Airflow DAG 运行成功，但任务失败

问题描述 投票：0回答：1

我正在尝试在 Docker 上运行的 Airflow 上运行一个简单的 DAG。

我有两个 python 脚本，第一个脚本使用 API 调用获取数据，第二个脚本将数据推送到谷歌表格中。因此，我使用 

t1
执行第一个 Python 脚本，并使用 
t2
执行第二个。

这是我的代码：

from airflow import DAG
#from airflow.operators.python import PythonOperator, BashOperator 
from datetime import datetime, timedelta
from airflow.operators.bash_operator import BashOperator
from airflow.utils.dates import days_ago

default_args = {
    'owner': 'airflow',
    'depends_on_past': False,
    'email': ['[email protected]'],
    'email_on_failure': True,
    'email_on_retry': False,
    'retries': 1,
    'retry_delay': timedelta(minutes=1)}

with DAG("ETL_script",
        description= "DAG for Data retrieval",
        default_args = default_args,
        start_date= datetime(2022, 1, 1),
        schedule_interval= "@weekly", 
        catchup = False
        ) as dag:
    t1 = BashOperator(
            task_id = 'extract_data',
            bash_command = '/Users/user.name/Coding Projects/Python_Apps/Docker_Applications/Airflow_Docker_Apps/Python_Scripts/API_extract.py',
            dag=dag,)
    
    t2 = BashOperator(
        task_id = "Insert_into_google_Sheets",
        bash_command = 'python3 "/Users/user.name/Coding Projects/Python_Apps/Docker_Applications/Airflow_Docker_Apps/Python_Scripts/Google_Sheets_Connection.py"',
        dag=dag,)
    
    t1 >> t2 # Defining the task dependencies

当我在 

zsh
上运行 Bash 命令时，我可以完美运行两个 python 脚本，但在 Airflow 上，我收到此错误：

*** Reading local file: /opt/airflow/logs/ETL_script/extract_data/2022-05-02T21:27:56.923195+00:00/1.log
[2022-05-02, 21:27:58 UTC] {taskinstance.py:1043} INFO - Dependencies all met for <TaskInstance: ETL_script.extract_data manual__2022-05-02T21:27:56.923195+00:00 [queued]>
[2022-05-02, 21:27:58 UTC] {taskinstance.py:1043} INFO - Dependencies all met for <TaskInstance: ETL_script.extract_data manual__2022-05-02T21:27:56.923195+00:00 [queued]>
[2022-05-02, 21:27:58 UTC] {taskinstance.py:1249} INFO - 
--------------------------------------------------------------------------------
[2022-05-02, 21:27:58 UTC] {taskinstance.py:1250} INFO - Starting attempt 1 of 2
[2022-05-02, 21:27:58 UTC] {taskinstance.py:1251} INFO - 
--------------------------------------------------------------------------------
[2022-05-02, 21:27:58 UTC] {taskinstance.py:1270} INFO - Executing <Task(BashOperator): extract_data> on 2022-05-02 21:27:56.923195+00:00
[2022-05-02, 21:27:58 UTC] {standard_task_runner.py:52} INFO - Started process 148 to run task
[2022-05-02, 21:27:58 UTC] {standard_task_runner.py:79} INFO - Running: ['***', 'tasks', 'run', 'ETL_script', 'extract_data', 'manual__2022-05-02T21:27:56.923195+00:00', '--job-id', '134', '--raw', '--subdir', 'DAGS_FOLDER/ETL_script.py', '--cfg-path', '/tmp/tmpeaprux6w', '--error-file', '/tmp/tmp1hhf5ge7']
[2022-05-02, 21:27:58 UTC] {standard_task_runner.py:80} INFO - Job 134: Subtask extract_data
[2022-05-02, 21:27:58 UTC] {logging_mixin.py:109} INFO - Running <TaskInstance: ETL_script.extract_data manual__2022-05-02T21:27:56.923195+00:00 [running]> on host 4c9d01f48299
[2022-05-02, 21:27:58 UTC] {taskinstance.py:1448} INFO - Exporting the following env vars:
AIRFLOW_CTX_DAG_EMAIL=***@***.com
AIRFLOW_CTX_DAG_OWNER=***
AIRFLOW_CTX_DAG_ID=ETL_script
AIRFLOW_CTX_TASK_ID=extract_data
AIRFLOW_CTX_EXECUTION_DATE=2022-05-02T21:27:56.923195+00:00
AIRFLOW_CTX_DAG_RUN_ID=manual__2022-05-02T21:27:56.923195+00:00
[2022-05-02, 21:27:58 UTC] {subprocess.py:62} INFO - Tmp dir root location: 
 /tmp
[2022-05-02, 21:27:58 UTC] {subprocess.py:74} INFO - Running command: ['bash', '-c', '/Users/user.name/Coding Projects/Python_Apps/Docker_Applications/Airflow_Docker_Apps/Python_Scripts/API_extract.py']
[2022-05-02, 21:27:58 UTC] {subprocess.py:85} INFO - Output:
[2022-05-02, 21:27:58 UTC] {subprocess.py:89} INFO - bash: /Users/user.name/Coding: No such file or directory
[2022-05-02, 21:27:58 UTC] {subprocess.py:93} INFO - Command exited with return code 127
[2022-05-02, 21:27:58 UTC] {taskinstance.py:1774} ERROR - Task failed with exception
Traceback (most recent call last):
  File "/home/airflow/.local/lib/python3.7/site-packages/airflow/operators/bash.py", line 188, in execute
    f'Bash command failed. The command returned a non-zero exit code {result.exit_code}.'
airflow.exceptions.AirflowException: Bash command failed. The command returned a non-zero exit code 127.
[2022-05-02, 21:27:58 UTC] {taskinstance.py:1288} INFO - Marking task as UP_FOR_RETRY. dag_id=ETL_script, task_id=extract_data, execution_date=20220502T212756, start_date=20220502T212758, end_date=20220502T212758
[2022-05-02, 21:27:58 UTC] {standard_task_runner.py:98} ERROR - Failed to execute job 134 for task extract_data (Bash command failed. The command returned a non-zero exit code 127.; 148)
[2022-05-02, 21:27:58 UTC] {local_task_job.py:154} INFO - Task exited with return code 1
[2022-05-02, 21:27:58 UTC] {local_task_job.py:264} INFO - 0 downstream tasks scheduled from follow-on schedule check

因此，根据我的理解，第一个任务

t1
失败并给了我这个错误。我想我可能没有正确定义 
$PATH
，所以这给了我这个错误。这是我的第一个想法，但我不知道如何纠正。

其他信息

这是我的工作目录：

Docker_Applications
├───dags
│   ├───ETL_script.py
│   
│    
├───logs
├───plugins
├───Python_Scripts   
│   ├───API_extract.py
│   ├───Google_Sheets_Connection.py
│  
├───.env
├───docker-compose.yaml

我的

echo $PATH
是

Projects:/Library/Frameworks/Python.framework/Versions/3.10/bin:/usr/local/bin:/usr/bin:/bin:/usr/sbin:/sbin:/Library/Frameworks/Python.framework/Versions/3.10/bin

我的

docker-compose.yaml
文件看起来像：

# 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
#Documentation: https://airflow.apache.org/docs/apache-airflow/stable/start/docker.html#
#   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:2.2.5
# 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: '3.7'
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:2.2.5}
  # build: .
  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: 'false'
    AIRFLOW__API__AUTH_BACKEND: '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" $${1//./ }
        }
        airflow_version=$$(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:
      - .:/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:

注意：这个

yaml
文件与Airflow网页教程中的文件相同。请提出任何更改或最佳实践，因为我仍在学习！

非常感谢！！！！

python docker etl zsh airflow-2.x
1个回答
0
投票

正如您在日志中看到的，您在

line 23 ({subprocess.py:74})

中引入的路径 与试图用来在 
API_extract.py
中定位您的 
line 25 ({subprocess.py:89})
的气流不同，您可以尝试以下几项操作：

  • 在 
    bash_command
    中使用双引号而不是单引号，当前脚本在 
    Coding Projects
    的空格中分隔，可能会导致错误：

bash_command = "/Users/user.name/Coding Projects/Python_Apps/Docker_Applications/Airflow_Docker_Apps/Python_Scripts/API_extract.py",

  • 更改您的导入和任务：

调用 python 脚本的更好做法是导入模块并使用任务或使用 

PythonOperator
。 将脚本作为模块导入，然后从任务中调用它，而不是从 
BashOperator
中调用它，这对于您的情况下的 python 脚本来说是一个很好的做法，要执行此操作，您需要：

  1. 在导入标头中包含以下内容：

    Import sys
    sys.path.append(‘/Users/user.name/Coding Projects/Python_Apps/Docker_Applications/Airflow_Docker_Apps/Python_Scripts/’

import API_extract

import Google_Sheets_Connection

或者，您可以在 

docker-compose.yaml
中为您的 
PythonScripts
安装一个新卷（我主观上更喜欢此选项，以保持我的模块井井有条），在这种情况下，您通常可以直接从 
PythonScripts
导入模块，无需系统:

import API_extract
import Google_Sheets_Connection

请记住将相关的 

__init__.py
包含在您需要的目录中。 如果您选择后一个选项，您可以创建新的子目录，但您必须指定气流的路径，以用于您的目录：

├───Python_Scripts   
│       ├─── subdir
│               ├─── subdir2
│                        ├───API_extract.py

你会这样做：

import subdir.subdir2.API_extract

  1. 一旦修复了导入，您就可以重写您的任务：

    @task(task_id="extract_data")
def t1():
    api_extract.main() #the specific function you need actually

@task(task_id="Insert_into_google_Sheets")
def t2():
    api_extract.main()

考虑到 python 脚本的解析可能会产生额外的问题，您应该遵循相同的指令来导入模块并建立路径。 （例如，在我的例子中，只有当我使用调度程序运行任务时才会触发异常，覆盖我的路径并导致类似的错误）。 您可以在这里阅读更多内容：https://airflow.apache.org/docs/apache-airflow/stable/administration-and-deployment/modules_management.html

希望对你有帮助

最新问题
© www.soinside.com 2019 - 2024. All rights reserved.