# Tutorial

This tutorial walks you through some of the fundamental Airflow concepts, objects, and their usage while writing your first pipeline.

## Example Pipeline definition

Here is an example of a basic pipeline definition. Do not worry if this looks complicated, a line by line explanation follows below.

    """
    Code that goes along with the Airflow tutorial located at:
    https://github.com/apache/incubator-airflow/blob/master/airflow/example_dags/tutorial.py
    """
    from airflow import DAG
    from airflow.operators.bash_operator import BashOperator
    from datetime import datetime, timedelta

    default_args = {
        'owner': 'airflow',
        'depends_on_past': False,
        'start_date': datetime(2015, 6, 1),
        'email': ['airflow@example.com'],
        'email_on_failure': False,
        'email_on_retry': False,
        'retries': 1,
        'retry_delay': timedelta(minutes=5),
        # 'queue': 'bash_queue',
        # 'pool': 'backfill',
        # 'priority_weight': 10,
        # 'end_date': datetime(2016, 1, 1),
    }

    dag = DAG('tutorial', default_args=default_args)

    # t1, t2 and t3 are examples of tasks created by instantiating operators
    t1 = BashOperator(
        task_id='print_date',
        bash_command='date',
        dag=dag)

    t2 = BashOperator(
        task_id='sleep',
        bash_command='sleep 5',
        retries=3,
        dag=dag)

    templated_command = """
        {% for i in range(5) %}
            echo "{{ ds }}"
            echo "{{ macros.ds_add(ds, 7)}}"
            echo "{{ params.my_param }}"
        {% endfor %}
    """

    t3 = BashOperator(
        task_id='templated',
        bash_command=templated_command,
        params={'my_param': 'Parameter I passed in'},
        dag=dag)

    t2.set_upstream(t1)
    t3.set_upstream(t1)

## It’s a DAG definition file

One thing to wrap your head around (it may not be very intuitive for everyone at first) is that this Airflow Python script is really just a configuration file specifying the DAG’s structure as code. The actual tasks defined here will run in a different context from the context of this script. Different tasks run on different workers at different points in time, which means that this script cannot be used to cross communicate between tasks. Note that for this purpose we have a more advanced feature called `XCom`.

People sometimes think of the DAG definition file as a place where they can do some actual data processing - that is not the case at all! The script’s purpose is to define a DAG object. It needs to evaluate quickly (seconds, not minutes) since the scheduler will execute it periodically to reflect the changes if any.

## Importing Modules

An Airflow pipeline is just a Python script that happens to define an Airflow DAG object. Let’s start by importing the libraries we will need.

    # The DAG object; we'll need this to instantiate a DAG
    from airflow import DAG

    # Operators; we need this to operate!
    from airflow.operators.bash_operator import BashOperator

## Default Arguments

We’re about to create a DAG and some tasks, and we have the choice to explicitly pass a set of arguments to each task’s constructor (which would become redundant), or (better!) we can define a dictionary of default parameters that we can use when creating tasks.

    from datetime import datetime, timedelta

    default_args = {
        'owner': 'airflow',
        'depends_on_past': False,
        'start_date': datetime(2015, 6, 1),
        'email': ['airflow@example.com'],
        'email_on_failure': False,
        'email_on_retry': False,
        'retries': 1,
        'retry_delay': timedelta(minutes=5),
        # 'queue': 'bash_queue',
        # 'pool': 'backfill',
        # 'priority_weight': 10,
        # 'end_date': datetime(2016, 1, 1),
    }

For more information about the BaseOperator’s parameters and what they do, refer to the `airflow.models.BaseOperator` documentation.

Also, note that you could easily define different sets of arguments that would serve different purposes. An example of that would be to have different settings between a production and development environment.

## Instantiate a DAG

We’ll need a DAG object to nest our tasks into. Here we pass a string that defines the `dag_id`, which serves as a unique identifier for your DAG. We also pass the default argument dictionary that we just defined and define a `schedule_interval` of 1 day for the DAG.

    dag = DAG(
        'tutorial', default_args=default_args, schedule_interval=timedelta(1))

## Tasks

Tasks are generated when instantiating operator objects. An object instantiated from an operator is called a constructor. The first argument `task_id` acts as a unique identifier for the task.

    t1 = BashOperator(
        task_id='print_date',
        bash_command='date',
        dag=dag)

    t2 = BashOperator(
        task_id='sleep',
        bash_command='sleep 5',
        retries=3,
        dag=dag)

Notice how we pass a mix of operator specific arguments (`bash_command`) and an argument common to all operators (`retries`) inherited from BaseOperator to the operator’s constructor. This is simpler than passing every argument for every constructor call. Also, notice that in the second task we override the `retries` parameter with `3`.

The precedence rules for a task are as follows:

1. Explicitly passed arguments
2. Values that exist in the `default_args` dictionary
3. The operator’s default value, if one exists

A task must include or inherit the arguments `task_id` and `owner`, otherwise Airflow will raise an exception.

## Templating with Jinja

Airflow leverages the power of Jinja Templating and provides the pipeline author with a set of built-in parameters and macros. Airflow also provides hooks for the pipeline author to define their own parameters, macros and templates.

This tutorial barely scratches the surface of what you can do with templating in Airflow, but the goal of this section is to let you know this feature exists, get you familiar with double curly brackets, and point to the most common template variable: `{{ ds }}` (today’s “date stamp”).

    templated_command = """
        {% for i in range(5) %}
            echo "{{ ds }}"
            echo "{{ macros.ds_add(ds, 7) }}"
            echo "{{ params.my_param }}"
        {% endfor %}
    """

    t3 = BashOperator(
        task_id='templated',
        bash_command=templated_command,
        params={'my_param': 'Parameter I passed in'},
        dag=dag)

Notice that the `templated_command` contains code logic in `{% %}` blocks, references parameters like `{{ ds }}`, calls a function as in `{{ macros.ds_add(ds, 7)}}`, and references a user-defined parameter in `{{ params.my_param }}`.

The `params` hook in `BaseOperator` allows you to pass a dictionary of parameters and/or objects to your templates. Please take the time to understand how the parameter `my_param` makes it through to the template.

Files can also be passed to the `bash_command` argument, like `bash_command='templated_command.sh'`, where the file location is relative to the directory containing the pipeline file (`tutorial.py` in this case). This may be desirable for many reasons, like separating your script’s logic and pipeline code, allowing for proper code highlighting in files composed in different languages, and general flexibility in structuring pipelines. It is also possible to define your `template_searchpath` as pointing to any folder locations in the DAG constructor call.

Using that same DAG constructor call, it is possible to define `user_defined_macros` which allow you to specify your own variables. For example, passing `dict(foo='bar')` to this argument allows you to use `{{ foo }}` in your templates. Moreover, specifying `user_defined_filters` allow you to register you own filters. For example, passing `dict(hello=lambda name: 'Hello %s' % name)` to this argument allows you to use `{{ 'world' | hello }}` in your templates. For more information regarding custom filters have a look at the Jinja Documentation

For more information on the variables and macros that can be referenced in templates, make sure to read through the Macros section

## Setting up Dependencies

We have two simple tasks that do not depend on each other. Here’s a few ways you can define dependencies between them:

    t2.set_upstream(t1)

    # This means that t2 will depend on t1
    # running successfully to run
    # It is equivalent to
    # t1.set_downstream(t2)

    t3.set_upstream(t1)

    # all of this is equivalent to
    # dag.set_dependency('print_date', 'sleep')
    # dag.set_dependency('print_date', 'templated')

Note that when executing your script, Airflow will raise exceptions when it finds cycles in your DAG or when a dependency is referenced more than once.

## Recap

Alright, so we have a pretty basic DAG. At this point your code should look something like this:

    """
    Code that goes along with the Airflow located at:
    http://airflow.readthedocs.org/en/latest/tutorial.html
    """
    from airflow import DAG
    from airflow.operators.bash_operator import BashOperator
    from datetime import datetime, timedelta

    default_args = {
        'owner': 'airflow',
        'depends_on_past': False,
        'start_date': datetime(2015, 6, 1),
        'email': ['airflow@example.com'],
        'email_on_failure': False,
        'email_on_retry': False,
        'retries': 1,
        'retry_delay': timedelta(minutes=5),
        # 'queue': 'bash_queue',
        # 'pool': 'backfill',
        # 'priority_weight': 10,
        # 'end_date': datetime(2016, 1, 1),
    }

    dag = DAG(
        'tutorial', default_args=default_args, schedule_interval=timedelta(1))

    # t1, t2 and t3 are examples of tasks created by instantiating operators
    t1 = BashOperator(
        task_id='print_date',
        bash_command='date',
        dag=dag)

    t2 = BashOperator(
        task_id='sleep',
        bash_command='sleep 5',
        retries=3,
        dag=dag)

    templated_command = """
        {% for i in range(5) %}
            echo "{{ ds }}"
            echo "{{ macros.ds_add(ds, 7)}}"
            echo "{{ params.my_param }}"
        {% endfor %}
    """

    t3 = BashOperator(
        task_id='templated',
        bash_command=templated_command,
        params={'my_param': 'Parameter I passed in'},
        dag=dag)

    t2.set_upstream(t1)
    t3.set_upstream(t1)

## Testing

### Running the Script

Time to run some tests. First let’s make sure that the pipeline parses. Let’s assume we’re saving the code from the previous step in `tutorial.py` in the DAGs folder referenced in your `airflow.cfg`. The default location for your DAGs is `~/airflow/dags`.

    python ~/airflow/dags/tutorial.py

If the script does not raise an exception it means that you haven’t done anything horribly wrong, and that your Airflow environment is somewhat sound.

### Command Line Metadata Validation

Let’s run a few commands to validate this script further.

    # print the list of active DAGs
    airflow list_dags

    # prints the list of tasks the "tutorial" dag_id
    airflow list_tasks tutorial

    # prints the hierarchy of tasks in the tutorial DAG
    airflow list_tasks tutorial --tree

### Testing

Let’s test by running the actual task instances on a specific date. The date specified in this context is an `execution_date`, which simulates the scheduler running your task or dag at a specific date + time:

    # command layout: command subcommand dag_id task_id date

    # testing print_date
    airflow test tutorial print_date 2015-06-01

    # testing sleep
    airflow test tutorial sleep 2015-06-01

Now remember what we did with templating earlier? See how this template gets rendered and executed by running this command:

    # testing templated
    airflow test tutorial templated 2015-06-01

This should result in displaying a verbose log of events and ultimately running your bash command and printing the result.

Note that the `airflow test` command runs task instances locally, outputs their log to stdout (on screen), doesn’t bother with dependencies, and doesn’t communicate state (running, success, failed, …) to the database. It simply allows testing a single task instance.

### Backfill

Everything looks like it’s running fine so let’s run a backfill. `backfill` will respect your dependencies, emit logs into files and talk to the database to record status. If you do have a webserver up, you’ll be able to track the progress. `airflow webserver` will start a web server if you are interested in tracking the progress visually as your backfill progresses.

Note that if you use `depends_on_past=True`, individual task instances will depend on the success of the preceding task instance, except for the start_date specified itself, for which this dependency is disregarded.

The date range in this context is a `start_date` and optionally an `end_date`, which are used to populate the run schedule with task instances from this dag.

    # optional, start a web server in debug mode in the background
    # airflow webserver --debug &

    # start your backfill on a date range
    airflow backfill tutorial -s 2015-06-01 -e 2015-06-07

## What’s Next?

That’s it, you’ve written, tested and backfilled your very first Airflow pipeline. Merging your code into a code repository that has a master scheduler running against it should get it to get triggered and run every day.

Here’s a few things you might want to do next:

- Take an in-depth tour of the UI - click all the things!

- Keep reading the docs! Especially the sections on:

  > - Command line interface
  > - Operators
  > - Macros

- Write your first pipeline!