Running Airflow locally¶
This quick start guide will help you bootstrap an Airflow standalone instance on your local machine.
Note
Successful installation requires a Python 3 environment.
Only pip
installation is currently officially supported.
While there have been successes with using other tools like poetry or
pip-tools, they do not share the same workflow as
pip
- especially when it comes to constraint vs. requirements management.
Installing via Poetry
or pip-tools
is not currently supported.
If you wish to install Airflow using those tools you should use the constraint files and convert them to appropriate format and workflow that your tool requires.
The installation of Airflow is painless if you are following the instructions below. Airflow uses
constraint files to enable reproducible installation, so using pip
and constraint files is recommended.
# Airflow needs a home. `~/airflow` is the default, but you can put it
# somewhere else if you prefer (optional)
export AIRFLOW_HOME=~/airflow
# Install Airflow using the constraints file
AIRFLOW_VERSION=2.3.4
PYTHON_VERSION="$(python --version | cut -d " " -f 2 | cut -d "." -f 1-2)"
# For example: 3.7
CONSTRAINT_URL="https://raw.githubusercontent.com/apache/airflow/constraints-${AIRFLOW_VERSION}/constraints-${PYTHON_VERSION}.txt"
# For example: https://raw.githubusercontent.com/apache/airflow/constraints-2.3.4/constraints-3.7.txt
pip install "apache-airflow==${AIRFLOW_VERSION}" --constraint "${CONSTRAINT_URL}"
# The Standalone command will initialise the database, make a user,
# and start all components for you.
airflow standalone
# Visit localhost:8080 in the browser and use the admin account details
# shown on the terminal to login.
# Enable the example_bash_operator dag in the home page
Upon running these commands, Airflow will create the $AIRFLOW_HOME
folder
and create the “airflow.cfg” file with defaults that will get you going fast.
You can override defaults using environment variables, see Configuration Reference.
You can inspect the file either in $AIRFLOW_HOME/airflow.cfg
, or through the UI in
the Admin->Configuration
menu. The PID file for the webserver will be stored
in $AIRFLOW_HOME/airflow-webserver.pid
or in /run/airflow/webserver.pid
if started by systemd.
Out of the box, Airflow uses a SQLite database, which you should outgrow
fairly quickly since no parallelization is possible using this database
backend. It works in conjunction with the
SequentialExecutor
which will
only run task instances sequentially. While this is very limiting, it allows
you to get up and running quickly and take a tour of the UI and the
command line utilities.
As you grow and deploy Airflow to production, you will also want to move away
from the standalone
command we use here to running the components
separately. You can read more in Production Deployment.
Here are a few commands that will trigger a few task instances. You should
be able to see the status of the jobs change in the example_bash_operator
DAG as you
run the commands below.
# run your first task instance
airflow tasks run example_bash_operator runme_0 2015-01-01
# run a backfill over 2 days
airflow dags backfill example_bash_operator \
--start-date 2015-01-01 \
--end-date 2015-01-02
If you want to run the individual parts of Airflow manually rather than using
the all-in-one standalone
command, you can instead run:
airflow db init
airflow users create \
--username admin \
--firstname Peter \
--lastname Parker \
--role Admin \
--email spiderman@superhero.org
airflow webserver --port 8080
airflow scheduler
What’s Next?¶
From this point, you can head to the Tutorial section for further examples or the How-to Guides section if you’re ready to get your hands dirty.