airflow-pentaho-plugin 1.2.0

No project description provided

Pentaho Airflow plugin

This plugins runs Jobs and Transformations through Carte servers. It allows to orchestrate a massive number of trans/jobs taking care of the dependencies between them, even between different instances. This is done by using CarteJobOperator and CarteTransOperator

It also runs Pan (transformations) and Kitchen (Jobs) in local mode, both from repository and local XML files. For this approach, use KitchenOperator and PanOperator

Requirements

1. A Apache Airflow system deployed.
2. One or many working PDI CE installations.
3. A Carte server for Carte Operators.

Setup

The same setup process must be performed on webserver, scheduler and workers (that runs this tasks) to get it working. If you want to deploy specific workers to run this kind of tasks, see Queues, in Airflow Concepts section.

Pip package

First of all, the package should be installed via pip install command.

pip install airflow-pentaho-plugin

Airflow connection

Then, a new connection needs to be added to Airflow Connections, to do this, go to Airflow web UI, and click on Admin -> Connections on the top menu. Now, click on Create tab.

Use HTTP connection type. Enter the Conn Id, this plugin uses pdi_default by default, the username and the password for your Pentaho Repository.

At the bottom of the form, fill the Extra field with pentaho_home, the path where your pdi-ce is placed, and rep, the repository name for this connection, using a json formatted string like it follows.

{
    "pentaho_home": "/opt/pentaho",
    "rep": "Default"
}

Carte

In order to use CarteJobOperator, the connection should be set different. Fill host (including http:// or https://) and port for Carte hostname and port, username and password for PDI repository, and extra as it follows.

{
    "rep": "Default",
    "carte_username": "cluster",
    "carte_password": "cluster"
}

Usage

CarteJobOperator

CarteJobOperator is responsible for running jobs in remote slave servers. Here it is an example of CarteJobOperator usage.

# For versions before 2.0
# from airflow.operators.airflow_pentaho import CarteJobOperator

from airflow_pentaho.operators.carte import CarteJobOperator

# ... #

# Define the task using the CarteJobOperator
avg_spent = CarteJobOperator(
    conn_id='pdi_default',
    task_id="average_spent",
    job="/home/bi/average_spent",
    params={"date": "{{ ds }}"},  # Date in yyyy-mm-dd format
    dag=dag)

# ... #

some_task >> avg_spent >> another_task

KitchenOperator

Kitchen operator is responsible for running Jobs. Lets suppose that we have a defined Job saved on /home/bi/average_spent in our repository with the argument date as input parameter. Lets define the task using the KitchenOperator.

# For versions before 2.0
# from airflow.operators.airflow_pentaho import KitchenOperator

from airflow_pentaho.operators.kettle import KitchenOperator

# ... #

# Define the task using the KitchenOperator
avg_spent = KitchenOperator(
    conn_id='pdi_default',
    queue="pdi",
    task_id="average_spent",
    directory="/home/bi",
    job="average_spent",
    params={"date": "{{ ds }}"},  # Date in yyyy-mm-dd format
    dag=dag)

# ... #

some_task >> avg_spent >> another_task

CarteTransOperator

CarteTransOperator is responsible for running transformations in remote slave servers. Here it is an example of CarteTransOperator usage.

# For versions before 2.0
# from airflow.operators.airflow_pentaho import CarteTransOperator

from airflow_pentaho.operators.carte import CarteTransOperator

# ... #

# Define the task using the CarteJobOperator
enriche_customers = CarteTransOperator(
    conn_id='pdi_default',
    task_id="enrich_customer_data",
    job="/home/bi/enrich_customer_data",
    params={"date": "{{ ds }}"},  # Date in yyyy-mm-dd format
    dag=dag)

# ... #

some_task >> enrich_customers >> another_task

PanOperator

Pan operator is responsible for running transformations. Lets suppose that we have one saved on /home/bi/clean_somedata. Lets define the task using the PanOperator. In this case, the transformation receives a parameter that determines the file to be cleaned.

# For versions before 2.0
# from airflow.operators.airflow_pentaho import PanOperator

from airflow_pentaho.operators.kettle import PanOperator

# ... #

# Define the task using the PanOperator
clean_input = PanOperator(
    conn_id='pdi_default',
    queue="pdi",
    task_id="cleanup",
    directory="/home/bi",
    trans="clean_somedata",
    params={"file": "/tmp/input_data/{{ ds }}/sells.csv"},
    dag=dag)

# ... #

some_task >> clean_input >> another_task

For more information, please see sample_dags/pdi_flow.py