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Bug fix Airflow Docker pip requirements.txt numpy DAG fails

Time：09-27

GOAL

The goal would be to get a very simple Airflow Docker where I just want to install a few very basic pip packages from requirements.txt . Tan run a python script that contains numpy and pandas parts.

COMMANDS

docker build -t my38 .
docker-compose up airflow-init
docker-compose up -d

FILES

airflow/Dockerfile

FROM apache/airflow:latest-python3.8
COPY requirements.txt .
RUN pip install -r requirements.txt

airflow/requirements.txt

apache-airflow==2.4.0
pandas==1.4.2
numpy==1.20.3
pendulum==2.1.2

airflow/docker-compose.yml (THIS IS FROM THE OFFICIAL AIRFLOW SITE)

--- 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:-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: ${_PIP_ADDITIONAL_REQUIREMENTS:-}   volumes:
    - ./dags:/opt/airflow/dags
    - ./logs:/opt/airflow/logs
    - ./plugins:/opt/airflow/plugins   user: "${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 "dddd" $${1//./ }
        }
        airflow_version=$$(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:

airflow/dag/test_np.py (THIS IS FROM THE OFFICIAL AIRFLOW SITE)

import pendulum

from airflow import DAG
from airflow.decorators import task

with DAG(
    dag_id="example_python_operator",
    schedule=None,
    start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
    catchup=False,
    tags=["example"],) as dag:

    @task()
    def print_array():
        """Print Numpy array."""
        import numpy as np  # <- THIS IS HOW NUMPY SHOULD BE IMPORTED IN THIS CASE

        a = np.arange(15).reshape(3, 5)
        print(a)
        return a

    print_array()

OUTPUT LOG

** Reading local file: /opt/airflow/logs/dag_id=example_python_operator/run_id=manual__2022-09-23T17:31:35.153420 00:00/task_id=print_array/attempt=1.log
[2022-09-23, 18:31:35 BST] {taskinstance.py:1165} INFO - Dependencies all met for <TaskInstance: example_python_operator.print_array manual__2022-09-23T17:31:35.153420 00:00 [queued]>
[2022-09-23, 18:31:35 BST] {taskinstance.py:1165} INFO - Dependencies all met for <TaskInstance: example_python_operator.print_array manual__2022-09-23T17:31:35.153420 00:00 [queued]>
[2022-09-23, 18:31:35 BST] {taskinstance.py:1362} INFO - 
--------------------------------------------------------------------------------
[2022-09-23, 18:31:35 BST] {taskinstance.py:1363} INFO - Starting attempt 1 of 1
[2022-09-23, 18:31:35 BST] {taskinstance.py:1364} INFO - 
--------------------------------------------------------------------------------
[2022-09-23, 18:31:35 BST] {taskinstance.py:1383} INFO - Executing <Task(_PythonDecoratedOperator): print_array> on 2022-09-23 17:31:35.153420 00:00
[2022-09-23, 18:31:35 BST] {standard_task_runner.py:54} INFO - Started process 75 to run task
[2022-09-23, 18:31:35 BST] {standard_task_runner.py:82} INFO - Running: ['***', 'tasks', 'run', 'example_python_operator', 'print_array', 'manual__2022-09-23T17:31:35.153420 00:00', '--job-id', '59', '--raw', '--subdir', 'DAGS_FOLDER/test_np.py', '--cfg-path', '/tmp/tmpyjtdxl2p']
[2022-09-23, 18:31:35 BST] {standard_task_runner.py:83} INFO - Job 59: Subtask print_array
[2022-09-23, 18:31:35 BST] {dagbag.py:525} INFO - Filling up the DagBag from /opt/***/dags/test_np.py
[2022-09-23, 18:31:35 BST] {task_command.py:384} INFO - Running <TaskInstance: example_python_operator.print_array manual__2022-09-23T17:31:35.153420 00:00 [running]> on host c1db16ac8cf8
[2022-09-23, 18:31:35 BST] {taskinstance.py:1590} INFO - Exporting the following env vars:
AIRFLOW_CTX_DAG_OWNER=***
AIRFLOW_CTX_DAG_ID=example_python_operator
AIRFLOW_CTX_TASK_ID=print_array
AIRFLOW_CTX_EXECUTION_DATE=2022-09-23T17:31:35.153420 00:00
AIRFLOW_CTX_TRY_NUMBER=1
AIRFLOW_CTX_DAG_RUN_ID=manual__2022-09-23T17:31:35.153420 00:00
[2022-09-23, 18:31:35 BST] {logging_mixin.py:117} INFO - [[ 0  1  2  3  4]
 [ 5  6  7  8  9]
 [10 11 12 13 14]]
[2022-09-23, 18:31:35 BST] {python.py:177} INFO - Done. Returned value was: [[ 0  1  2  3  4]
 [ 5  6  7  8  9]
 [10 11 12 13 14]]
[2022-09-23, 18:31:35 BST] {xcom.py:599} ERROR - Could not serialize the XCom value into JSON. If you are using pickle instead of JSON for XCom, then you need to enable pickle support for XCom in your *** config.
[2022-09-23, 18:31:35 BST] {taskinstance.py:1851} ERROR - Task failed with exception
Traceback (most recent call last):
  File "/home/airflow/.local/lib/python3.8/site-packages/airflow/utils/session.py", line 72, in wrapper
    return func(*args, **kwargs)
  File "/home/airflow/.local/lib/python3.8/site-packages/airflow/models/taskinstance.py", line 2374, in xcom_push
    XCom.set(
  File "/home/airflow/.local/lib/python3.8/site-packages/airflow/utils/session.py", line 72, in wrapper
    return func(*args, **kwargs)
  File "/home/airflow/.local/lib/python3.8/site-packages/airflow/models/xcom.py", line 206, in set
    value = cls.serialize_value(
  File "/home/airflow/.local/lib/python3.8/site-packages/airflow/models/xcom.py", line 597, in serialize_value
    return json.dumps(value).encode('UTF-8')
  File "/usr/local/lib/python3.8/json/__init__.py", line 231, in dumps
    return _default_encoder.encode(obj)
  File "/usr/local/lib/python3.8/json/encoder.py", line 199, in encode
    chunks = self.iterencode(o, _one_shot=True)
  File "/usr/local/lib/python3.8/json/encoder.py", line 257, in iterencode
    return _iterencode(o, 0)
  File "/usr/local/lib/python3.8/json/encoder.py", line 179, in default
    raise TypeError(f'Object of type {o.__class__.__name__} '
TypeError: Object of type ndarray is not JSON serializable
[2022-09-23, 18:31:35 BST] {taskinstance.py:1401} INFO - Marking task as FAILED. dag_id=example_python_operator, task_id=print_array, execution_date=20220923T173135, start_date=20220923T173135, end_date=20220923T173135
[2022-09-23, 18:31:35 BST] {standard_task_runner.py:102} ERROR - Failed to execute job 59 for task print_array (Object of type ndarray is not JSON serializable; 75)
[2022-09-23, 18:31:35 BST] {local_task_job.py:164} INFO - Task exited with return code 1
[2022-09-23, 18:31:35 BST] {local_task_job.py:273} INFO - 0 downstream tasks scheduled from follow-on schedule check

TRIED TO SOLVE THE ISSUE

CodePudding user response：

I had to add AIRFLOW__CORE__ENABLE_XCOM_PICKLING: 'true' to my docker-compose.yml file - https://github.com/apache/airflow/issues/13487#issuecomment-757661848

---
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:-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:-}
    AIRFLOW__CORE__ENABLE_XCOM_PICKLING: 'true'

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