Step by step approach to easily dockerize Airflow and Pentaho Data Integration IN SEPARATE CONTAINERS. Below is the high level architecture of the setup:

• Airflow:

  • Orchestrator container
  • Sends transformation/job metadata as task to Pentaho container

• Pentaho:

  • Container receives transformation/job details as task to be done
  • Performs (runs) the actual task (transformation/job)

• Docker Engine
• Docker Compose

• Airflow 2.0
• PDI 9.1

Change directory to the project folder before performing below steps.

• Create a .env file and add the user and group Ids for the respective containers. This is required for the containers to have same access privileges as that of the host user during docker compose.

    echo -e "PENTAHO_UID=$(id -u)\nPENTAHO_GID=0\nAIRFLOW_UID=$(id -u)\nAIRFLOW_GID=0" > .env
  

• If needed, append the below optional variables to the above .env file.

    echo -e "<variable name>=<value>" >> .env
  

  • HOST_ENV --> run containers as localhost/dev/qa/prod. This will copy corresponding kettle.properties into the PDI container. Also enables PDI transformations to pick environment specific DB JNDI connections during execution. Can be used by Airflow to connect to corresponding resources.
  • CARTE_USER --> Default: cluster
  • CARTE_PASSWORD --> Default: cluster
  • AIRFLOW_ADMIN_USER --> Create Web UI user. Default: airflow
  • AIRFLOW_ADMIN_PASSWORD --> Default: airflow
  • AIRFLOW_ADMIN_EMAIL --> Required if new user to be created
  • PENTAHO_DI_JAVA_OPTIONS --> Allocate JVM memory to PDI container, based on host machine RAM. Increase if container crashes due to GC Out of memory. Ex: for Min. 1G and Max 4G, set this to "-Xms1g -Xmx4g"
  • CARTE_HOST_PORT --> Default: 8181
  • AIRFLOW_HOST_PORT --> Default: 8080

• Create below folders for the container volumes to bind

   mkdir ./setup-airflow/logs ./setup-airflow/plugins ./setup-pentaho/logs
  

• Source Code Since the DAGs/PDI source code files might undergo frequent updates, they are not copied into the container during image build, instead mounted via docker compose. Any update to these source code files on host will automatically get visible inside the container.

  • Airflow:

    • Default folder for DAGs on host is ./source-code/dags
    • Replace the above default folder in the docker compose file, with the desired folder location on host.
    • Place all the DAG files in the above host dags folder.

  • Pentaho:

    • Default folder for ktr/kjb files on host is ./source-code/ktrs
    • Replace the above default folder in the docker compose file, with the desired folder location on host.
    • Place all the PDI files in the above host ktrs folder.
    • Update repositories.xml file accordingly, to make them visible to Carte.

Below command will build (if first time) and start all the services.

    docker-compose up

To run as daemon, add -d option.

• If not localhost, replace with server endpoint Url
• If not below default ports, replace with the ones used during CARTE_HOST_PORT & AIRFLOW_HOST_PORT setup.

Airflow Webserver

    localhost:8080/home

Carte Webserver

    localhost:8181/kettle/status

As per Carte REST API documentaion, executeJob and executeTrans APIs can be used to trigger tasks remotely.

Job trigger:

    job = BashOperator(
            task_id='Trigger_Job',
            bash_command='curl "${PDI_CONN_STR}/kettle/executeJob/?rep=test-repo&job=/helloworld/helloworld-job"'
    )

Transformation trigger:

    trans = BashOperator(
            task_id='Trigger_Transformation',
            bash_command='curl "${PDI_CONN_STR}/kettle/executeTrans/?rep=test-repo&trans=/helloworld/helloworld-trans"'
    )

• Parameters can be added to curl command by adding &, ex: &param1=value1&param2=value2

• PDI_CONN_STR: this is an environment variable in compose file, set to the PDI docker container URL. Used by Airflow DAG to send tasks to Carte. Below URL has pdi-master as the container name (used in compose file) with Carte running in it.

    http://${CARTE_USER:-cluster}:${CARTE_PASSWORD:-cluster}@pdi-master:${CARTE_HOST_PORT:-8181}
  

In DAG file, import the user defined helper functions defined in utils/execute_pdi.py. Unlike method 1, this makes use of execute-carte.sh file which not only keeps checking Carte task status but also gets actual task log (for jobs only) generated by Carte, into Airflow log.

Job trigger:

    job = BashOperator(
            task_id='Trigger_Job',
            bash_command=execute_job(
            rep="test-repo",
            task="helloworld-job",
            dir="/helloworld/",
            param=""
            )
    )

Transformation trigger:

    trans = BashOperator(
            task_id='Trigger_Transformation',
            bash_command=execute_trans(
            rep="test-repo",
            task="helloworld-trans",
            dir="/helloworld/",
            param=""
            )
    )

• jdbc.properties file, which contains database access credentials, has been included in this repo for reference purpose only. In actual development, this should be avoided and needs to be added to gitignore instead. After first code pull to a server, update it with all JNDI details before docker compose.

• .env file also may contain sensitive information, like environment dependent access keys. This also should be added to .gitignore file. Instead create this file with necessary parameters during image build.

• HOST_ENV setting this parameter gives us a flexibility to choose appropriate kettle.properties file. For example, QA and PROD mailing server SMTP details may differ. This can be included in separate kettle properties file, to be selected dynamically based on the host environment. Not only this, if one uses the jdbc.properties file, we can enable PDI container dynamically select the correct JNDI from jdbc.properties file. For ex: if one needs to test a transformation in QA environemnt using Postgres JNDI connection encoded as db-${HOST_ENV}, running PDI service with HOST_ENV=qa, will render db-qa database JNDI, thus using QA data for testing.

• PENTAHO_DI_JAVA_OPTIONS Having this option lets the user tweak the amount of memory PDI gets inside the container, to run a task. Depending on the host machine memory and average task complexity, this can be modified to avoid PDI container crash due to "GC Out of Memory" errors. If host machine has ample RAM and PDI container is crashing due to the default memory limits, we can increase it by setting PENTAHO_DI_JAVA_OPTIONS=-Xms2g -Xmx4g 2GB and 4GB being the lower and upper limits respectively.

• What is Carte Server ?

• Configure Carte Server

• Set Repository on the Carte Server

• Carte APIs to trigger kettle transformation/jobs

• Monitoring Carte logs from Airlfow container

• Docker entrypoint logic