Situation

• Since 2022 Sept 19 The release of Apache Airflow 2.4.0
• Airflow supports ExternalPythonOperator
• I have asked the main contributors as well and I should be able to add 2 python virtual environments to the base image of Airflow Docker 2.4.1 and be able to rune single tasks inside a DAG.

Goal

• My goal is to use multiple host python virtualenvs that built from a local requirements.txt.
• using ExternalPythonOperator to run them
• Each of my dags just execute a timed python function

I would like to request

• Example files how to create a separate consciously existing python virtual environments, built via the base docker Airflow 2.4.1 image and the:
  • docker-compose.yml #best option so I only need to use docker-compose on the official image
  • Dockerfile # second best option but because I need to docker compose the official image with some of my takes on the docker-compose.yml file

System

• 2.4.1 Docker image that works.
• ubuntu 20.04 LTS

Knowledge gaps

• TIPS – https://github.com/apache/airflow/discussions/26783#discussioncomment-3766422
• I have seen the documentation https://airflow.apache.org/docs/apache-airflow/stable/howto/operator/python.html#externalpythonoperator on how the DAG going to look like in this case. But I don’t know how to add the python environemnt.
• DockerOperator – I cant find any understandable resources
• KubernetesOperator – I don’t need kubernets, non of my dags runs on multiple nodes currently.
• I was recommend the following site -> https://airflow.apache.org/docs/apache-airflow/stable/best-practices.html#handling-conflicting-complex-python-dependencies -> but this is just a comparison. What I realy need is practical full on implementation guides.

I don’t want this

• PythonVirtualenvOperator to create those venvs dynamically. (Successfully performed this, but I have too light weight dags or too many import one so it is not ideal to use)
• I have 1 python function / DAG so it is nine I don’t need this -> "Note that te virtualenvs are per task not per DAGs. You cannot (for now) parse your DAGs and execute whole dags in different virtualenv – you can execute individual Python* tasks in those. Separate runtime environment for "whole DAGs" will likely be implemented in 2.4 or 2.6 as result of https://cwiki.apache.org/confluence/display/AIRFLOW/AIP-46+Runtime+isolation+for+airflow+tasks+and+dag+parsing"

Terminal Commands

docker build -t my-image-apache/airflow:2.4.1 .

I would run a the following command afterwards but the 1st step fails

docker-compose up

My Files

docker-compose.yml

https://airflow.apache.org/docs/apache-airflow/2.4.1/docker-compose.yaml

# 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 "33[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 "33[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 "33[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 "33[1;33mWARNING!!!: Not enough CPUS available for Docker.e[0m"
          echo "At least 2 CPUs recommended. You have $${cpus_available}"
          echo
          warning_resources="true"
        fi
        if (( disk_available < one_meg * 10 )); then
          echo
          echo -e "33[1;33mWARNING!!!: Not enough Disk space available for Docker.e[0m"
          echo "At least 10 GBs recommended. You have $$(numfmt --to iec $$((disk_available * 1024 )))"
          echo
          warning_resources="true"
        fi
        if [[ $${warning_resources} == "true" ]]; then
          echo
          echo -e "33[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:

Dockrfile (all the mess that I have tied)

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

ERRORs

I have had python envs in docker before Dockerfile ex.:

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: but with airflow it just doesn’t works:

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

ERROR

 => ERROR [stage-1 2/2] RUN python3 -m venv /opt/airflow/virtual_1 && /opt/airflow/virtual_1/bin/python -m pip install requirements.txt 

other thing I have tried

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

command – docker build -t my-image-apache/airflow:2.4.1 .

error
=> ERROR [4/4] RUN /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

error
=> ERROR [3/3] RUN python3 -m venv && /venv/bin/python install -m pip requirements.txt