Running Airflow in Docker

This quick-start guide will allow you to quickly get Airflow up and running with CeleryExecutor in Docker. For running airflow in production, we recommend running on kubernetes with the official helm chart.

Customizing the quick-start Docker Compose

DO NOT attempt to customize images and the Docker Compose if you do not know exactly what you are doing, do not know Docker Compose, or are not prepared to debug and resolve problems on your own. If you do not know Docker Compose and expect Airflow to just work beyond following precisely the quick-start, then please use other ways of running Airflow - for example Running Airflow locally for testing and trying and Official Airflow Community Helm Chart for production purposes.

Even if many users think of Docker Compose as “ready to use”, it is really a developer tool, that requires the user to know very well how docker images, containers, docker compose networking, volumes, naming, image building works. It is extremely easy to make mistakes that lead to difficult to diagnose problems and if you are not ready to spend your own time on learning and diagnosing and resolving those problems on your own do not follow this path. You have been warned.

If you customize, or modify images, the compose file and see problem do not expect you will get a lot of help with solving those problems in the Airflow support channels. Most of the problems you will experience are Docker Compose related problems and if you need help in solving them, there are dedicated channels in Docker Compose that you can use.

Production readiness


DO NOT expect the Docker Compose below will be enough to run production-ready Docker Compose Airflow installation using it. This is truly quick-start docker-compose for you to get Airflow up and running locally and get your hands dirty with Airflow. Configuring a Docker-Compose installation that is ready for production requires an intrinsic knowledge of Docker Compose, a lot of customization and possibly even writing the Docker Compose file that will suit your needs from the scratch. It’s probably OK if you want to run Docker Compose-based deployment, but short of becoming a Docker Compose expert, it’s highly unlikely you will get robust deployment with it.

If you want to get an easy to configure Docker-based deployment that Airflow Community develops, supports and can provide support with deployment, you should consider using Kubernetes and deploying Airflow using Official Airflow Community Helm Chart.

Before you begin

Follow these steps to install the necessary tools.

  1. Install Docker Community Edition (CE) on your workstation. Depending on the OS, you may need to configure your Docker instance to use 4.00 GB of memory for all containers to run properly. Please refer to the Resources section if using Docker for Windows or Docker for Mac for more information.

  2. Install Docker Compose v1.29.1 and newer on your workstation.

Older versions of docker-compose do not support all the features required by docker-compose.yaml file, so double check that your version meets the minimum version requirements.


Default amount of memory available for Docker on MacOS is often not enough to get Airflow up and running. If enough memory is not allocated, it might lead to airflow webserver continuously restarting. You should at least allocate 4GB memory for the Docker Engine (ideally 8GB). You can check and change the amount of memory in Resources

You can also check if you have enough memory by running this command:

docker run --rm "debian:bullseye-slim" bash -c 'numfmt --to iec $(echo $(($(getconf _PHYS_PAGES) * $(getconf PAGE_SIZE))))'


To deploy Airflow on Docker Compose, you should fetch docker-compose.yaml.

curl -LfO ''

This file contains several service definitions:

  • airflow-scheduler - The scheduler monitors all tasks and DAGs, then triggers the task instances once their dependencies are complete.

  • airflow-webserver - The webserver is available at http://localhost:8080.

  • airflow-worker - The worker that executes the tasks given by the scheduler.

  • airflow-init - The initialization service.

  • postgres - The database.

  • redis - The redis - broker that forwards messages from scheduler to worker.

Optionally you can enable flower by adding --profile flower option e.g. docker-compose --profile flower up or by explicitly targeted on the command line e.g. docker-compose up flower.

  • flower - The flower app for monitoring the environment.

    It is available at http://localhost:5555.

In general, if you want to use airflow locally, your DAGs may try to connect to servers which are running on the host. In order to achieve that, an extra configuration must be added in docker-compose.yaml. For example, on Linux the configuration must be in the section services: airflow-worker adding extra_hosts: - "host.docker.internal:host-gateway"; and use host.docker.internal instead of localhost. This configuration vary in different platforms. Please, see documentation for Windows and Mac for further information.

All these services allow you to run Airflow with CeleryExecutor. For more information, see Architecture Overview.

Some directories in the container are mounted, which means that their contents are synchronized between your computer and the container.

  • ./dags - you can put your DAG files here.

  • ./logs - contains logs from task execution and scheduler.

  • ./plugins - you can put your custom plugins here.

This file uses the latest Airflow image (apache/airflow). If you need to install a new Python library or system library, you can build your image.

Using custom images

When you want to run Airflow locally, you might want to use an extended image, containing some additional dependencies - for example you might add new python packages, or upgrade airflow providers to a later version. This can be done very easily by placing a custom Dockerfile alongside your docker-compose.yaml. Then you can use docker-compose build command to build your image (you need to do it only once). You can also add the --build flag to your docker-compose commands to rebuild the images on-the-fly when you run other docker-compose commands.

Examples of how you can extend the image with custom providers, python packages, apt packages and more can be found in Building the image.

Initializing Environment

Before starting Airflow for the first time, You need to prepare your environment, i.e. create the necessary files, directories and initialize the database.

Setting the right Airflow user

On Linux, the quick-start needs to know your host user id and needs to have group id set to 0. Otherwise the files created in dags, logs and plugins will be created with root user. You have to make sure to configure them for the docker-compose:

mkdir -p ./dags ./logs ./plugins
echo -e "AIRFLOW_UID=$(id -u)" > .env

See Docker Compose environment variables

For other operating systems, you will get warning that AIRFLOW_UID is not set, but you can ignore it. You can also manually create the .env file in the same folder your docker-compose.yaml is placed with this content to get rid of the warning:


Initialize the database

On all operating systems, you need to run database migrations and create the first user account. To do it, run.

docker-compose up airflow-init

After initialization is complete, you should see a message like below.

airflow-init_1       | Upgrades done
airflow-init_1       | Admin user airflow created
airflow-init_1       | 2.3.3
start_airflow-init_1 exited with code 0

The account created has the login airflow and the password airflow.

Cleaning-up the environment

The docker-compose we prepare is a “Quick-start” one. It is not intended to be used in production and it has a number of caveats - one of them being that the best way to recover from any problem is to clean it up and restart from the scratch.

The best way to do it is to:

  • Run docker-compose down --volumes --remove-orphans command in the directory you downloaded the docker-compose.yaml file

  • remove the whole directory where you downloaded the docker-compose.yaml file rm -rf '<DIRECTORY>'

  • re-download the docker-compose.yaml file

  • re-start following the instructions from the very beginning in this guide

Running Airflow

Now you can start all services:

docker-compose up

In the second terminal you can check the condition of the containers and make sure that no containers are in unhealthy condition:

$ docker ps
CONTAINER ID   IMAGE                  COMMAND                  CREATED          STATUS                    PORTS                              NAMES
247ebe6cf87a   apache/airflow:2.3.3   "/usr/bin/dumb-init …"   3 minutes ago    Up 3 minutes (healthy)    8080/tcp                           compose_airflow-worker_1
ed9b09fc84b1   apache/airflow:2.3.3   "/usr/bin/dumb-init …"   3 minutes ago    Up 3 minutes (healthy)    8080/tcp                           compose_airflow-scheduler_1
7cb1fb603a98   apache/airflow:2.3.3   "/usr/bin/dumb-init …"   3 minutes ago    Up 3 minutes (healthy)>8080/tcp             compose_airflow-webserver_1
74f3bbe506eb   postgres:13            "docker-entrypoint.s…"   18 minutes ago   Up 17 minutes (healthy)   5432/tcp                           compose_postgres_1
0bd6576d23cb   redis:latest           "docker-entrypoint.s…"   10 hours ago     Up 17 minutes (healthy)>6379/tcp             compose_redis_1

Accessing the environment

After starting Airflow, you can interact with it in 3 ways;

Running the CLI commands

You can also run CLI commands, but you have to do it in one of the defined airflow-* services. For example, to run airflow info, run the following command:

docker-compose run airflow-worker airflow info

If you have Linux or Mac OS, you can make your work easier and download a optional wrapper scripts that will allow you to run commands with a simpler command.

curl -LfO ''
chmod +x

Now you can run commands easier.

./ info

You can also use bash as parameter to enter interactive bash shell in the container or python to enter python container.

./ bash
./ python

Accessing the web interface

Once the cluster has started up, you can log in to the web interface and try to run some tasks.

The webserver is available at: http://localhost:8080. The default account has the login airflow and the password airflow.

Sending requests to the REST API

Basic username password authentication is currently supported for the REST API, which means you can use common tools to send requests to the API.

The webserver is available at: http://localhost:8080. The default account has the login airflow and the password airflow.

Here is a sample curl command, which sends a request to retrieve a pool list:

curl -X GET  \
    --user "airflow:airflow" \

Cleaning up

To stop and delete containers, delete volumes with database data and download images, run:

docker-compose down --volumes --rmi all

FAQ: Frequently asked questions

ModuleNotFoundError: No module named 'XYZ'

The Docker Compose file uses the latest Airflow image (apache/airflow). If you need to install a new Python library or system library, you can customize and extend it.

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.

Environment variables supported by Docker Compose

Do not confuse the variable names here with the build arguments set when image is built. The AIRFLOW_UID build arg defaults to 50000 when the image is built, so it is “baked” into the image. On the other hand, the environment variables below can be set when the container is running, using - for example - result of id -u command, which allows to use the dynamic host runtime user id which is unknown at the time of building the image.





Airflow Image to use.



UID of the user to run Airflow containers as. Override if you want to use use non-default Airflow UID (for example when you map folders from host, it should be set to result of id -u call. When it is changed, a user with the UID is created with default name inside the container and home of the use is set to /airflow/home/ in order to share Python libraries installed there. This is in order to achieve the OpenShift compatibility. See more in the Arbitrary Docker User



Before Airflow 2.2, the Docker Compose also had AIRFLOW_GID parameter, but it did not provide any additional functionality - only added confusion - so it has been removed.

Those additional variables are useful in case you are trying out/testing Airflow installation via Docker Compose. They are not intended to be used in production, but they make the environment faster to bootstrap for first time users with the most common customizations.





Username for the administrator UI account. If this value is specified, admin UI user gets created automatically. This is only useful when you want to run Airflow for a test-drive and want to start a container with embedded development database.



Password for the administrator UI account. Only used when _AIRFLOW_WWW_USER_USERNAME set.



If not empty, airflow containers will attempt to install requirements specified in the variable. example: lxml==4.6.3 charset-normalizer==1.4.1. Available in Airflow image 2.1.1 and above.

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