at some previous date & time specified by the ``execution_date``. .. _concepts:tasks: Tasks ===== A Task defines a unit of work within a DAG; it is represented as a node in the DAG graph, and it is written in Python. Each task is an implementation of an Operator, for example a ``PythonOperator`` to execute some Python code, or a ``BashOperator`` to run a Bash command. The task implements an operator by defining specific values for that operator, such as a Python callable in the case of ``PythonOperator`` or a Bash command in the case of ``BashOperator``. Relations between Tasks ----------------------- Consider the following DAG with two tasks. Each task is a node in our DAG, and there is a dependency from task_1 to task_2: .. code:: python with DAG('my_dag', start_date=datetime(2016, 1, 1)) as dag: task_1 = DummyOperator('task_1') task_2 = DummyOperator('task_2') task_1 >> task_2 # Define dependencies We can say that task_1 is *upstream* of task_2, and conversely task_2 is *downstream* of task_1. When a DAG Run is created, task_1 will start running and task_2 waits for task_1 to complete successfully before it may start. Task Instances ============== A task instance represents a specific run of a task and is characterized as the combination of a DAG, a task, and a point in time (``execution_date``). Task instances also have an indicative state, which could be "running", "success", "failed", "skipped", "up for retry", etc. Tasks are defined in DAGs, and both are written in Python code to define what you want to do. Task Instances belong to DAG Runs, have an associated ``execution_date``, and are physicalised, runnable entities. Relations between Task Instances -------------------------------- Again consider the following tasks, defined for some DAG: .. code:: python with DAG('my_dag', start_date=datetime(2016, 1, 1)) as dag: task_1 = DummyOperator('task_1') task_2 = DummyOperator('task_2') task_1 >> task_2 # Define dependencies When we enable this DAG, the scheduler creates several DAG Runs - one with ``execution_date`` of 2016-01-01, one with ``execution_date`` of 2016-01-02, and so on up to the current date. Each DAG Run will contain a task_1 Task Instance and a task_2 Task instance. Both Task Instances will have ``execution_date`` equal to the DAG Run's ``execution_date``, and each task_2 will be *downstream* of (depends on) its task_1. We can also say that task_1 for 2016-01-01 is the *previous* task instance of the task_1 for 2016-01-02. Or that the DAG Run for 2016-01-01 is the *previous* DAG Run to the DAG Run of 2016-01-02. Here, *previous* refers to the logical past/prior ``execution_date``, that runs independently of other runs, and *upstream* refers to a dependency within the same run and having the same ``execution_date``. .. note:: The Airflow documentation sometimes refers to *previous* instead of *upstream* in places, and vice-versa. If you find any occurrences of this, please help us improve by contributing some corrections! Task Lifecycle ============== A task goes through various stages from start to completion. In the Airflow UI (graph and tree views), these stages are displayed by a color representing each stage: .. image:: img/task_stages.png The complete lifecycle of the task looks like this: .. image:: img/task_lifecycle_diagram.png The happy flow consists of the following stages: 1. No status (scheduler created empty task instance) 2. Scheduled (scheduler determined task instance needs to run) 3. Queued (scheduler sent task to executor to run on the queue) 4. Running (worker picked up a task and is now running it) 5. Success (task completed)8 There is also visual difference between scheduled and manually triggered DAGs/tasks: .. image:: img/task_manual_vs_scheduled.png The DAGs/tasks with a black border are scheduled runs, whereas the non-bordered DAGs/tasks are manually triggered, i.e. by ``airflow dags trigger``. .. _concepts:operators: Operators ========= While DAGs describe *how* to run a workflow, ``Operators`` determine what actually gets done by a task. An operator describes a single task in a workflow. Operators are usually (but not always) atomic, meaning they can stand on their own and don't need to share resources with any other operators. The DAG will make sure that operators run in the correct order; other than those dependencies, operators generally run independently. In fact, they may run on two completely different machines. This is a subtle but very important point: in general, if two operators need to share information, like a filename or small amount of data, you should consider combining them into a single operator. If it absolutely can't be avoided, Airflow does have a feature for operator cross-communication called XCom that is described in the section :ref:`XComs ` Airflow provides operators for many common tasks, including: - :class:`~airflow.operators.bash.BashOperator` - executes a bash command - :class:`~airflow.operators.python.PythonOperator` - calls an arbitrary Python function - :class:`~airflow.providers.email.operators.email.EmailOperator` - sends an email - :class:`~airflow.providers.http.operators.http.SimpleHttpOperator` - sends an HTTP request - :class:`~airflow.providers.mysql.operators.mysql.MySqlOperator`, :class:`~airflow.providers.sqlite.operators.sqlite.SqliteOperator`, :class:`~airflow.providers.postgres.operators.postgres.PostgresOperator`, :class:`~airflow.providers.microsoft.mssql.operators.mssql.MsSqlOperator`, :class:`~airflow.providers.oracle.operators.oracle.OracleOperator`, :class:`~airflow.providers.jdbc.operators.jdbc.JdbcOperator`, etc. - executes a SQL command - ``Sensor`` - an Operator that waits (polls) for a certain time, file, database row, S3 key, etc... In addition to these basic building blocks, there are many more specific operators: :class:`~airflow.providers.docker.operators.docker.DockerOperator`, :class:`~airflow.providers.apache.hive.operators.hive.HiveOperator`, :class:`~airflow.providers.amazon.aws.operators.s3_file_transform.S3FileTransformOperator`, :class:`~airflow.providers.mysql.operators.presto_to_mysql.PrestoToMySqlTransfer`, :class:`~airflow.providers.slack.operators.slack.SlackAPIOperator`... you get the idea! Operators are only loaded by Airflow if they are assigned to a DAG. .. seealso:: - :ref:`List Airflow operators ` - :doc:`How-to guides for some Airflow operators`. DAG Assignment -------------- *Added in Airflow 1.8* Operators do not have to be assigned to DAGs immediately (previously ``dag`` was a required argument). However, once an operator is assigned to a DAG, it can not be transferred or unassigned. DAG assignment can be done explicitly when the operator is created, through deferred assignment, or even inferred from other operators. .. code:: python dag = DAG('my_dag', start_date=datetime(2016, 1, 1)) # sets the DAG explicitly explicit_op = DummyOperator(task_id='op1', dag=dag) # deferred DAG assignment deferred_op = DummyOperator(task_id='op2') deferred_op.dag = dag # inferred DAG assignment (linked operators must be in the same DAG) inferred_op = DummyOperator(task_id='op3') inferred_op.set_upstream(deferred_op) Bitshift Composition -------------------- *Added in Airflow 1.8* We recommend you setting operator relationships with bitshift operators rather than ``set_upstream()`` and ``set_downstream()``. Traditionally, operator relationships are set with the ``set_upstream()`` and ``set_downstream()`` methods. In Airflow 1.8, this can be done with the Python bitshift operators ``>>`` and ``<<``. The following four statements are all functionally equivalent: .. code:: python op1 >> op2 op1.set_downstream(op2) op2 << op1 op2.set_upstream(op1) When using the bitshift to compose operators, the relationship is set in the direction that the bitshift operator points. For example, ``op1 >> op2`` means that ``op1`` runs first and ``op2`` runs second. Multiple operators can be composed -- keep in mind the chain is executed left-to-right and the rightmost object is always returned. For example: .. code:: python op1 >> op2 >> op3 << op4 is equivalent to: .. code:: python op1.set_downstream(op2) op2.set_downstream(op3) op3.set_upstream(op4) We can put this all together to build a simple pipeline: .. code:: python with DAG('my_dag', start_date=datetime(2016, 1, 1)) as dag: ( DummyOperator(task_id='dummy_1') >> BashOperator( task_id='bash_1', bash_command='echo "HELLO!"') >> PythonOperator( task_id='python_1', python_callable=lambda: print("GOODBYE!")) ) Bitshift can also be used with lists. For example: .. code:: python op1 >> [op2, op3] >> op4 is equivalent to: .. code:: python op1 >> op2 >> op4 op1 >> op3 >> op4 and equivalent to: .. code:: python op1.set_downstream([op2, op3]) op4.set_upstream([op2, op3]) Relationship Builders --------------------- *Moved in Airflow 2.0* In Airflow 2.0 those two methods moved from ``airflow.utils.helpers`` to ``airflow.models.baseoperator``. ``chain`` and ``cross_downstream`` function provide easier ways to set relationships between operators in specific situation. When setting a relationship between two lists, if we want all operators in one list to be upstream to all operators in the other, we cannot use a single bitshift composition. Instead we have to split one of the lists: .. code:: python [op1, op2, op3] >> op4 [op1, op2, op3] >> op5 [op1, op2, op3] >> op6 ``cross_downstream`` could handle list relationships easier. .. code:: python cross_downstream([op1, op2, op3], [op4, op5, op6]) When setting single direction relationships to many operators, we could concat them with bitshift composition. .. code:: python op1 >> op2 >> op3 >> op4 >> op5 This can be accomplished using ``chain`` .. code:: python chain(op1, op2, op3, op4, op5) even without operator's name .. code:: python chain([DummyOperator(task_id='op' + i, dag=dag) for i in range(1, 6)]) ``chain`` can handle a list of operators .. code:: python chain(op1, [op2, op3], op4) is equivalent to: .. code:: python op1 >> [op2, op3] >> op4 When ``chain`` sets relationships between two lists of operators, they must have the same size. .. code:: python chain(op1, [op2, op3], [op4, op5], op6) is equivalent to: .. code:: python op1 >> [op2, op3] op2 >> op4 op3 >> op5 [op4, op5] >> op6 Workflows ========= You're now familiar with the core building blocks of Airflow. Some of the concepts may sound very similar, but the vocabulary can be conceptualized like this: - DAG: The work (tasks), and the order in which work should take place (dependencies), written in Python. - DAG Run: An instance of a DAG for a particular logical date and time. - Operator: A class that acts as a template for carrying out some work. - Task: Defines work by implementing an operator, written in Python. - Task Instance: An instance of a task - that has been assigned to a DAG and has a state associated with a specific DAG run (i.e for a specific execution_date). - execution_date: The logical date and time for a DAG Run and its Task Instances. By combining ``DAGs`` and ``Operators`` to create ``TaskInstances``, you can build complex workflows. Additional Functionality '''''''''''''''''''''''' In addition to the core Airflow objects, there are a number of more complex features that enable behaviors like limiting simultaneous access to resources, cross-communication, conditional execution, and more. Hooks ===== Hooks are interfaces to external platforms and databases like Hive, S3, MySQL, Postgres, HDFS, and Pig. Hooks implement a common interface when possible, and act as a building block for operators. They also use the ``airflow.models.connection.Connection`` model to retrieve hostnames and authentication information. Hooks keep authentication code and information out of pipelines, centralized in the metadata database. Hooks are also very useful on their own to use in Python scripts, Airflow airflow.operators.PythonOperator, and in interactive environments like iPython or Jupyter Notebook. .. seealso:: :ref:`List Airflow hooks ` Pools ===== Some systems can get overwhelmed when too many processes hit them at the same time. Airflow pools can be used to **limit the execution parallelism** on arbitrary sets of tasks. The list of pools is managed in the UI (``Menu -> Admin -> Pools``) by giving the pools a name and assigning it a number of worker slots. Tasks can then be associated with one of the existing pools by using the ``pool`` parameter when creating tasks (i.e., instantiating operators). .. code:: python aggregate_db_message_job = BashOperator( task_id='aggregate_db_message_job', execution_timeout=timedelta(hours=3), pool='ep_data_pipeline_db_msg_agg', bash_command=aggregate_db_message_job_cmd, dag=dag) aggregate_db_message_job.set_upstream(wait_for_empty_queue) The ``pool`` parameter can be used in conjunction with ``priority_weight`` to define priorities in the queue, and which tasks get executed first as slots open up in the pool. The default ``priority_weight`` is ``1``, and can be bumped to any number. When sorting the queue to evaluate which task should be executed next, we use the ``priority_weight``, summed up with all of the ``priority_weight`` values from tasks downstream from this task. You can use this to bump a specific important task and the whole path to that task gets prioritized accordingly. Tasks will be scheduled as usual while the slots fill up. Once capacity is reached, runnable tasks get queued and their state will show as such in the UI. As slots free up, queued tasks start running based on the ``priority_weight`` (of the task and its descendants). Note that if tasks are not given a pool, they are assigned to a default pool ``default_pool``. ``default_pool`` is initialized with 128 slots and can changed through the UI or CLI (though it cannot be removed). To combine Pools with SubDAGs see the `SubDAGs`_ section. .. _concepts-connections: Connections =========== The information needed to connect to external systems is stored in the Airflow metastore database and can be managed in the UI (``Menu -> Admin -> Connections``). A ``conn_id`` is defined there, and hostname / login / password / schema information attached to it. Airflow pipelines retrieve centrally-managed connections information by specifying the relevant ``conn_id``. You may add more than one connection with the same ``conn_id``. When there is more than one connection with the same ``conn_id``, the :py:meth:`~airflow.hooks.base_hook.BaseHook.get_connection` method on :py:class:`~airflow.hooks.base_hook.BaseHook` will choose one connection randomly. This can be be used to provide basic load balancing and fault tolerance, when used in conjunction with retries. Airflow also provides a mechanism to store connections outside the database, e.g. in :ref:`environment variables `. Additonal sources may be enabled, e.g. :ref:`AWS SSM Parameter Store `, or you may :ref:`roll your own secrets backend `. Many hooks have a default ``conn_id``, where operators using that hook do not need to supply an explicit connection ID. For example, the default ``conn_id`` for the :class:`~airflow.providers.postgres.hooks.postgres.PostgresHook` is ``postgres_default``. See :doc:`howto/connection/index` for details on creating and managing connections. Queues ====== When using the CeleryExecutor, the Celery queues that tasks are sent to can be specified. ``queue`` is an attribute of BaseOperator, so any task can be assigned to any queue. The default queue for the environment is defined in the ``airflow.cfg``'s ``celery -> default_queue``. This defines the queue that tasks get assigned to when not specified, as well as which queue Airflow workers listen to when started. Workers can listen to one or multiple queues of tasks. When a worker is started (using the command ``airflow celery worker``), a set of comma-delimited queue names can be specified (e.g. ``airflow celery worker -q spark``). This worker will then only pick up tasks wired to the specified queue(s). This can be useful if you need specialized workers, either from a resource perspective (for say very lightweight tasks where one worker could take thousands of tasks without a problem), or from an environment perspective (you want a worker running from within the Spark cluster itself because it needs a very specific environment and security rights). .. _concepts:xcom: XComs ===== XComs let tasks exchange messages, allowing more nuanced forms of control and shared state. The name is an abbreviation of "cross-communication". XComs are principally defined by a key, value, and timestamp, but also track attributes like the task/DAG that created the XCom and when it should become visible. Any object that can be pickled can be used as an XCom value, so users should make sure to use objects of appropriate size. XComs can be "pushed" (sent) or "pulled" (received). When a task pushes an XCom, it makes it generally available to other tasks. Tasks can push XComs at any time by calling the ``xcom_push()`` method. In addition, if a task returns a value (either from its Operator's ``execute()`` method, or from a PythonOperator's ``python_callable`` function), then an XCom containing that value is automatically pushed. Tasks call ``xcom_pull()`` to retrieve XComs, optionally applying filters based on criteria like ``key``, source ``task_ids``, and source ``dag_id``. By default, ``xcom_pull()`` filters for the keys that are automatically given to XComs when they are pushed by being returned from execute functions (as opposed to XComs that are pushed manually). If ``xcom_pull`` is passed a single string for ``task_ids``, then the most recent XCom value from that task is returned; if a list of ``task_ids`` is passed, then a corresponding list of XCom values is returned. .. code:: python # inside a PythonOperator called 'pushing_task' def push_function(): return value # inside another PythonOperator def pull_function(task_instance): value = task_instance.xcom_pull(task_ids='pushing_task') When specifying arguments that are part of the context, they will be automatically passed to the function. It is also possible to pull XCom directly in a template, here's an example of what this may look like: .. code:: jinja SELECT * FROM {{ task_instance.xcom_pull(task_ids='foo', key='table_name') }} Note that XComs are similar to `Variables`_, but are specifically designed for inter-task communication rather than global settings. .. _concepts:variables: Variables ========= Variables are a generic way to store and retrieve arbitrary content or settings as a simple key value store within Airflow. Variables can be listed, created, updated and deleted from the UI (``Admin -> Variables``), code or CLI. In addition, json settings files can be bulk uploaded through the UI. While your pipeline code definition and most of your constants and variables should be defined in code and stored in source control, it can be useful to have some variables or configuration items accessible and modifiable through the UI. .. code:: python from airflow.models import Variable foo = Variable.get("foo") bar = Variable.get("bar", deserialize_json=True) baz = Variable.get("baz", default_var=None) The second call assumes ``json`` content and will be deserialized into ``bar``. Note that ``Variable`` is a sqlalchemy model and can be used as such. The third call uses the ``default_var`` parameter with the value ``None``, which either returns an existing value or ``None`` if the variable isn't defined. The get function will throw a ``KeyError`` if the variable doesn't exist and no default is provided. You can use a variable from a jinja template with the syntax : .. code:: bash echo {{ var.value. }} or if you need to deserialize a json object from the variable : .. code:: bash echo {{ var.json. }} Branching ========= Sometimes you need a workflow to branch, or only go down a certain path based on an arbitrary condition which is typically related to something that happened in an upstream task. One way to do this is by using the ``BranchPythonOperator``. The ``BranchPythonOperator`` is much like the PythonOperator except that it expects a ``python_callable`` that returns a task_id (or list of task_ids). The task_id returned is followed, and all of the other paths are skipped. The task_id returned by the Python function has to reference a task directly downstream from the BranchPythonOperator task. Note that when a path is a downstream task of the returned task (list), it will not be skipped: .. image:: img/branch_note.png Paths of the branching task are ``branch_a``, ``join`` and ``branch_b``. Since ``join`` is a downstream task of ``branch_a``, it will be excluded from the skipped tasks when ``branch_a`` is returned by the Python callable. The ``BranchPythonOperator`` can also be used with XComs allowing branching context to dynamically decide what branch to follow based on upstream tasks. For example: .. code:: python def branch_func(ti): xcom_value = int(ti.xcom_pull(task_ids='start_task')) if xcom_value >= 5: return 'continue_task' else: return 'stop_task' start_op = BashOperator( task_id='start_task', bash_command="echo 5", xcom_push=True, dag=dag) branch_op = BranchPythonOperator( task_id='branch_task', python_callable=branch_func, dag=dag) continue_op = DummyOperator(task_id='continue_task', dag=dag) stop_op = DummyOperator(task_id='stop_task', dag=dag) start_op >> branch_op >> [continue_op, stop_op] If you wish to implement your own operators with branching functionality, you can inherit from :class:`~airflow.operators.branch_operator.BaseBranchOperator`, which behaves similarly to ``BranchPythonOperator`` but expects you to provide an implementation of the method ``choose_branch``. As with the callable for ``BranchPythonOperator``, this method should return the ID of a downstream task, or a list of task IDs, which will be run, and all others will be skipped. .. code:: python class MyBranchOperator(BaseBranchOperator): def choose_branch(self, context): """ Run an extra branch on the first day of the month """ if context['execution_date'].day == 1: return ['daily_task_id', 'monthly_task_id'] else: return 'daily_task_id' SubDAGs ======= SubDAGs are perfect for repeating patterns. Defining a function that returns a DAG object is a nice design pattern when using Airflow. Airbnb uses the *stage-check-exchange* pattern when loading data. Data is staged in a temporary table, after which data quality checks are performed against that table. Once the checks all pass the partition is moved into the production table. As another example, consider the following DAG: .. image:: img/subdag_before.png We can combine all of the parallel ``task-*`` operators into a single SubDAG, so that the resulting DAG resembles the following: .. image:: img/subdag_after.png Note that SubDAG operators should contain a factory method that returns a DAG object. This will prevent the SubDAG from being treated like a separate DAG in the main UI. For example: .. exampleinclude:: ../airflow/example_dags/subdags/subdag.py :language: python :start-after: [START subdag] :end-before: [END subdag] This SubDAG can then be referenced in your main DAG file: .. exampleinclude:: ../airflow/example_dags/example_subdag_operator.py :language: python :start-after: [START example_subdag_operator] :end-before: [END example_subdag_operator] You can zoom into a SubDagOperator from the graph view of the main DAG to show the tasks contained within the SubDAG: .. image:: img/subdag_zoom.png Some other tips when using SubDAGs: - by convention, a SubDAG's ``dag_id`` should be prefixed by its parent and a dot. As in ``parent.child`` - share arguments between the main DAG and the SubDAG by passing arguments to the SubDAG operator (as demonstrated above) - SubDAGs must have a schedule and be enabled. If the SubDAG's schedule is set to ``None`` or ``@once``, the SubDAG will succeed without having done anything - clearing a SubDagOperator also clears the state of the tasks within - marking success on a SubDagOperator does not affect the state of the tasks within - refrain from using ``depends_on_past=True`` in tasks within the SubDAG as this can be confusing - it is possible to specify an executor for the SubDAG. It is common to use the SequentialExecutor if you want to run the SubDAG in-process and effectively limit its parallelism to one. Using LocalExecutor can be problematic as it may over-subscribe your worker, running multiple tasks in a single slot See ``airflow/example_dags`` for a demonstration. Note that airflow pool is not honored by SubDagOperator. Hence resources could be consumed by SubdagOperators. SLAs ==== Service Level Agreements, or time by which a task or DAG should have succeeded, can be set at a task level as a ``timedelta``. If one or many instances have not succeeded by that time, an alert email is sent detailing the list of tasks that missed their SLA. The event is also recorded in the database and made available in the web UI under ``Browse->SLA Misses`` where events can be analyzed and documented. SLAs can be configured for scheduled tasks by using the ``sla`` parameter. In addition to sending alerts to the addresses specified in a task's ``email`` parameter, the ``sla_miss_callback`` specifies an additional ``Callable`` object to be invoked when the SLA is not met. If you don't want to check SLAs, you can disable globally (all the DAGs) by setting ``check_slas=False`` under ``[core]`` section in ``airflow.cfg`` file: .. code-block:: ini [core] check_slas = False .. note:: For information on the email configuration, see :doc:`howto/email-config` .. _concepts/trigger_rule: Trigger Rules ============= Though the normal workflow behavior is to trigger tasks when all their directly upstream tasks have succeeded, Airflow allows for more complex dependency settings. All operators have a ``trigger_rule`` argument which defines the rule by which the generated task get triggered. The default value for ``trigger_rule`` is ``all_success`` and can be defined as "trigger this task when all directly upstream tasks have succeeded". All other rules described here are based on direct parent tasks and are values that can be passed to any operator while creating tasks: * ``all_success``: (default) all parents have succeeded * ``all_failed``: all parents are in a ``failed`` or ``upstream_failed`` state * ``all_done``: all parents are done with their execution * ``one_failed``: fires as soon as at least one parent has failed, it does not wait for all parents to be done * ``one_success``: fires as soon as at least one parent succeeds, it does not wait for all parents to be done * ``none_failed``: all parents have not failed (``failed`` or ``upstream_failed``) i.e. all parents have succeeded or been skipped * ``none_skipped``: no parent is in a ``skipped`` state, i.e. all parents are in a ``success``, ``failed``, or ``upstream_failed`` state * ``dummy``: dependencies are just for show, trigger at will Note that these can be used in conjunction with ``depends_on_past`` (boolean) that, when set to ``True``, keeps a task from getting triggered if the previous schedule for the task hasn't succeeded. One must be aware of the interaction between trigger rules and skipped tasks in schedule level. Skipped tasks will cascade through trigger rules ``all_success`` and ``all_failed`` but not ``all_done``, ``one_failed``, ``one_success``, ``none_failed``, ``none_skipped`` and ``dummy``. For example, consider the following DAG: .. code:: python #dags/branch_without_trigger.py import datetime as dt from airflow.models import DAG from airflow.operators.dummy_operator import DummyOperator from airflow.operators.python import BranchPythonOperator dag = DAG( dag_id='branch_without_trigger', schedule_interval='@once', start_date=dt.datetime(2019, 2, 28) ) run_this_first = DummyOperator(task_id='run_this_first', dag=dag) branching = BranchPythonOperator( task_id='branching', dag=dag, python_callable=lambda: 'branch_a' ) branch_a = DummyOperator(task_id='branch_a', dag=dag) follow_branch_a = DummyOperator(task_id='follow_branch_a', dag=dag) branch_false = DummyOperator(task_id='branch_false', dag=dag) join = DummyOperator(task_id='join', dag=dag) run_this_first >> branching branching >> branch_a >> follow_branch_a >> join branching >> branch_false >> join In the case of this DAG, ``join`` is downstream of ``follow_branch_a`` and ``branch_false``. The ``join`` task will show up as skipped because its ``trigger_rule`` is set to ``all_success`` by default and skipped tasks will cascade through ``all_success``. .. image:: img/branch_without_trigger.png By setting ``trigger_rule`` to ``none_failed`` in ``join`` task, .. code:: python #dags/branch_with_trigger.py ... join = DummyOperator(task_id='join', dag=dag, trigger_rule='none_failed') ... The ``join`` task will be triggered as soon as ``branch_false`` has been skipped (a valid completion state) and ``follow_branch_a`` has succeeded. Because skipped tasks **will not** cascade through ``none_failed``. .. image:: img/branch_with_trigger.png Latest Run Only =============== Standard workflow behavior involves running a series of tasks for a particular date/time range. Some workflows, however, perform tasks that are independent of run time but need to be run on a schedule, much like a standard cron job. In these cases, backfills or running jobs missed during a pause just wastes CPU cycles. For situations like this, you can use the ``LatestOnlyOperator`` to skip tasks that are not being run during the most recent scheduled run for a DAG. The ``LatestOnlyOperator`` skips all direct downstream tasks, if the time right now is not between its ``execution_time`` and the next scheduled ``execution_time`` or the DagRun has been externally triggered. For example, consider the following DAG: .. exampleinclude:: ../airflow/example_dags/example_latest_only_with_trigger.py :language: python :start-after: [START example] :end-before: [END example] In the case of this DAG, the task ``task1`` is directly downstream of ``latest_only`` and will be skipped for all runs except the latest. ``task2`` is entirely independent of ``latest_only`` and will run in all scheduled periods. ``task3`` is downstream of ``task1`` and ``task2`` and because of the default ``trigger_rule`` being ``all_success`` will receive a cascaded skip from ``task1``. ``task4`` is downstream of ``task1`` and ``task2``, but it will not be skipped, since its ``trigger_rule`` is set to ``all_done``. .. image:: img/latest_only_with_trigger.png Zombies & Undeads ================= Task instances die all the time, usually as part of their normal life cycle, but sometimes unexpectedly. Zombie tasks are characterized by the absence of an heartbeat (emitted by the job periodically) and a ``running`` status in the database. They can occur when a worker node can't reach the database, when Airflow processes are killed externally, or when a node gets rebooted for instance. Zombie killing is performed periodically by the scheduler's process. Undead processes are characterized by the existence of a process and a matching heartbeat, but Airflow isn't aware of this task as ``running`` in the database. This mismatch typically occurs as the state of the database is altered, most likely by deleting rows in the "Task Instances" view in the UI. Tasks are instructed to verify their state as part of the heartbeat routine, and terminate themselves upon figuring out that they are in this "undead" state. Cluster Policy ============== Your local Airflow settings file can define a ``policy`` function that has the ability to mutate task attributes based on other task or DAG attributes. It receives a single argument as a reference to task objects, and is expected to alter its attributes. For example, this function could apply a specific queue property when using a specific operator, or enforce a task timeout policy, making sure that no tasks run for more than 48 hours. Here's an example of what this may look like inside your ``airflow_local_settings.py``: .. code:: python def policy(task): if task.__class__.__name__ == 'HivePartitionSensor': task.queue = "sensor_queue" if task.timeout > timedelta(hours=48): task.timeout = timedelta(hours=48) Documentation & Notes ===================== It's possible to add documentation or notes to your DAGs & task objects that become visible in the web interface ("Graph View" for DAGs, "Task Details" for tasks). There are a set of special task attributes that get rendered as rich content if defined: ========== ================ attribute rendered to ========== ================ doc monospace doc_json json doc_yaml yaml doc_md markdown doc_rst reStructuredText ========== ================ Please note that for DAGs, doc_md is the only attribute interpreted. This is especially useful if your tasks are built dynamically from configuration files, it allows you to expose the configuration that led to the related tasks in Airflow. .. code:: python """ ### My great DAG """ dag = DAG('my_dag', default_args=default_args) dag.doc_md = __doc__ t = BashOperator("foo", dag=dag) t.doc_md = """\ #Title" Here's a [url](www.airbnb.com) """ This content will get rendered as markdown respectively in the "Graph View" and "Task Details" pages. .. _jinja-templating: Jinja Templating ================ Airflow leverages the power of `Jinja Templating `_ and this can be a powerful tool to use in combination with macros (see the :doc:`macros-ref` section). For example, say you want to pass the execution date as an environment variable to a Bash script using the ``BashOperator``. .. code:: python # The execution date as YYYY-MM-DD date = "{{ ds }}" t = BashOperator( task_id='test_env', bash_command='/tmp/test.sh ', dag=dag, env={'EXECUTION_DATE': date}) Here, ``{{ ds }}`` is a macro, and because the ``env`` parameter of the ``BashOperator`` is templated with Jinja, the execution date will be available as an environment variable named ``EXECUTION_DATE`` in your Bash script. You can use Jinja templating with every parameter that is marked as "templated" in the documentation. Template substitution occurs just before the pre_execute function of your operator is called. You can also use Jinja templating with nested fields, as long as these nested fields are marked as templated in the structure they belong to: fields registered in ``template_fields`` property will be submitted to template substitution, like the ``path`` field in the example below: .. code:: python class MyDataReader: template_fields = ['path'] def __init__(self, my_path): self.path = my_path # [additional code here...] t = PythonOperator( task_id='transform_data', python_callable=transform_data op_args=[ MyDataReader('/tmp/{{ ds }}/my_file') ], dag=dag) .. note:: ``template_fields`` property can equally be a class variable or an instance variable. Deep nested fields can also be substituted, as long as all intermediate fields are marked as template fields: .. code:: python class MyDataTransformer: template_fields = ['reader'] def __init__(self, my_reader): self.reader = my_reader # [additional code here...] class MyDataReader: template_fields = ['path'] def __init__(self, my_path): self.path = my_path # [additional code here...] t = PythonOperator( task_id='transform_data', python_callable=transform_data op_args=[ MyDataTransformer(MyDataReader('/tmp/{{ ds }}/my_file')) ], dag=dag) You can pass custom options to the Jinja ``Environment`` when creating your DAG. One common usage is to avoid Jinja from dropping a trailing newline from a template string: .. code:: python my_dag = DAG(dag_id='my-dag', jinja_environment_kwargs={ 'keep_trailing_newline': True, # some other jinja2 Environment options here }) See `Jinja documentation `_ to find all available options. Packaged DAGs ''''''''''''' While often you will specify DAGs in a single ``.py`` file it might sometimes be required to combine a DAG and its dependencies. For example, you might want to combine several DAGs together to version them together or you might want to manage them together or you might need an extra module that is not available by default on the system you are running Airflow on. To allow this you can create a zip file that contains the DAG(s) in the root of the zip file and have the extra modules unpacked in directories. For instance you can create a zip file that looks like this: .. code-block:: bash my_dag1.py my_dag2.py package1/__init__.py package1/functions.py Airflow will scan the zip file and try to load ``my_dag1.py`` and ``my_dag2.py``. It will not go into subdirectories as these are considered to be potential packages. In case you would like to add module dependencies to your DAG you basically would do the same, but then it is more suitable to use a virtualenv and pip. .. code-block:: bash virtualenv zip_dag source zip_dag/bin/activate mkdir zip_dag_contents cd zip_dag_contents pip install --install-option="--install-lib=$PWD" my_useful_package cp ~/my_dag.py . zip -r zip_dag.zip * .. note:: the zip file will be inserted at the beginning of module search list (sys.path) and as such it will be available to any other code that resides within the same interpreter. .. note:: packaged dags cannot be used with pickling turned on. .. note:: packaged dags cannot contain dynamic libraries (eg. libz.so) these need to be available on the system if a module needs those. In other words only pure python modules can be packaged. .airflowignore '''''''''''''' A ``.airflowignore`` file specifies the directories or files in ``DAG_FOLDER`` that Airflow should intentionally ignore. Each line in ``.airflowignore`` specifies a regular expression pattern, and directories or files whose names (not DAG id) match any of the patterns would be ignored (under the hood, ``re.findall()`` is used to match the pattern). Overall it works like a ``.gitignore`` file. Use the ``#`` character to indicate a comment; all characters on a line following a ``#`` will be ignored. ``.airflowignore`` file should be put in your ``DAG_FOLDER``. For example, you can prepare a ``.airflowignore`` file with contents .. code:: project_a tenant_[\d] Then files like ``project_a_dag_1.py``, ``TESTING_project_a.py``, ``tenant_1.py``, ``project_a/dag_1.py``, and ``tenant_1/dag_1.py`` in your ``DAG_FOLDER`` would be ignored (If a directory's name matches any of the patterns, this directory and all its subfolders would not be scanned by Airflow at all. This improves efficiency of DAG finding). The scope of a ``.airflowignore`` file is the directory it is in plus all its subfolders. You can also prepare ``.airflowignore`` file for a subfolder in ``DAG_FOLDER`` and it would only be applicable for that subfolder.