在 Docker 上快速运行 Apache Airflow 2.2.4

Docker 安装 Apache Airflow

目录

• 参考资料
• 安装依赖
• 快速运行 Apache Airflow 2.2.4
  • 1. 下载 docker-compose.yaml
  • 2. 在 docker-compose.yaml 同级目录下创建文件夹
  • 3. 初始化环境
    • 3.1 设置正确的用户
    • 3.2 初始化数据库
  • 4. 运行 airflow
  • 5. 访问环境
    • 5.1 命令行
    • 5.2 浏览器访问
    • 5.3 给 REST API 发请求
  • 清除容器
• 清除环境信息

参考资料

• Running Airflow in Docker

安装依赖

1. Docker Engine
2. Docker Composite

快速运行 Apache Airflow 2.2.4

在 Docker 使用 CeleryExecutor（一种统计 worker 数量的途径） 快速运行 Apache Airflow

1. 下载 docker-compose.yaml

命令：

# 创建一个目录
mkdir -p /home/public/Soft/airflow
cd /home/public/Soft/airflow
# 下载 
curl -LfO 'https://airflow.apache.org/docs/apache-airflow/2.2.4/docker-compose.yaml'

这个文件包含了多个服务的定义：

• 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.
• flower - The flower app for monitoring the environment. It is available at http://localhost:5555.
• postgres - The database.
• redis - The redis - broker that forwards messages from scheduler to worker.

docker-compose.yaml 文件内容如下：

# 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:2.2.4
# AIRFLOW_UID                  - User ID in Airflow containers
#                                Default: 50000
# Those configurations are useful mostly in case of standalone testing/running Airflow in test/try-out mode
#
# _AIRFLOW_WWW_USER_USERNAME   - Username for the administrator account (if requested).
#                                Default: airflow
# _AIRFLOW_WWW_USER_PASSWORD   - Password for the administrator account (if requested).
#                                Default: airflow
# _PIP_ADDITIONAL_REQUIREMENTS - Additional PIP requirements to add when starting all containers.
#                                Default: ''
#
# Feel free to modify this file to suit your needs.
---
version: '3'
x-airflow-common:
  &airflow-common
  # In order to add custom dependencies or upgrade provider packages you can use your extended image.
  # Comment the image line, place your Dockerfile in the directory where you placed the docker-compose.yaml
  # and uncomment the "build" line below, Then run `docker-compose build` to build the images.
  image: ${AIRFLOW_IMAGE_NAME:-apache/airflow:2.2.4}
  # build: .
  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'
    _PIP_ADDITIONAL_REQUIREMENTS: ${_PIP_ADDITIONAL_REQUIREMENTS:-}
  volumes:
    - ./dags:/opt/airflow/dags
    - ./logs:/opt/airflow/logs
    - ./plugins:/opt/airflow/plugins
  user: "${AIRFLOW_UID:-50000}:0"
  depends_on:
    &airflow-common-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
    expose:
      - 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
    depends_on:
      <<: *airflow-common-depends-on
      airflow-init:
        condition: service_completed_successfully

  airflow-scheduler:
    <<: *airflow-common
    command: scheduler
    healthcheck:
      test: ["CMD-SHELL", 'airflow jobs check --job-type SchedulerJob --hostname "$${HOSTNAME}"']
      interval: 10s
      timeout: 10s
      retries: 5
    restart: always
    depends_on:
      <<: *airflow-common-depends-on
      airflow-init:
        condition: service_completed_successfully

  airflow-worker:
    <<: *airflow-common
    command: celery worker
    healthcheck:
      test:
        - "CMD-SHELL"
        - 'celery --app airflow.executors.celery_executor.app inspect ping -d "celery@$${HOSTNAME}"'
      interval: 10s
      timeout: 10s
      retries: 5
    environment:
      <<: *airflow-common-env
      # Required to handle warm shutdown of the celery workers properly
      # See https://airflow.apache.org/docs/docker-stack/entrypoint.html#signal-propagation
      DUMB_INIT_SETSID: "0"
    restart: always
    depends_on:
      <<: *airflow-common-depends-on
      airflow-init:
        condition: service_completed_successfully

  airflow-triggerer:
    <<: *airflow-common
    command: triggerer
    healthcheck:
      test: ["CMD-SHELL", 'airflow jobs check --job-type TriggererJob --hostname "$${HOSTNAME}"']
      interval: 10s
      timeout: 10s
      retries: 5
    restart: always
    depends_on:
      <<: *airflow-common-depends-on
      airflow-init:
        condition: service_completed_successfully

  airflow-init:
    <<: *airflow-common
    entrypoint: /bin/bash
    # yamllint disable rule:line-length
    command:
      - -c
      - |
        function ver() {
          printf "%04d%04d%04d%04d" $${1//./ }
        }
        airflow_version=$$(gosu airflow airflow version)
        airflow_version_comparable=$$(ver $${airflow_version})
        min_airflow_version=2.2.0
        min_airflow_version_comparable=$$(ver $${min_airflow_version})
        if (( airflow_version_comparable < min_airflow_version_comparable )); then
          echo
          echo -e "\033[1;31mERROR!!!: Too old Airflow version $${airflow_version}!\e[0m"
          echo "The minimum Airflow version supported: $${min_airflow_version}. Only use this or higher!"
          echo
          exit 1
        fi
        if [[ -z "${AIRFLOW_UID}" ]]; then
          echo
          echo -e "\033[1;33mWARNING!!!: AIRFLOW_UID not set!\e[0m"
          echo "If you are on Linux, you SHOULD follow the instructions below to set "
          echo "AIRFLOW_UID environment variable, otherwise files will be owned by root."
          echo "For other operating systems you can get rid of the warning with manually created .env file:"
          echo "    See: https://airflow.apache.org/docs/apache-airflow/stable/start/docker.html#setting-the-right-airflow-user"
          echo
        fi
        one_meg=1048576
        mem_available=$$(($$(getconf _PHYS_PAGES) * $$(getconf PAGE_SIZE) / one_meg))
        cpus_available=$$(grep -cE 'cpu[0-9]+' /proc/stat)
        disk_available=$$(df / | tail -1 | awk '{print $$4}')
        warning_resources="false"
        if (( mem_available < 4000 )) ; then
          echo
          echo -e "\033[1;33mWARNING!!!: Not enough memory available for Docker.\e[0m"
          echo "At least 4GB of memory required. You have $$(numfmt --to iec $$((mem_available * one_meg)))"
          echo
          warning_resources="true"
        fi
        if (( cpus_available < 2 )); then
          echo
          echo -e "\033[1;33mWARNING!!!: Not enough CPUS available for Docker.\e[0m"
          echo "At least 2 CPUs recommended. You have $${cpus_available}"
          echo
          warning_resources="true"
        fi
        if (( disk_available < one_meg * 10 )); then
          echo
          echo -e "\033[1;33mWARNING!!!: Not enough Disk space available for Docker.\e[0m"
          echo "At least 10 GBs recommended. You have $$(numfmt --to iec $$((disk_available * 1024 )))"
          echo
          warning_resources="true"
        fi
        if [[ $${warning_resources} == "true" ]]; then
          echo
          echo -e "\033[1;33mWARNING!!!: You have not enough resources to run Airflow (see above)!\e[0m"
          echo "Please follow the instructions to increase amount of resources available:"
          echo "   https://airflow.apache.org/docs/apache-airflow/stable/start/docker.html#before-you-begin"
          echo
        fi
        mkdir -p /sources/logs /sources/dags /sources/plugins
        chown -R "${AIRFLOW_UID}:0" /sources/{logs,dags,plugins}
        exec /entrypoint airflow version
    # yamllint enable rule:line-length
    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}
    user: "0:0"
    volumes:
      - .:/sources

  airflow-cli:
    <<: *airflow-common
    profiles:
      - debug
    environment:
      <<: *airflow-common-env
      CONNECTION_CHECK_MAX_COUNT: "0"
    # Workaround for entrypoint issue. See: https://github.com/apache/airflow/issues/16252
    command:
      - bash
      - -c
      - 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
    depends_on:
      <<: *airflow-common-depends-on
      airflow-init:
        condition: service_completed_successfully

volumes:
  postgres-db-volume:

2. 在 docker-compose.yaml 同级目录下创建文件夹

在 docker-compose.yaml 同级目录下，创建 dags logs plugins文件夹

cd /home/public/Soft/airflow
mkdir -p ./dags 
mkdir -p ./logs
mkdir -p ./plugins

dags logs plugins文件夹 作用：

• ./dags - you can put your DAG files here.
• ./logs - contains logs from task execution and scheduler.
• ./plugins - you can put your custom plugins here.

3. 初始化环境

初始化环境，就是添加几个文件夹。

3.1 设置正确的用户

命令：

cd /home/public/Soft/airflow
echo -e "AIRFLOW_UID=$(id -u)" > .env

其中，AIRFLOW_UID 是 Docker Compose 环境变量，具体请看（https://airflow.apache.org/docs/apache-airflow/2.2.4/start/docker.html#docker-compose-env-variables ）。

生成的 .env 文件内容可能如下：

AIRFLOW_UID=50000

3.2 初始化数据库

cd /home/public/Soft/airflow
docker-compose up airflow-init

控制台可能打印如下内容：

airflow-init_1       | Upgrades done
airflow-init_1       | Admin user airflow created
airflow-init_1       | 2.2.4
start_airflow-init_1 exited with code 0

初始化，默认的 Airflow 的登陆用户和密码 ： airflow airflow

4. 运行 airflow

cd /home/public/Soft/airflow
docker-compose up

5. 访问环境

有3中方式访问环境：命令行，浏览器访问，REST API。

5.1 命令行

下载 airflow.sh

curl -LfO 'https://airflow.apache.org/docs/apache-airflow/2.2.4/airflow.sh'
chmod +x airflow.sh

airflow.sh 脚本内容如下：

#!/usr/bin/env bash
# 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.

#
# Run airflow command in container
#

PROJECT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"

set -euo pipefail

export COMPOSE_FILE="${PROJECT_DIR}/docker-compose.yaml"
if [ $# -gt 0 ]; then
    exec docker-compose run --rm airflow-cli "${@}"
else
    exec docker-compose run --rm airflow-cli
fi

使用 airflow.sh 可以快速执行命令，例如：

arflow.sh info  

5.2 浏览器访问

浏览器访问 http://localhost:8080
默认登录名和密码： airflow airflow

5.3 给 REST API 发请求

使用 curl 发请求：

ENDPOINT_URL="http://localhost:8080/"
curl -X GET  \
    --user "airflow:airflow" \
    "${ENDPOINT_URL}/api/v1/pools"

清除容器

清除容器，卷等，命令如下：

docker-compose down --volumes --rmi all

清除环境信息

以上是快速启动配置，如果需要定制化配置，则可以先清除环境信息

1. 停止容器

cd /home/public/Soft/airflow
docker-compose down --volumes --remove-orphans

2. 删除下载目录和 docker-compose.yaml

cd /home/public/Soft/airflow
rm -rf *

3. 重新下载 docker-compose.yaml

curl -LfO 'https://airflow.apache.org/docs/apache-airflow/2.2.4/docker-compose.yaml'

4. 从开头重新执行指令