首页 » 使用Docker来启动Airflow

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# to you under the Apache License, Version 2.0 (the
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#
#   http://www.apache.org/licenses/LICENSE-2.0
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# 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:
      - 12379:6379
    healthcheck:
      test: ["CMD", "redis-cli", "ping"]
      interval: 5s
      timeout: 30s
      retries: 50
    restart: always

  airflow-webserver:
    <<: *airflow-common
    command: webserver
    ports:
      - 12080:8080
    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:
      - 12555:5555
    healthcheck:
      test: ["CMD", "curl", "--fail", "http://localhost:5555/"]
      interval: 10s
      timeout: 10s
      retries: 5
    restart: always

volumes:
  postgres-db-volume:

docker-compose up -d

"""Used for unit tests"""
from airflow import DAG
from airflow.operators.bash import BashOperator
from airflow.utils.dates import days_ago

with DAG(dag_id='test_utils', schedule_interval=None, tags=['example']) as dag:

    task = BashOperator(
        task_id='sleeps_forever',
        bash_command="sleep 10000000000",
        start_date=days_ago(2),
        owner='airflow',
    )