Airflow docker cluster 구성 - 1

2024.02.09 - [Airflow] - Airflow docker cluster 구성 - 2

Airflow docker cluster 구성 - 2

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• Airflow 버전 : 2.8.1
• Ubuntu 22.04 LTS
• Airflow Executor : CeleryExecutor

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Airflow docker cluster 구성도

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• 설치(마스터 노드) - 10.10.x.x

# 도커 설치 및 경로 변경
curl -fsSL https://get.docker.com | sh

# 도커 정보 확인 : client - server, Docker Root Dir, Registry
docker info

# 도커 정보 확인 : Docker Engine - Community
docker version

# 도커 프로세스 내리기
sudo systemctl stop docker.service
sudo systemctl stop docker.socket
sudo systemctl status docker.service
sudo systemctl status docker.socket

# 폴더 복사
cd /var/lib
sudo cp -av docker/ /data/

# 도커 폴더 변경
cat <<EOT>> /etc/docker/daemon.json
{
        "data-root": "/data/docker"
}
EOT

# 재시작 후 확인
sudo systemctl start docker
sudo systemctl status docker.service
sudo systemctl status docker.socket

# 변경된 디렉터리 확인
sudo docker info | grep "Docker Root Dir"
Docker Root Dir: /data/docker
---------------------------------------------------
#설치
sudo su -
mkdir /data/airflow_master /data/airflow_master/dags /data/airflow_master/logs /data/airflow_master/plugins
sudo chmod 755 /data/airflow_master/dags /data/airflow_master/logs /data/airflow_master/plugins
sudo chown root:root /data/airflow_master/dags /data/airflow_master/logs /data/airflow_master/plugins

cd /data/airflow_master
curl -LfO 'https://airflow.apache.org/docs/apache-airflow/2.8.1/docker-compose.yaml'

# docker-compose 실행
cd /data/airflow_master
docker compose up airflow-init
docker compose up -d
docker compose up -d flower

# 확인
docker ps
CONTAINER ID   IMAGE                  COMMAND                  CREATED          STATUS                    PORTS                                                 NAMES
8be9d6084cec   apache/airflow:2.8.1   "/usr/bin/dumb-init …"   23 minutes ago   Up 23 minutes (healthy)   0.0.0.0:5555->5555/tcp, :::5555->5555/tcp, 8080/tcp   airflow_master-flower-1
40b1546dbf41   apache/airflow:2.8.1   "/usr/bin/dumb-init …"   24 minutes ago   Up 23 minutes (healthy)   8080/tcp                                              airflow_master-airflow-triggerer-1
e91be32295ad   apache/airflow:2.8.1   "/usr/bin/dumb-init …"   24 minutes ago   Up 23 minutes (healthy)   8080/tcp                                              airflow_master-airflow-scheduler-1
06e6471e8c91   apache/airflow:2.8.1   "/usr/bin/dumb-init …"   24 minutes ago   Up 23 minutes (healthy)   8080/tcp                                              airflow_master-airflow-worker-1
96363982f50e   apache/airflow:2.8.1   "/usr/bin/dumb-init …"   24 minutes ago   Up 23 minutes (healthy)   0.0.0.0:8080->8080/tcp, :::8080->8080/tcp             airflow_master-airflow-webserver-1
d93dda0235fa   postgres:13            "docker-entrypoint.s…"   24 minutes ago   Up 23 minutes (healthy)   5432/tcp                                              airflow_master-postgres-1
dd32b9310702   redis:latest           "docker-entrypoint.s…"   24 minutes ago   Up 23 minutes (healthy)   0.0.0.0:6379->6379/tcp, :::6379->6379/tcp             airflow_master-redis-1

# FERNET_KEY 및 SECRET_KEY 생성
docker exec -it airflow_master-airflow-scheduler-1 bash
python
>>> from cryptography.fernet import Fernet 
>>> FERNET_KEY = Fernet.generate_key().decode() 
>>> print(FERNET_KEY)
pZ0i_vrVtawk.....................

python
>>> import os 
>>> print(os.urandom(16))
b'\xef\x8a.....................


# docker-compose.yaml 수정(하단 docker-compose.yaml 참고)
cp -pv docker-compose.yaml docker-compose.yaml_bak
vi docker-compose.yml

# .env 생성
vi .env
chmod 755 .env

# 획득한 FERNET_KEY와 SECRET_KEY를 위의 docker-compose.yaml의 AIRFLOW__CORE__FERNET_KEYY에 집어 넣습니다.
cat docker-compose.yaml | grep AIRFLOW__CORE__FERNET_KEY
AIRFLOW__CORE__FERNET_KEY: 'pZ0i_vrVtawkrzgc2nJU8mJvz1R..............'
AIRFLOW__WEBSERVER__SECRET_KEY : b'\xef\x8a\xc7\xc4\xe2\x8c............'

# 컨테이너에 적용
docker compose up --build --force-recreate -d airflow-init
docker compose up --build --force-recreate -d
docker compose up --build --force-recreate -d flower

# 종료
docker compose down

• docker-compose.yaml 내용

x-airflow-common:
  &airflow-common
  image: ${AIRFLOW_IMAGE_NAME:-apache/airflow:2.8.1}
  # build: .
  environment:
    &airflow-common-env
    AIRFLOW__CORE__EXECUTOR: CeleryExecutor
    AIRFLOW__DATABASE__SQL_ALCHEMY_CONN: postgresql+psycopg2://postgres:postgres@IP:5432/airflow_dev_vm
    AIRFLOW__CORE__SQL_ALCHEMY_CONN: postgresql+psycopg2://postgres:postgres@IP:5432/airflow_dev_vm
    AIRFLOW__CELERY__RESULT_BACKEND: db+postgresql://postgres:postgres@IP:5432/airflow_dev_vm
    AIRFLOW__CELERY__BROKER_URL: redis://:@redis:6379/0
    AIRFLOW__CORE__FERNET_KEY: 'KEY'
    AIRFLOW__WEBSERVER__SECRET_KEY : KEY
    AIRFLOW__CORE__DAGS_ARE_PAUSED_AT_CREATION: 'true'
    AIRFLOW__CORE__LOAD_EXAMPLES: 'false'
    AIRFLOW__API__AUTH_BACKENDS: 'airflow.api.auth.backend.basic_auth,airflow.api.auth.backend.session'
    _PIP_ADDITIONAL_REQUIREMENTS: ${_PIP_ADDITIONAL_REQUIREMENTS:- dbt-trino apache-airflow-providers-apache-spark airflow-code-editor apache-airflow-providers-apache-kafka}
    AIRFLOW__CORE__DEFAULT_TIMEZONE: 'Asia/Seoul'
    AIRFLOW__WEBSERVER__BASE_URL: 'http://IP:8080'
    AIRFLOW__SCHEDULER__ENABLE_HEALTH_CHECK: 'true'
  extra_hosts:
    - "worker1:IP"
    - "worker2:IP"
    - "hadoop-master1:IP"
    - "hadoop-master2:IP"
    - "hadoop-new-slave1:IP"
    - "hadoop-new-slave2:IP"
    - "hadoop-new-slave3:IP"
    - "hadoop-new-slave4:IP"
    - "hadoop-new-slave5:IP"
    - "hadoop-zookeeper1:IP"
    - "dev-hadoop-master1:IP"
    - "dev-hadoop-master2:IP"
    - "dev-hadoop-slave1:IP"
    - "dev-hadoop-slave2:IP"
    - "dev-hadoop-slave3:IP"
  volumes:
    - ${AIRFLOW_PROJ_DIR:-.}/dags:/opt/airflow/dags
    - ${AIRFLOW_PROJ_DIR:-.}/logs:/opt/airflow/logs
    - ${AIRFLOW_PROJ_DIR:-.}/plugins:/opt/airflow/plugins
    - ${AIRFLOW_PROJ_DIR:-.}/scripts:/opt/airflow/scripts
    - ${AIRFLOW_PROJ_DIR:-.}/build_sh:/opt/airflow/build_sh
    - ${AIRFLOW_PROJ_DIR:-.}/envs:/opt/airflow/envs
  user: "${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: postgres
      POSTGRES_PASSWORD: postgres
      POSTGRES_DB: airflow_dev_vm
    volumes:
      - postgres-db-volume:/var/lib/postgresql/data
    healthcheck:
      test: ["CMD", "pg_isready", "-U", "postgres"]
      interval: 10s
      retries: 5
      start_period: 5s
    restart: always

  redis:
    image: redis:latest
    ports:
      - "6379:6379"
    expose:
      - 6379
    healthcheck:
      test: [ "CMD", "redis-cli", "ping" ]
      interval: 10s
      timeout: 30s
      retries: 50
      start_period: 30s
    restart: always

  airflow-webserver:
    <<: *airflow-common
    command: webserver
    ports:
      - "8080:8080"
    healthcheck:
      test: [ "CMD", "curl", "--fail", "http://localhost:8080/health" ]
      interval: 30s
      timeout: 10s
      retries: 5
      start_period: 30s
    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: 30s
      timeout: 10s
      retries: 5
      start_period: 30s
    restart: always
    depends_on:
      <<: *airflow-common-depends-on
      airflow-init:
        condition: service_completed_successfully

  airflow-worker:
    <<: *airflow-common
    #build: .
    hostname: master
    command: celery worker
    healthcheck:
      test:
        - "CMD-SHELL"
        - 'celery --app airflow.executors.celery_executor.app inspect ping -d "celery@$${HOSTNAME}"'
      interval: 30s
      timeout: 10s
      retries: 5
      start_period: 30s
    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: "1"
    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: 30s
      timeout: 10s
      retries: 5
      start_period: 30s
    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" $${1//./ }
        }
        airflow_version=$$(AIRFLOW__LOGGING__LOGGING_LEVEL=INFO && 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/howto/docker-compose/index.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/howto/docker-compose/index.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}
      _PIP_ADDITIONAL_REQUIREMENTS: ''
    user: "0:0"
    volumes:
      - ${AIRFLOW_PROJ_DIR:-.}:/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

  # You can enable flower by adding "--profile flower" option e.g. docker-compose --profile flower up
  # or by explicitly targeted on the command line e.g. docker-compose up flower.
  # See: https://docs.docker.com/compose/profiles/
  flower:
    <<: *airflow-common
    command: celery flower
    profiles:
      - flower
    ports:
      - "5555:5555"
    healthcheck:
      test: [ "CMD", "curl", "--fail", "http://localhost:5555/" ]
      interval: 30s
      timeout: 10s
      retries: 5
      start_period: 30s
    restart: always
    depends_on:
      <<: *airflow-common-depends-on
      airflow-init:
        condition: service_completed_successfully

volumes:
  postgres-db-volume:

• .env 내용

AIRFLOW_UID=50000
FERNET_KEY=key값
SECRET_KEY=key값
HOSTNAME=master