tLoop properties for Apache Spark Batch

These properties are used to configure tLoop running in the Spark Batch Job framework.

The Spark Batch tLoop component belongs to the Orchestration family.

The component in this framework is available in all subscription-based Talend products with Big Data and Talend Data Fabric.

Basic settings

Loop Type	Select a type of loop to be carried out: either For or While. For: The task or Job is carried out for the defined number of iteration While: The task or Job is carried until the condition is met.
For	From: enter the first instance number which the loop should start from. A start instance number of 2 with a step of 2 means the loop takes on every even number instance. To: enter the last instance number which the loop should finish with. Step: enter the step the loop should be incremented of. A step of 2 means every second instance. Values are increasing: select this check box to only allow an increasing sequence. Deselect this check box to only allow a decreasing sequence.
While	Declaration: enter an expression initiating the loop. Condition: enter the condition that should be met for the loop to end. Iteration: enter the expression showing the operation to be performed at each loop.

Loop Type

Select a type of loop to be carried out: either For or While.

For: The task or Job is carried out for the defined number of iteration

While: The task or Job is carried until the condition is met.

For

From: enter the first instance number which the loop should start from. A start instance number of 2 with a step of 2 means the loop takes on every even number instance.
To: enter the last instance number which the loop should finish with.
Step: enter the step the loop should be incremented of. A step of 2 means every second instance.
Values are increasing: select this check box to only allow an increasing sequence. Deselect this check box to only allow a decreasing sequence.

While

Declaration: enter an expression initiating the loop.
Condition: enter the condition that should be met for the loop to end.
Iteration: enter the expression showing the operation to be performed at each loop.

Global Variables

Global Variables	ERROR_MESSAGE: the error message generated by the component when an error occurs. This is an After variable and it returns a string. This variable functions only if the Die on error check box is cleared, if the component has this check box. CURRENT_VALUE: the current value. Only available for a For type loop. This is a Flow variable and it returns an integer. CURRENT_ITERATION: the sequence number of the current iteration. This is a Flow variable and it returns an integer. A Flow variable functions during the execution of a component while an After variable functions after the execution of the component. To fill up a field or expression with a variable, press Ctrl+Space to access the variable list and choose the variable to use from it. For more information about variables, see Using contexts and variables.

ERROR_MESSAGE: the error message generated by the component when an error occurs. This is an After variable and it returns a string. This variable functions only if the Die on error check box is cleared, if the component has this check box.

CURRENT_VALUE: the current value. Only available for a For type loop. This is a Flow variable and it returns an integer.

CURRENT_ITERATION: the sequence number of the current iteration. This is a Flow variable and it returns an integer.

A Flow variable functions during the execution of a component while an After variable functions after the execution of the component.

To fill up a field or expression with a variable, press Ctrl+Space to access the variable list and choose the variable to use from it.

For more information about variables, see Using contexts and variables.

Usage

Usage rule	tLoop is to be used as a start component and can only be used with an iterate connection to the next component.
For	From
	To
	Step
	Values are increasing
While	Declaration
	Condition
	Iteration
Spark Connection	In the Spark Configuration tab in the Run view, define the connection to a given Spark cluster for the whole Job. In addition, since the Job expects its dependent jar files for execution, you must specify the directory in the file system to which these jar files are transferred so that Spark can access these files: Yarn mode (Yarn client or Yarn cluster): When using Google Dataproc, specify a bucket in the Google Storage staging bucket field in the Spark configuration tab. When using HDInsight, specify the blob to be used for Job deployment in the Windows Azure Storage configuration area in the Spark configuration tab. When using Altus, specify the S3 bucket or the Azure Data Lake Storage for Job deployment in the Spark configuration tab. When using on-premises distributions, use the configuration component corresponding to the file system your cluster is using. Typically, this system is HDFS and so use tHDFSConfiguration. Standalone mode: use the configuration component corresponding to the file system your cluster is using, such as tHDFSConfiguration Apache Spark Batch or tS3Configuration Apache Spark Batch. If you are using Databricks without any configuration component present in your Job, your business data is written directly in DBFS (Databricks Filesystem). This connection is effective on a per-Job basis.

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