This tutorial walks you through some of the fundamental Airflow concepts, objects, and their usage while writing your first pipeline.
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/airbnb/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': ['[email protected]'],
'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)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.
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 BashOperatorWe'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': ['[email protected]'],
'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 :py:class: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.
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 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:
- Explicitly passed arguments
- Values that exist in the
default_argsdictionary - 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.
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 }}.
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.
For more information on the variables and macros that can be referenced in templates, make sure to read through the :ref:`macros` section
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.
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': ['[email protected]'],
'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)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.pyIf 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.
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 --treeLet'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 2015-06-01Now 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 2015-06-01This 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.
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 2015-06-07That'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!