Executing a Docker Decorator task results in Jinja Error

Apache Airflow Provider(s)

docker

Versions of Apache Airflow Providers

apache-airflow-providers-docker==3.1.0

Apache Airflow version

2.4.0

Operating System

Debian GNU/Linux 11 (bullseye)

Deployment

Docker-Compose

Deployment details

Client: Docker Engine - Community Cloud integration: v1.0.28 Version: 20.10.17 API version: 1.41 Go version: go1.17.11 Git commit: 100c701 Built: Mon Jun 6 23:03:17 2022 OS/Arch: linux/amd64 Context: default Experimental: true

Docker Compose: v2.7.0

Using a slightly modified version of the example 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:2.4.0
# 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:-apache/airflow:2.4.0}
  # build: .
  environment:
    &airflow-common-env
    AIRFLOW__CORE__EXECUTOR: LocalExecutor
    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__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: ${_PIP_ADDITIONAL_REQUIREMENTS:-}
    IS_LOCAL: 'true'
  volumes:
    - ./dags:/opt/airflow/dags
    - ./logs:/opt/airflow/logs
    - ./plugins:/opt/airflow/plugins
    - ./kube.conf:/opt/airflow/kube.conf
    - /var/run/docker.sock:/var/run/docker.sock
  user: "${AIRFLOW_UID:-50000}:0"
  group_add:
    - '1001' # Add user to docker group. Change value depending on gid of docker on your machine
  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-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/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 "\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/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

volumes:
  postgres-db-volume:

What happened

I was trying to test running a task using the @task.docker decorator, so I set up the following DAG with a series of Docker tasks.

from airflow import DAG
from airflow.decorators import task, dag

from docker.types import Mount
from datetime import datetime

@dag(
    description='Run a series of Docker containers with outputs',
    start_date=datetime(2022, 1, 1),
    catchup=False,
    schedule_interval=None,
)
def docker_parallel_decorator():
    @task.docker(image="python:3.9-slim-bullseye")
    def container_a():
        print("Hello from Container A")
        return None

    @task.docker(image="python:3.9-slim-bullseye")
    def container_b():
        print("Hello from Container B")
        return None

    @task.docker(image="python:3.9-slim-bullseye")
    def container_c():
        print("Hello from Container C")
        return None

    container_a() >> container_b() >> container_c()

docker_parallel_decorator()

In the past, I’ve had success with the DockerOperator, so I expected no difference. However, I received the following error in the output log:

[2022-09-27, 17:37:03 UTC] {taskinstance.py:1851} ERROR - Task failed with exception
Traceback (most recent call last):
  File "/home/airflow/.local/lib/python3.7/site-packages/airflow/providers/docker/decorators/docker.py", line 111, in execute
    filename=script_filename,
  File "/home/airflow/.local/lib/python3.7/site-packages/airflow/utils/python_virtualenv.py", line 128, in write_python_script
    template.stream(**jinja_context).dump(filename)
  File "/home/airflow/.local/lib/python3.7/site-packages/jinja2/environment.py", line 1618, in dump
    fp.writelines(iterable)
  File "/home/airflow/.local/lib/python3.7/site-packages/jinja2/environment.py", line 1613, in <genexpr>
    iterable = (x.encode(encoding, errors) for x in self)  # type: ignore
  File "/home/airflow/.local/lib/python3.7/site-packages/jinja2/environment.py", line 1662, in __next__
    return self._next()  # type: ignore
  File "/home/airflow/.local/lib/python3.7/site-packages/jinja2/environment.py", line 1354, in generate
    yield self.environment.handle_exception()
  File "/home/airflow/.local/lib/python3.7/site-packages/jinja2/environment.py", line 936, in handle_exception
    raise rewrite_traceback_stack(source=source)
  File "/home/airflow/.local/lib/python3.7/site-packages/airflow/utils/python_virtualenv_script.jinja2", line 23, in top-level template code
    {% if expect_airflow %}
jinja2.exceptions.UndefinedError: 'expect_***' is undefined

What you think should happen instead

I expected the Docker tasks to run the code in the provided Python function.

How to reproduce

1. Deploy Airflow from the provided docker-compose.yaml file
2. Place the provided DAG into the ./dags folder
3. Manually trigger the docker_parallel_decorator from the web UI

Anything else

I have no experience with Jinja, so I don’t know the specifics, but I noticed that I was able to create a workaround by patching the /home/airflow/.local/lib/python3.7/site-packages/airflow/providers/docker/decorators/docker.py file in the airflow-scheduler service.

First, I copied the file out of the container.

docker compose cp airflow-scheduler:/home/airflow/.local/lib/python3.7/site-packages/airflow/providers/docker/decorators/docker.py ./docker.py

Then I changed the following snippet starting on line 101:

            write_python_script(
                jinja_context=dict(
                    op_args=self.op_args,
                    op_kwargs=self.op_kwargs,
                    pickling_library=self.pickling_library.__name__,
                    python_callable=self.python_callable.__name__,
                    python_callable_source=py_source,
                    string_args_global=False,
                ),
                filename=script_filename,
            )

To this:

            write_python_script(
                jinja_context=dict(
                    op_args=self.op_args,
                    op_kwargs=self.op_kwargs,
                    pickling_library=self.pickling_library.__name__,
                    python_callable=self.python_callable.__name__,
                    python_callable_source=py_source,
                    string_args_global=False,
                    expect_airflow=False, # Added this line
                ),
                filename=script_filename,
            )

Then I copied the file back into the container.

docker compose cp ./docker.py airflow-scheduler:/home/airflow/.local/lib/python3.7/site-packages/airflow/providers/docker/decorators/docker.py

After that, running the DAG resulted in no errors with the expected output in the logs.

Are you willing to submit PR?

• Yes I am willing to submit a PR!

Code of Conduct

• I agree to follow this project’s Code of Conduct

[noah-gil]

I can confirm that the problem is fixed for the RC version. Thanks for the quick response!

[potiuk]

Glad it worked 😃