Python库 | airflow-pentaho-plugin-1.0.3.tar.gz

# Pentaho Airflow plugin [](https://travis-ci.org/damavis/airflow-pentaho-plugin) [](https://codecov.io/gh/damavis/airflow-pentaho-plugin) [](https://pypi.org/project/airflow-pentaho-plugin/) [](https://pypi.org/project/airflow-pentaho-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](https://airflow.apache.org/docs/stable/concepts.html#queues), in **Airflow** *Concepts* section. ### Pip package First of all, the package should be installed via `pip install` command. ```bash 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. ```json { "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. ```json { "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. ```python # For versions before 2.0 # from airflow.operators.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`. ```python # For versions before 2.0 # from airflow.operators.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. ```python # For versions before 2.0 # from airflow.operators.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. ```python # For versions before 2.0 # from airflow.operators.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`