docker - Airflow Docker AWS EC2 DAG文件日志错误

包含错误的日志

我在运行简单的 dag 后得到了这个

241adsgf1108 *** Log file does not exist: /opt/airflow/logs/dag_id=numpy_pandas/run_id=manual__2022-09-27T16:31:22.968544+00:00/task_id=print_the_context/attempt=1.log *** Fetching from: http://241adsgf1108:8793/dag_id=numpy_pandas/run_id=manual__2022-09-27T16:31:22.968544+00:00/task_id=print_the_context/attempt=1.log *** !!!! Please make sure that all your Airflow components (e.g. schedulers, webservers and workers) have the same 'secret_key' configured in 'webserver' section and time is synchronized on all your machines (for example with ntpd) !!!!! ****** See more at https://airflow.apache.org/docs/apache-airflow/stable/configurations-ref.html#secret-key ****** Failed to fetch log file from worker. Client error '403 FORBIDDEN' for url 'http://241adsgf1108:8793/dag_id=numpy_pandas/run_id=manual__2022-09-27T16:31:22.968544+00:00/task_id=print_the_context/attempt=1.log' For more information check: https://httpstatuses.com/403

文件

我的 dag 文件

import pendulum from airflow import DAG #from airflow.decorators import task from airflow.operators.python import PythonOperator with DAG( dag_id="numpy_pandas", schedule=None, start_date=pendulum.datetime(2021, 1, 1, tz="UTC"), catchup=False, tags=["example"],) as dag: def numpy_something(): """Print Numpy array.""" import numpy as np # <- THIS IS HOW NUMPY SHOULD BE IMPORTED IN THIS CASE import pandas as pd d = {'col1': [1, 2], 'col2': [3, 4]} df = pd.DataFrame(data=d) print(df) a = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]]) print(a) #a= 10 return a run_this = PythonOperator( task_id="print_the_context", python_callable=numpy_something, )

dockerfile

FROM apache/airflow:latest-python3.8 COPY requirements.txt . COPY personal_python_file.py /usr/local/airflow/dags/personal_python_file.py RUN pip install -r requirements.txt

docker-compose.yml

--- 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:latest-python3.8} ### SAME AS IN MY DOCKERFILE "FROM apache/airflow:latest-python3.8" BUT I CAN MODIFY IT AS THE IAMGE THAT I GNERATE IS THE IMPORTANT ONE # my-image-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: ${_PIP_ADDITIONAL_REQUIREMENTS:-} # I have added these AIRFLOW__CORE__ENABLE_XCOM_PICKLING: 'true' #AIRFLOW__CORE__DAGS_FOLDER: /opt/airflow/dags #connect local and docker conatiner DAGS folers #AIRFLOW__CORE__DAGS_FOLDER: ./dags # AIRFLOW__CORE__PLUGINS_FOLDER: /opt/airflow/plugins # AIRFLOW__CORE__LOGGING_CONFIG_CLASS: airflow.utils.log.logging_config.DEFAULT_LOGGING_CONFIG # AIRFLOW__CORE__LOGGING_LEVEL: INFO volumes: - ./dags/:/opt/airflow/dags - ./logs/:/opt/airflow/logs - ./plugins/:/opt/airflow/plugins user: "${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 stdin_open: true tty: true depends_on: <<: *airflow-common-depends-on airflow-init: condition: service_completed_successfully volumes: - ./airflow:/usr/local/airflow 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:

已经尝试过：