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README.md	2.1.2-debian-10-r12 release	2021-07-28 10:04:55 +00:00
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README.md

What is Apache Airflow Scheduler?

Airflow is a platform to programmatically author, schedule and monitor workflows. Airflow Scheduler is one of the required components when the CeleryExecutor is configured.

https://airflow.apache.org/

TL;DR

Docker Compose

$ curl -LO https://raw.githubusercontent.com/bitnami/bitnami-docker-airflow-scheduler/master/docker-compose.yml
$ docker-compose up

You can find the default credentials and available configuration options in the Environment Variables section.

Why use Bitnami Images?

Bitnami closely tracks upstream source changes and promptly publishes new versions of this image using our automated systems.
With Bitnami images the latest bug fixes and features are available as soon as possible.
Bitnami containers, virtual machines and cloud images use the same components and configuration approach - making it easy to switch between formats based on your project needs.
All our images are based on minideb a minimalist Debian based container image which gives you a small base container image and the familiarity of a leading Linux distribution.
All Bitnami images available in Docker Hub are signed with Docker Content Trust (DCT). You can use DOCKER_CONTENT_TRUST=1 to verify the integrity of the images.
Bitnami container images are released daily with the latest distribution packages available.

This CVE scan report contains a security report with all open CVEs. To get the list of actionable security issues, find the "latest" tag, click the vulnerability report link under the corresponding "Security scan" field and then select the "Only show fixable" filter on the next page.

Supported tags and respective `Dockerfile` links

Learn more about the Bitnami tagging policy and the difference between rolling tags and immutable tags in our documentation page.

Subscribe to project updates by watching the bitnami/airflow GitHub repo.

Prerequisites

To run this application you need Docker Engine >= 1.10.0. Docker Compose is recommended with a version 1.6.0 or later.

How to use this image

Airflow Scheduler is a component of an Airflow solution configuring with the CeleryExecutor. Hence, you will need to rest of Airflow components for this image to work. You will need an Airflow Webserver, one or more Airflow Workers, a PostgreSQL database and a Redis(TM) server.

Using Docker Compose

The main folder of this repository contains a functional docker-compose.yml file. Run the application using it as shown below:

$ curl -sSL https://raw.githubusercontent.com/bitnami/bitnami-docker-airflow-scheduler/master/docker-compose.yml > docker-compose.yml
$ docker-compose up -d

Using the Docker Command Line

If you want to run the application manually instead of using docker-compose, these are the basic steps you need to run:

Create a network

$ docker network create airflow-tier

Create a volume for PostgreSQL persistence and create a PostgreSQL container

$ docker volume create --name postgresql_data
$ docker run -d --name postgresql \
  -e POSTGRESQL_USERNAME=bn_airflow \
  -e POSTGRESQL_PASSWORD=bitnami1 \
  -e POSTGRESQL_DATABASE=bitnami_airflow \
  --net airflow-tier \
  --volume postgresql_data:/bitnami/postgresql \
  bitnami/postgresql:latest

Create a volume for Redis(TM) persistence and create a Redis(TM) container

$ docker volume create --name redis_data
$ docker run -d --name redis \
  -e ALLOW_EMPTY_PASSWORD=yes \
  --net airflow-tier \
  --volume redis_data:/bitnami \
  bitnami/redis:latest

Create volumes for Airflow persistence and launch the container

$ docker volume create --name airflow_data
$ docker run -d --name airflow -p 8080:8080 \
  -e AIRFLOW_FERNET_KEY=46BKJoQYlPPOexq0OhDZnIlNepKFf87WFwLbfzqDDho= \
  -e AIRFLOW_EXECUTOR=CeleryExecutor \
  -e AIRFLOW_DATABASE_NAME=bitnami_airflow \
  -e AIRFLOW_DATABASE_USERNAME=bn_airflow \
  -e AIRFLOW_DATABASE_PASSWORD=bitnami1 \
  -e AIRFLOW_LOAD_EXAMPLES=yes \
  -e AIRFLOW_PASSWORD=bitnami123 \
  -e AIRFLOW_USERNAME=user \
  -e AIRFLOW_EMAIL=user@example.com \
  --net airflow-tier \
  --volume airflow_data:/bitnami \
  bitnami/airflow:latest

Create volumes for Airflow Scheduler persistence and launch the container

$ docker volume create --name airflow_scheduler_data
$ docker run -d --name airflow-scheduler \
  -e AIRFLOW_FERNET_KEY=46BKJoQYlPPOexq0OhDZnIlNepKFf87WFwLbfzqDDho= \
  -e AIRFLOW_EXECUTOR=CeleryExecutor \
  -e AIRFLOW_DATABASE_NAME=bitnami_airflow \
  -e AIRFLOW_DATABASE_USERNAME=bn_airflow \
  -e AIRFLOW_DATABASE_PASSWORD=bitnami1 \
  -e AIRFLOW_LOAD_EXAMPLES=yes \
  --net airflow-tier \
  --volume airflow_scheduler_data:/bitnami \
  bitnami/airflow-scheduler:latest

Create volumes for Airflow Worker persistence and launch the container

$ docker volume create --name airflow_worker_data
$ docker run -d --name airflow-worker \
  -e AIRFLOW_FERNET_KEY=46BKJoQYlPPOexq0OhDZnIlNepKFf87WFwLbfzqDDho= \
  -e AIRFLOW_EXECUTOR=CeleryExecutor \
  -e AIRFLOW_DATABASE_NAME=bitnami_airflow \
  -e AIRFLOW_DATABASE_USERNAME=bn_airflow \
  -e AIRFLOW_DATABASE_PASSWORD=bitnami1 \
  --net airflow-tier \
  --volume airflow_worker_data:/bitnami \
  bitnami/airflow-worker:latest

Access your application at http://your-ip:8080

Persisting your application

If you remove the container all your data and configurations will be lost, and the next time you run the image the database will be reinitialized. To avoid this loss of data, you should mount a volume that will persist even after the container is removed.

For persistence you should mount a volume at the /bitnami path. Additionally you should mount volumes for persistence of PostgreSQL data and Redis(TM) data

The above examples define docker volumes namely postgresql_data, redis_data, airflow_data, airflow_scheduler_data and airflow_worker_data. The Airflow Scheduler application state will persist as long as these volumes are not removed.

To avoid inadvertent removal of these volumes you can mount host directories as data volumes. Alternatively you can make use of volume plugins to host the volume data.

Mount host directories as data volumes with Docker Compose

The following docker-compose.yml template demonstrates the use of host directories as data volumes.

version: '2'
services:
  postgresql:
    image: 'bitnami/postgresql:latest'
    environment:
      - POSTGRESQL_DATABASE=bitnami_airflow
      - POSTGRESQL_USERNAME=bn_airflow
      - POSTGRESQL_PASSWORD=bitnami1
    volumes:
      - /path/to/airflow-persistence:/bitnami
  redis:
    image: 'bitnami/redis:latest'
    environment:
      - ALLOW_EMPTY_PASSWORD=yes
    volumes:
      - /path/to/airflow-persistence:/bitnami
  airflow-worker:
    image: bitnami/airflow-worker:latest
    environment:
      - AIRFLOW_FERNET_KEY=46BKJoQYlPPOexq0OhDZnIlNepKFf87WFwLbfzqDDho=
      - AIRFLOW_EXECUTOR=CeleryExecutor
      - AIRFLOW_DATABASE_NAME=bitnami_airflow
      - AIRFLOW_DATABASE_USERNAME=bn_airflow
      - AIRFLOW_DATABASE_PASSWORD=bitnami1
      - AIRFLOW_LOAD_EXAMPLES=yes
    volumes:
      - /path/to/airflow-persistence:/bitnami
  airflow-scheduler:
    image: bitnami/airflow-scheduler:latest
    environment:
      - AIRFLOW_FERNET_KEY=46BKJoQYlPPOexq0OhDZnIlNepKFf87WFwLbfzqDDho=
      - AIRFLOW_EXECUTOR=CeleryExecutor
      - AIRFLOW_DATABASE_NAME=bitnami_airflow
      - AIRFLOW_DATABASE_USERNAME=bn_airflow
      - AIRFLOW_DATABASE_PASSWORD=bitnami1
      - AIRFLOW_LOAD_EXAMPLES=yes
    volumes:
      - /path/to/airflow-persistence:/bitnami
  airflow:
    image: bitnami/airflow:latest
    environment:
      - AIRFLOW_FERNET_KEY=46BKJoQYlPPOexq0OhDZnIlNepKFf87WFwLbfzqDDho=
      - AIRFLOW_EXECUTOR=CeleryExecutor
      - AIRFLOW_DATABASE_NAME=bitnami_airflow
      - AIRFLOW_DATABASE_USERNAME=bn_airflow
      - AIRFLOW_DATABASE_PASSWORD=bitnami1
      - AIRFLOW_PASSWORD=bitnami123
      - AIRFLOW_USERNAME=user
      - AIRFLOW_EMAIL=user@example.com
    ports:
      - '8080:8080'
    volumes:
      - /path/to/airflow-persistence:/bitnami

Mount host directories as data volumes using the Docker command line

Create a network (if it does not exist)

$ docker network create airflow-tier

Create the PostgreSQL container with host volumes

$ docker run -d --name postgresql \
  -e POSTGRESQL_USERNAME=bn_airflow \
  -e POSTGRESQL_PASSWORD=bitnami1 \
  -e POSTGRESQL_DATABASE=bitnami_airflow \
  --net airflow-tier \
  --volume /path/to/postgresql-persistence:/bitnami \
  bitnami/postgresql:latest

Create the Redis(TM) container with host volumes

$ docker run -d --name redis \
  -e ALLOW_EMPTY_PASSWORD=yes \
  --net airflow-tier \
  --volume /path/to/redis-persistence:/bitnami \
  bitnami/redis:latest

Create the Airflow container with host volumes

$ docker run -d --name airflow -p 8080:8080 \
  -e AIRFLOW_FERNET_KEY=46BKJoQYlPPOexq0OhDZnIlNepKFf87WFwLbfzqDDho= \
  -e AIRFLOW_EXECUTOR=CeleryExecutor \
  -e AIRFLOW_DATABASE_NAME=bitnami_airflow \
  -e AIRFLOW_DATABASE_USERNAME=bn_airflow \
  -e AIRFLOW_DATABASE_PASSWORD=bitnami1 \
  -e AIRFLOW_LOAD_EXAMPLES=yes \
  -e AIRFLOW_PASSWORD=bitnami123 \
  -e AIRFLOW_USERNAME=user \
  -e AIRFLOW_EMAIL=user@example.com \
  --net airflow-tier \
  --volume /path/to/airflow-persistence:/bitnami \
  bitnami/airflow:latest

Create the Airflow Scheduler container with host volumes

$ docker run -d --name airflow-scheduler \
  -e AIRFLOW_FERNET_KEY=46BKJoQYlPPOexq0OhDZnIlNepKFf87WFwLbfzqDDho= \
  -e AIRFLOW_EXECUTOR=CeleryExecutor \
  -e AIRFLOW_DATABASE_NAME=bitnami_airflow \
  -e AIRFLOW_DATABASE_USERNAME=bn_airflow \
  -e AIRFLOW_DATABASE_PASSWORD=bitnami1 \
  -e AIRFLOW_LOAD_EXAMPLES=yes \
  --net airflow-tier \
  --volume /path/to/airflow-scheduler-persistence:/bitnami \
  bitnami/airflow-scheduler:latest

Create the Airflow Worker container with host volumes

$ docker run -d --name airflow-worker \
  -e AIRFLOW_FERNET_KEY=46BKJoQYlPPOexq0OhDZnIlNepKFf87WFwLbfzqDDho= \
  -e AIRFLOW_EXECUTOR=CeleryExecutor \
  -e AIRFLOW_DATABASE_NAME=bitnami_airflow \
  -e AIRFLOW_DATABASE_USERNAME=bn_airflow \
  -e AIRFLOW_DATABASE_PASSWORD=bitnami1 \
  --net airflow-tier \
  --volume /path/to/airflow-worker-persistence:/bitnami \
  bitnami/airflow-worker:latest

Configuration

Installing additional python modules

This container supports the installation of additional python modules at start-up time. In order to do that, you can mount a requirements.txt file with your specific needs under the path /bitnami/python/requirements.txt.

Environment variables

The Airflow Scheduler instance can be customized by specifying environment variables on the first run. The following environment values are provided to customize Airflow Scheduler:

Airflow Scheduler configuration

AIRFLOW_EXECUTOR: Airflow Scheduler executor. Default: SequentialExecutor
AIRFLOW_FERNET_KEY: Airflow Scheduler Fernet key. No defaults.
AIRFLOW_WEBSERVER_HOST: Airflow Scheduler webserver host. Default: airflow
AIRFLOW_WEBSERVER_PORT_NUMBER: Airflow Scheduler webserver port. Default: 8080
AIRFLOW_LOAD_EXAMPLES: To load example tasks into the application. Default: yes
AIRFLOW_HOSTNAME_CALLABLE: Method to obtain the hostname. No defaults.

Use an existing database

AIRFLOW_DATABASE_HOST: Hostname for PostgreSQL server. Default: postgresql
AIRFLOW_DATABASE_PORT_NUMBER: Port used by PostgreSQL server. Default: 5432
AIRFLOW_DATABASE_NAME: Database name that Airflow Scheduler will use to connect with the database. Default: bitnami_airflow
AIRFLOW_DATABASE_USERNAME: Database user that Airflow Scheduler will use to connect with the database. Default: bn_airflow
AIRFLOW_DATABASE_PASSWORD: Database password that Airflow Scheduler will use to connect with the database. No defaults.
AIRFLOW_DATABASE_USE_SSL: Set to yes if the database uses SSL. Default: no
AIRFLOW_REDIS_USE_SSL: Set to yes if Redis(TM) uses SSL. Default: no
REDIS_HOST: Hostname for Redis(TM) server. Default: redis
REDIS_PORT_NUMBER: Port used by Redis(TM) server. Default: 6379
REDIS_USER: USER that Airflow Scheduler will use to connect with Redis(TM). No defaults.
REDIS_PASSWORD: Password that Airflow Scheduler will use to connect with Redis(TM). No defaults.

In addition to the previous environment variables, all the parameters from the configuration file can be overwritten by using environment variables with this format: AIRFLOW__{SECTION}__{KEY}. Note the double underscores.

Specifying Environment variables using Docker Compose

version: '2'

services:
  airflow:
    image: bitnami/airflow:1
    environment:
      - AIRFLOW_FERNET_KEY=46BKJoQYlPPOexq0OhDZnIlNepKFf87WFwLbfzqDDho=
      - AIRFLOW_EXECUTOR=CeleryExecutor
      - AIRFLOW_DATABASE_NAME=bitnami_airflow
      - AIRFLOW_DATABASE_USERNAME=bn_airflow
      - AIRFLOW_DATABASE_PASSWORD=bitnami1
      - AIRFLOW_PASSWORD=bitnami123
      - AIRFLOW_USERNAME=user
      - AIRFLOW_EMAIL=user@example.com

Specifying Environment variables on the Docker command line

$ docker run -d --name airflow -p 8080:8080 \
    -e AIRFLOW_FERNET_KEY=46BKJoQYlPPOexq0OhDZnIlNepKFf87WFwLbfzqDDho= \
    -e AIRFLOW_EXECUTOR=CeleryExecutor \
    -e AIRFLOW_DATABASE_NAME=bitnami_airflow \
    -e AIRFLOW_DATABASE_USERNAME=bn_airflow \
    -e AIRFLOW_DATABASE_PASSWORD=bitnami1 \
    -e AIRFLOW_PASSWORD=bitnami123 \
    -e AIRFLOW_USERNAME=user \
    -e AIRFLOW_EMAIL=user@example.com \
    --volume airflow_data:/bitnami \
    bitnami/airflow:latest

Notable Changes

1.10.15-debian-10-r18 and 2.0.1-debian-10-r52

The size of the container image has been decreased.
The configuration logic is now based on Bash scripts in the rootfs/ folder.

Contributing

We'd love for you to contribute to this container. You can request new features by creating an issue, or submit a pull request with your contribution.

Issues

If you encountered a problem running this container, you can file an issue. For us to provide better support, be sure to include the following information in your issue:

Host OS and version
Docker version ($ docker version)
Output of $ docker info
Version of this container ($ echo $BITNAMI_IMAGE_VERSION inside the container)
The command you used to run the container, and any relevant output you saw (masking any sensitive information)

License

Licensed 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.