情况
- 自2022年9月19日发布Apache Airflow 2.4.0以来
- Airflow支持ExternalPythonOperator
- 我已经向主要贡献者提出了请求，并且应该能够将2个python虚拟环境添加到Airflow Docker 2.4.1的基本映像中，并能够在DAG内运行单个任务。
目标
- 我的目标是使用从本地requirements.txt构建的多个主机python虚拟环境。
- 使用ExternalPythonOperator运行它们
- 我的每个DAG都只执行定时python函数
我想请求
- 示例文件如何创建一个独立的有意识存在的python虚拟环境，通过基本的docker Airflow 2.4.1映像和：
- docker-compose.yml #最佳选项，因此我只需要在官方映像上使用docker-compose
- Dockerfile #第二好的选择，但因为我需要使用docker-compose.yml文件对官方映像进行一些处理，所以需要进行docker组合
系统
- 2.4.1可用的Docker映像。
- ubuntu 20.04 LTS
知识空白
- TIPS - https://github.com/apache/airflow/discussions/26783#discussioncomment-3766422
- 我已经查看了文档https://airflow.apache.org/docs/apache-airflow/stable/howto/operator/python.html#externalpythonoperator，了解这种情况下DAG的外观。但是我不知道如何添加python环境。
- DockerOperator - 我找不到任何可理解的资源
- KubernetesOperator - 我不需要kubernets，目前我的任何DAG都不在多个节点上运行。
- 我被推荐访问以下网站-> https://airflow.apache.org/docs/apache-airflow/stable/best-practices.html#handling-conflicting-complex-python-dependencies ->但这只是一个比较。我真正需要的是实用的全面实施指南。
我不想要这个
- 使用PythonVirtualenvOperator动态创建venvs。（已成功执行此操作，但我的DAG太轻或导入太多，因此不适合使用）
- 我有1个Python函数/DAG，因此我不需要这个 ->“请注意，虚拟环境是针对任务而不是DAG的。您目前无法解析DAG并在不同的虚拟环境中执行整个DAG - 您可以在其中执行单个Python *任务。将为“整个DAG”实现单独的运行时环境可能会在2.4或2.6中实现，作为https://cwiki.apache.org/confluence/display/AIRFLOW/AIP-46+Runtime+isolation+for+airflow+tasks+and+dag+parsing"的结果。
终端命令
docker build -t my-image-apache/airflow:2.4.1 .
之后我会运行以下命令，但第一步失败了。
docker-compose up
我的文件
docker-compose.yml
https://airflow.apache.org/docs/apache-airflow/2.4.1/docker-compose.yaml
这是一个关于IT技术的文件，其中包括了docker-compose.yml文件的链接。请点击链接以获取更多信息。# 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:2.4.1
# 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:-my-image-apache/airflow:2.4.1}
# 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: 'true'
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: $${min_airflow_version}. Only use this or higher!"
echo
exit 1
fi
if [[ -z "${AIRFLOW_UID}" ]]; then
echo
echo -e "\033[1;33mWARNING!!!: AIRFLOW_UID not set!\e[0m"
echo "If you are on Linux, you SHOULD follow the instructions below to set "
echo "AIRFLOW_UID environment variable, otherwise files will be owned by root."
echo "For other operating systems you can get rid of the warning with manually created .env file:"
echo " See: https://airflow.apache.org/docs/apache-airflow/stable/howto/docker-compose/index.html#setting-the-right-airflow-user"
echo
fi
one_meg=1048576
mem_available=$$(($$(getconf _PHYS_PAGES) * $$(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:
- .:/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:
Dockerfile（我混乱的整理）
FROM apache/airflow:2.4.1-python3.8
# https://pythonspeed.com/articles/activate-virtualenv-dockerfile/
ENV VIRTUAL_ENV=/opt/venv
RUN python3 -m venv $VIRTUAL_ENV
ENV PATH="$VIRTUAL_ENV/bin:$PATH"
# Install dependencies:
COPY requirements.txt .
RUN pip install -r requirements.txt
# Run the application:
# COPY myapp.py .
# CMD ["python", "myapp.py"]
# RUN python3 -m venv /path/to/new/virtual/environment_1 && \
# /path/to/new/virtual/environment_1/bin/python \
# -m pip install requirements.txt
# RUN python3 -m venv /path/to/new/virtual/environment_2 && \
# /path/to/new/virtual/environment_2/bin/python \
# -m pip install my_requirements_2.txt
错误
我之前在Dockerfile中有过Python环境，例如：
FROM python:3.9-slim-bullseye
RUN python3 -m venv /opt/venv
# Install dependencies:
COPY requirements.txt .
RUN . /opt/venv/bin/activate && pip install -r requirements.txt
# Run the application:
COPY myapp.py .
CMD . /opt/venv/bin/activate && exec python myapp.py
Dockerfile：但是在Airflow中它就无法正常工作。
FROM apache/airflow:2.4.1-python3.8
COPY requirements.txt .
RUN python3 -m venv /opt/airflow/virtual_1 && \
/opt/airflow/virtual_1/bin/python \
-m pip install requirements.txt
错误
=> ERROR [stage-1 2/2] RUN python3 -m venv /opt/airflow/virtual_1 && /opt/airflow/virtual_1/bin/python -m pip install requirements.txt
我尝试过的其他事情
1.)
FROM apache/airflow:2.4.1-python3.8
RUN python3 -m venv /opt/airflow
# Install dependencies:
COPY requirements.txt .
RUN /opt/airflow/venv/bin/pip install -r requirements.txt
命令 - docker build -t my-image-apache/airflow:2.4.1 .
错误
=> 错误 [4/4] 运行 /opt/airflow/venv/bin/pip install -r requirements.txt
2.)
FROM apache/airflow:2.4.1-python3.8
COPY requirements.txt .
RUN python3 -m venv && \
/venv/bin/python install -m pip requirements.txt
错误
=> 错误 [3/3] 运行 python3 -m venv && /venv/bin/python install -m pip requirements.txt