如何自动将所有DAG文件移动到气流码头，而不仅仅是将最新添加的文件移动并重命名为“示例_python_operator”？

Question

问题

如何自动将所有DAG文件移动到气流码头(

• )，而不仅仅是将最新添加的文件移动
• 并将其重命名为

我试过的指南

1.

码头运行-d -p 8080:8080 -v -v puckel/ docker -airflow webserver

这是我的实际本地路径:/usr/local/airflow/dags

• Is
• -> :/usr/local/airflow/dags？
• 是一个标准的parth
• 这是the服务器的容器名还是内部路由puckel/docker-airflow webserver？码头运行-d -p 8080:8080 -v -v

2.

我不能把这个放到配置文件中，因为我使用的是坞-撰写，所以在第一次开始后我没有得到一个。dags_folder = /usr/local/airflow/dags

3.

这架飞机不会把我的DAG推到码头docker-compose up -d --build

4.添加了卷，但我仍然没有生成配置文件。1.

图片:puckel/docker-气流:1.10.0-2.卷：./ airflow :/usr/local/airflow 2.编辑气流配置文件中的dags文件夹配置(它在默认情况下不需要编辑，因为它位于气流文件夹下)。3.每次检查进程名是否通过以下命令出现: airflow list_dags

我的问题是如何自动添加它们。我不是每次都要为它写什么命令。另外，这些答案中的任何一个甚至都会将文件获取到文件夹。- Airflow in Docker: how to add DAGs to Airflow?

我所有的代码

命令

docker build -t my38 .
docker-compose up airflow-init
docker-compose up -d

文件

气流/码头文件

FROM apache/airflow:latest-python3.8
COPY requirements.txt .
RUN pip install -r requirements.txt

气流/要求

apache-airflow==2.4.0
pandas==1.4.2
numpy==1.20.3
pendulum==2.1.2

气流/船坞-合成物THE (这是官方的气流站点)

--- 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:latest-python3.8}   # build: .   environment:
    &airflow-common-env
    AIRFLOW__CORE__EXECUTOR: CeleryExecutor
    AIRFLOW__DATABASE__SQL_ALCHEMY_CONN: postgresql+psycopg2://airflow:airflow@postgres/airflow
    # For backward compatibility, with Airflow <2.3
    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: 'false'
    AIRFLOW__API__AUTH_BACKENDS: '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" $${1//./ }
        }
        airflow_version=$$(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/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}
      _PIP_ADDITIONAL_REQUIREMENTS: ''
    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

  # 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: 10s
      timeout: 10s
      retries: 5
    restart: always
    depends_on:
      <<: *airflow-common-depends-on
      airflow-init:
        condition: service_completed_successfully

volumes:   
    postgres-db-volume:

试图解决这个问题

1.

我已经尝试添加到x气流中的environment

• AIRFLOW__CORE__DAGS_FOLDER: /opt/airflow/dags

• I containers.

• Rebuilt

• 已经删除了我的所有图像，以及containers.

• Rebuilt所有的图像和对接器--将项目

• 进行了汇总，但它仍然没有工作。

Answer 1

我必须在实际的DAG文件中更改dag_id

dag_id="hello_world", # <--这必须是唯一的

前：

import pendulum

from airflow import DAG
from airflow.decorators import task

with DAG(
    dag_id="hello_world", ############################### <-- THIS HAVE TO BE UNIQUE
    schedule=None,
    start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
    catchup=False,
    tags=["example"],) as dag:

    @task()
    def print_array():
        """scipy."""
        from scipy.special import exp10   # <- THIS IS HOW IMPOTS SHOULD BE IMPORTED IN THIS CASE
        #define exp10 function and pass value in its
        exp = exp10([1,10])
        print(exp)

    print_array()

Answer 2

要点(1)：关于puckel/docker-airflow图像问题。

• :/usr/local/airflow/dags这是这个图像中DAGs的标准路径。

• webserver不是容器名称，它用于入口点，默认情况下，该图像中的入口点选项是webserver。

docker run -d -p 8080:8080 -v /LOCAL_PATH/dags:/usr/local/airflow/dags IMAGE_NAME COMMAND_ENTRYPOINT[OPTIONAL]

第(2)点：

为什么需要更改容器中的dags文件夹的路径?！但你可以通过

• 在使用docker-compose up运行容器之后，可以执行docker exec -it <CONTAINER ID> bash。然后在path /opt/airflow/下打开/opt/airflow/，您将需要像VIM这样的文本编辑器来编辑文件.

第(3)点：

• 检查安装在docker-复合文件中的volumes，应该是

。

    volumes:
      - LOCAL_PATH/dags:/opt/airflow/dags
      - LOCAL_PATH/logs:/opt/airflow/logs
      - LOCAL_PATH/plugins:/opt/airflow/plugins

my38

• 将

• 文件中的图像名称更改为新图像，在您的情况下，AIRFLOW_IMAGE_NAME将是AIRFLOW_IMAGE_NAME。

    image: ${AIRFLOW_IMAGE_NAME:-my38:latest}