apache airflow docker 运行简单试用

airflow 是一个编排、调度和监控workflow的平台，由Airbnb开源，现在在Apache Software Foundation 孵化。
airflow 将workflow编排为tasks组成的DAGs，调度器在一组workers上按照指定的依赖关系执行tasks。同时，
airflow 提供了丰富的命令行工具和简单易用的用户界面以便用户查看和操作，并且airflow提供了监控和报警
系统

测试运行环境使用docker

基本安装

docker安装

使用别人已经构建好的 puckel/docker-airflow

或者使用pip 安装

pip install apache-airflow

简单测试&&运行

docker-compose

local 运行：

version: '2.1'
services:
    postgres:
        image: postgres:9.6
        environment:
            - POSTGRES_USER=airflow
            - POSTGRES_PASSWORD=airflow
            - POSTGRES_DB=airflow
        ports:
        - "5432:5432"

    webserver:
        image: puckel/docker-airflow:1.10.0-2
        depends_on:
            - postgres
        environment:
            - LOAD_EX=n
            - EXECUTOR=Local
        volumes:
            - ./dags:/usr/local/airflow/dags
            # Uncomment to include custom plugins
            # - ./plugins:/usr/local/airflow/plugins
        ports:
            - "8080:8080"
        command: webserver
        healthcheck:
            test: ["CMD-SHELL", "[ -f /usr/local/airflow/airflow-webserver.pid ]"]
            interval: 30s
            timeout: 30s
            retries: 3

Celery 运行：
version: '2.1'
services:
    redis:
        image: 'redis:3.2.7'
        # command: redis-server --requirepass redispass

    postgres:
        image: postgres:9.6
        environment:
            - POSTGRES_USER=airflow
            - POSTGRES_PASSWORD=airflow
            - POSTGRES_DB=airflow
        # Uncomment these lines to persist data on the local filesystem.
        # - PGDATA=/var/lib/postgresql/data/pgdata
        # volumes:
        # - ./pgdata:/var/lib/postgresql/data/pgdata

    webserver:
        image: puckel/docker-airflow:1.10.0-2
        restart: always
        depends_on:
            - postgres
            - redis
        environment:
            - LOAD_EX=n
            - FERNET_KEY=46BKJoQYlPPOexq0OhDZnIlNepKFf87WFwLbfzqDDho=
            - EXECUTOR=Celery
            # - POSTGRES_USER=airflow
            # - POSTGRES_PASSWORD=airflow
            # - POSTGRES_DB=airflow
            # - REDIS_PASSWORD=redispass
        volumes:
            - ./dags:/usr/local/airflow/dags
            # Uncomment to include custom plugins
            # - ./plugins:/usr/local/airflow/plugins
        ports:
            - "8080:8080"
        command: webserver
        healthcheck:
            test: ["CMD-SHELL", "[ -f /usr/local/airflow/airflow-webserver.pid ]"]
            interval: 30s
            timeout: 30s
            retries: 3

    flower:
        image: puckel/docker-airflow:1.10.0-2
        restart: always
        depends_on:
            - redis
        environment:
            - EXECUTOR=Celery
            # - REDIS_PASSWORD=redispass
        ports:
            - "5555:5555"
        command: flower

    scheduler:
        image: puckel/docker-airflow:1.10.0-2
        restart: always
        depends_on:
            - webserver
        volumes:
            - ./dags:/usr/local/airflow/dags
            # Uncomment to include custom plugins
            # - ./plugins:/usr/local/airflow/plugins
        environment:
            - LOAD_EX=n
            - FERNET_KEY=46BKJoQYlPPOexq0OhDZnIlNepKFf87WFwLbfzqDDho=
            - EXECUTOR=Celery
            # - POSTGRES_USER=airflow
            # - POSTGRES_PASSWORD=airflow
            # - POSTGRES_DB=airflow
            # - REDIS_PASSWORD=redispass
        command: scheduler

    worker:
        image: puckel/docker-airflow:1.10.0-2
        restart: always
        depends_on:
            - scheduler
        volumes:
            - ./dags:/usr/local/airflow/dags
            # Uncomment to include custom plugins
            # - ./plugins:/usr/local/airflow/plugins
        environment:
            - FERNET_KEY=46BKJoQYlPPOexq0OhDZnIlNepKFf87WFwLbfzqDDho=
            - EXECUTOR=Celery
            # - POSTGRES_USER=airflow
            # - POSTGRES_PASSWORD=airflow
            # - POSTGRES_DB=airflow
            # - REDIS_PASSWORD=redispass
        command: worker

简单flow

"""
Code that goes along with the Airflow located at:
http://airflow.readthedocs.org/en/latest/tutorial.html
"""
from airflow import DAG
from airflow.operators.bash_operator import BashOperator
from datetime import datetime, timedelta


default_args = {
    "owner": "airflow",
    "depends_on_past": False,
    "start_date": datetime(2015, 6, 1),
    "email": ["airflow@airflow.com"],
    "email_on_failure": False,
    "email_on_retry": False,
    "retries": 1,
    "retry_delay": timedelta(minutes=5),
    # 'queue': 'bash_queue',
    # 'pool': 'backfill',
    # 'priority_weight': 10,
    # 'end_date': datetime(2016, 1, 1),
}

dag = DAG("tutorial", default_args=default_args, schedule_interval=timedelta(1))

# t1, t2 and t3 are examples of tasks created by instantiating operators
t1 = BashOperator(task_id="print_date", bash_command="date", dag=dag)

t2 = BashOperator(task_id="sleep", bash_command="sleep 5", retries=3, dag=dag)

templated_command = """
    {% for i in range(5) %}
        echo "{{ ds }}"
        echo "{{ macros.ds_add(ds, 7)}}"
        echo "{{ params.my_param }}"
    {% endfor %}
"""

t3 = BashOperator(
    task_id="templated",
    bash_command=templated_command,
    params={"my_param": "Parameter I passed in"},
    dag=dag,
)

t2.set_upstream(t1)
t3.set_upstream(t1)