Running a Transformation
After creating a transformation as a network of steps (a data workflow) that performs your ETL tasks, you should run it in the PDI client to test how it performs in various scenarios. With the Run Options window, you can apply and adjust different run configurations, options, parameters, and variables. By defining multiple run configurations, you have a choice of running your transformation locally, on a server using the Pentaho engine, or running your transformation using the Spark engine in a Hadoop cluster.
When you are ready to run your transformation, you can perform any of the following actions to access the Run Options window:
Click the Run icon on the toolbar.
- Select Run from the Action menu.
- Press F9.
The Run Options window appears.
In the Run Options window, you can specify a Run configuration to define whether the transformation runs on the Pentaho engine or a Spark client. If you choose the Pentaho engine, you can run the transformation locally, on the Pentaho Server, or on a slave (remote) server. To set up run configurations, see Run Configurations.
The default Pentaho local configuration runs the transformation using the Pentaho engine on your local machine. You cannot edit this default configuration.
The Run Options window also lets you specify logging and other options, or experiment by passing temporary values for defined parameters and variables during each iterative run.
Always show dialog on run is set by default. You can deselect this option if you want to use the same run options every time you execute your transformation. After you have selected to not Always show dialog on run, you can access it again through the dropdown menu next to the Run icon in the toolbar, through the Action main menu, or by pressing F8.
After running your transformation, you can use the Execution Panel to analyze the results.
Some ETL activities are lightweight, such as loading in a small text file to write out to a database or filtering a few rows to trim down your results. For these activities, you can run your transformation locally using the default Pentaho engine. Some ETL activities are more demanding, containing many steps calling other steps or a network of transformation modules. For these activities, you can set up a separate Pentaho Server dedicated for running transformations using the Pentaho engine. Other ETL activites involve large amounts of data on network clusters requiring greater scalability and reduced execution times. For these activities, you can run your transformation using the Spark engine in a Hadoop cluster.
Run configurations allow you to select when to use either the Pentaho (Kettle) or Spark engine. You can create or edit these configurations through the Run configurations folder in the View tab as shown below:
To create a new run configuration, right-click on the Run Configurations folder and select New, as shown in the folder structure below:
To edit or delete a run configuration, right-click on an existing configuration, as shown in the folder structure below:
Pentaho local is the default run configuration. It runs transformations with the Pentaho engine on your local machine. You cannot edit this default configuration.
Selecting New or Edit opens the Run configuration dialog box that contains the following fields:
|Name||Specify the name of the run configuration.|
|Description||Optionally, specify details of your configuration.|
|Engine||Select the type of engine for running a transformation. You can run a transformation with either a Pentaho or a Spark engine. The fields displayed in the Settings section of the dialog box depend on which engine you select.|
Select an Engine
You can select from the following two engines:
- Pentaho Engine: runs transformations in the default Pentaho (Kettle) environment.
- Spark Engine: runs big data transformations through the Adaptive Execution Layer (AEL). AEL builds transformation definitions for Spark, which moves execution directly to your Hadoop cluster, leveraging Spark’s ability to coordinate large amount of data over multiple nodes. See Adaptive Execution Layer for details on how AEL works.
The Settings section of the Run configuration dialog box contains the following options when Pentaho is selected as the Engine for running a transformation:
|Local||Select this option to use the Pentaho engine to run a transformation on your local machine.|
Select this option to run your transformation on the Pentaho Server. This option only appears if you are connected to a Pentaho Repository.
|Slave server||Select this option to send your transformation to a slave (remote) server or Carte cluster.|
If you select Slave server, specify its location.
If you have set up a Carte cluster, you can specify Clustered. See Using Carte Clusters for more details.
|Send resources to the server||If you specified a slave server for your remote Location, select to send your transformation to the specified server before running it. Select this option to run the transformation locally on the server. Any related resources, such as other referenced files, are also included in the information sent to the server.|
|Log remote execution locally||If you specified Clustered for your remote Location, select to show the logs from the cluster nodes.|
|Show transformations||If you specified Clustered for your remote Location, select to show the other transformations that are generated when you run on a cluster.|
The Settings section of the Run configuration dialog box contains the following options when Spark is selected as the Engine for running a transformation:
|Protocol||Select whether the protocol of the Spark host URL uses standard HTTP or HTTPS (SSL encryption).|
|Spark host URL||Specify the address and port for the AEL daemon.|
Refer to your Pentaho or IT administrator as to which Protocol to use and the value for Spark host URL. Your administrator must set up the Adaptive Execution Layer (AEL) before you can use the Spark engine.
See Troubleshooting if issues occur while trying to use the Spark engine.
Errors, warnings, and other information generated as the transformation runs are stored in logs. You can specify how much information is in a log and whether the log is cleared each time through the Options section of this window. You can also enable safe mode and specify whether PDI should gather performance metrics. Logging and Monitoring Operations describes the logging methods available in PDI.
|Clear log before running||Indicates whether to clear all your logs before you run your transformation. If your log is large, you might need to clear it before the next execution to conserve space.|
|Log level||Specifies how much logging is performed and the amount of information captured:
Debug and Row Level logging levels contain information you may consider too sensitive to be shown. Please consider the sensitivity of your data when selecting these logging levels. Performance Monitoring and Logging describes how best to use these logging methods.
|Enable safe mode||Checks every row passed through your transformation and ensure all layouts are identical. If a row does not have the same layout as the first row, an error is generated and reported.|
|Gather performance metrics||Monitors the performance of your transformation execution through these metrics. Using Performance Graphs shows how to visually analyze these metrics.|
Parameters and Variables
You can temporarily modify parameters and variables for each execution of your transformation to experimentally determine their best values. The values you enter into these tables are only used when you run the transformation from the Run Options window. The values you originally defined for these parameters and variables are not permanently changed by the values you specify in these tables.
|Parameters||Set parameter values pertaining to your transformation during runtime. A parameter is a local variable. The parameters you define while creating your transformation are shown in the table under the Parameters tab.
|Variables||Set values for user-defined and environment variables pertaining to your transformation during runtime.|