Setup Apache Airflow Using Docker Compose

Do you find it so complicated to set up Apache Airflow for using right away? Don't worry, I can show you a simple and fast way! Are you ready? Then please take a look the instruction below.

Getting Started

• Install docker if you haven't.
• Install Docker Compose >= v1.27.0

Download docker-compose.yml

curl -LfO 'https://airflow.apache.org/docs/apache-airflow/2.0.2/docker-compose.yaml'

The docker-compose.yml file will look like this

# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you 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.
#

# Basic Airflow cluster configuration for CeleryExecutor with Redis and PostgreSQL.
#
# WARNING: This configuration is for local development. Do not use it in a production deployment.
#
# This configuration supports basic configuration using environment variables or an .env file
# The following variables are supported:
#
# AIRFLOW_IMAGE_NAME         - Docker image name used to run Airflow.
#                              Default: apache/airflow:master-python3.8
# AIRFLOW_UID                - User ID in Airflow containers
#                              Default: 50000
# AIRFLOW_GID                - Group ID in Airflow containers
#                              Default: 50000
# _AIRFLOW_WWW_USER_USERNAME - Username for the administrator account.
#                              Default: airflow
# _AIRFLOW_WWW_USER_PASSWORD - Password for the administrator account.
#                              Default: airflow
#
# Feel free to modify this file to suit your needs.
---
version: '3'
x-airflow-common:
  &airflow-common
  image: ${AIRFLOW_IMAGE_NAME:-apache/airflow:2.0.2}
  environment:
    &airflow-common-env
    AIRFLOW__CORE__EXECUTOR: CeleryExecutor
    AIRFLOW__CORE__SQL_ALCHEMY_CONN: postgresql+psycopg2://airflow:airflow@postgres/airflow
    AIRFLOW__CELERY__RESULT_BACKEND: db+postgresql://airflow:airflow@postgres/airflow
    AIRFLOW__CELERY__BROKER_URL: redis://:@redis:6379/0
    AIRFLOW__CORE__FERNET_KEY: ''
    AIRFLOW__CORE__DAGS_ARE_PAUSED_AT_CREATION: 'true'
    AIRFLOW__CORE__LOAD_EXAMPLES: 'true'
    AIRFLOW__API__AUTH_BACKEND: 'airflow.api.auth.backend.basic_auth'
  volumes:
    - ./dags:/opt/airflow/dags
    - ./logs:/opt/airflow/logs
    - ./plugins:/opt/airflow/plugins
  user: "${AIRFLOW_UID:-50000}:${AIRFLOW_GID:-50000}"
  depends_on:
    redis:
      condition: service_healthy
    postgres:
      condition: service_healthy

services:
  postgres:
    image: postgres:13
    environment:
      POSTGRES_USER: airflow
      POSTGRES_PASSWORD: airflow
      POSTGRES_DB: airflow
    volumes:
      - postgres-db-volume:/var/lib/postgresql/data
    healthcheck:
      test: ["CMD", "pg_isready", "-U", "airflow"]
      interval: 5s
      retries: 5
    restart: always

  redis:
    image: redis:latest
    ports:
      - 6379:6379
    healthcheck:
      test: ["CMD", "redis-cli", "ping"]
      interval: 5s
      timeout: 30s
      retries: 50
    restart: always

  airflow-webserver:
    <<: *airflow-common
    command: webserver
    ports:
      - 8080:8080
    healthcheck:
      test: ["CMD", "curl", "--fail", "http://localhost:8080/health"]
      interval: 10s
      timeout: 10s
      retries: 5
    restart: always

  airflow-scheduler:
    <<: *airflow-common
    command: scheduler
    restart: always

  airflow-worker:
    <<: *airflow-common
    command: celery worker
    restart: always

  airflow-init:
    <<: *airflow-common
    command: version
    environment:
      <<: *airflow-common-env
      _AIRFLOW_DB_UPGRADE: 'true'
      _AIRFLOW_WWW_USER_CREATE: 'true'
      _AIRFLOW_WWW_USER_USERNAME: ${_AIRFLOW_WWW_USER_USERNAME:-airflow}
      _AIRFLOW_WWW_USER_PASSWORD: ${_AIRFLOW_WWW_USER_PASSWORD:-airflow}

  flower:
    <<: *airflow-common
    command: celery flower
    ports:
      - 5555:5555
    healthcheck:
      test: ["CMD", "curl", "--fail", "http://localhost:5555/"]
      interval: 10s
      timeout: 10s
      retries: 5
    restart: always

volumes:
  postgres-db-volume:

Initialize the environment

• Prepare some folders to store application data and script

mkdir ./dags ./logs ./plugins

echo -e "AIRFLOW_UID=$(id -u)\nAIRFLOW_GID=0" > .env

• Initialize database

docker-compose up airflow-init

• Use the login user airflow and the password airflow.

Start the application

• Start all containers defined in the docker-compose.yml

docker-compose up

• Access the web interface http://localhost:8080

How to install additional python packages

• Create new file Dockerfile to customize airflow image based on apache/airflow:2.0.2

FROM apache/airflow:2.0.2

# Now install the pandas package
RUN pip install pandas

# Or install all packages from requirements.txt
COPY requirements.txt requirements.txt
RUN pip install -r requirements.txt

• Update the docker-compose.yml

Change the line image: ${AIRFLOW_IMAGE_NAME:-apache/airflow:2.0.2} to build: .

version: '3'
x-airflow-common:
  &airflow-common
  # image: ${AIRFLOW_IMAGE_NAME:-apache/airflow:2.0.2}
  build: .
  environment:
  ...

• Build the image by run the command docker-compose build

• Run the updated image docker-compose up

Reference

• Airflow Docs