openpyxl ImportError in Airflow docker 使用pd.read_excel()時

Question

在容器內的 airflow 任務中使用 pandas pd.read_excel() 時，出現以下 openpyxl 錯誤。 我嘗試使用詩歌安裝 openpyxl，甚至在 dockerfile 中使用 pip 但沒有成功。

  File "/home/airflow/.local/lib/python3.8/site-packages/pandas/io/excel/_openpyxl.py", line 521, in __init__
    import_optional_dependency("openpyxl")
  File "/home/airflow/.local/lib/python3.8/site-packages/pandas/compat/_optional.py", line 118, in import_optional_dependency
    raise ImportError(msg) from None
ImportError: Missing optional dependency 'openpyxl'.  Use pip or conda to install openpyxl.

這里是詩歌 toml 中的版本

[tool.poetry.dependencies]
python = ">=3.8, <3.11"
pandas = "^1.3.3"
apache-airflow = "2.2.4"
openpyxl = "^3.0.9"

此處報告了類似的最新問題： https://dockerquestions.com/2022/03/02/docker-compose-airflow-no-error-during-build-missing-python-package-in-worker-container/任何建議？

docker-compose.yaml

（不要忘記 create.env，請看下面的評論）

version: '3'
x-airflow-common:
  &airflow-common
  build:
    dockerfile: ./docker/airflow.dockerfile
    context: .
  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: ${_PIP_ADDITIONAL_REQUIREMENTS:-}
  volumes:
    - ./airflow/dags:/opt/airflow/dags  # laptop_folder:container_folder sync
    - ./airflow/logs:/opt/airflow/logs
    - ./airflow/plugins:/opt/airflow/plugins
    - ./airflow/scheduler:/opt/airflow/scheduler
  user: "${AIRFLOW_UID:-50000}:${AIRFLOW_GID:-50000}" # echo -e "AIRFLOW_UID=$(id -u)\nAIRFLOW_GID=0" > .env
  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:
      - ./airflow/:/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:

帶有 airflow.dockerfile 的 docker 文件夾

FROM apache/airflow:2.2.5-python3.8

# use root for settings
USER root

ARG YOUR_ENV="virtualenv"

ENV YOUR_ENV=${YOUR_ENV} \
    PYTHONPATH="/opt/" \
    # PYTHONPATH="/opt/" -> python path to airflow-common volumes
    PYTHONFAULTHANDLER=1 \
    PYTHONUNBUFFERED=1 \
    PYTHONHASHSEED=random \
    PIP_NO_CACHE_DIR=off \
    PIP_DISABLE_PIP_VERSION_CHECK=on \
    PIP_DEFAULT_TIMEOUT=100 \
    LC_ALL=C.UTF-8 \
    LANG=C.UTF-8

# linux libs 
RUN apt-get update \
    && apt-get install -y --no-install-recommends \
    gcc curl libpq-dev \ 
    && pip3 install openpyxl pandas apache-airflow \
    && apt-get autoremove -yqq --purge \
    && apt-get clean \
    && rm -rf /var/lib/apt/lists/* 

# use airflow for r/w files
USER airflow

dags文件夾下的py文件

import os
import pandas as pd
from datetime import datetime, timedelta

from airflow import DAG

from airflow.operators.python import PythonOperator
from airflow.operators.dummy import DummyOperator



# pipeline setup
dag = DAG(
    'pipeline_etl',
    start_date=datetime.now(),
    schedule_interval='@daily',
    catchup=False,
)

URL = "https://archive.ics.uci.edu/ml/machine-learning-databases/00352/Online%20Retail.xlsx"

def read_xls():
    df = pd.read_excel(URL,nrows=1000)
    return df


run_python_report = PythonOperator(
    task_id='python_report', python_callable=read_xls, dag=dag
)


start_op = DummyOperator(task_id='start_task', dag=dag)
end_op = DummyOperator(task_id='end_task', dag=dag)

start_op >> run_python_report >> end_op

Answer 1

跳入容器內部並手動安裝

獲取 airflow 服務器容器 ID：

$ docker ps

Go 集裝箱內：

$ docker exec -it <id> bash

最后安裝 package：

$ pip install openpyxl