tJavaRow properties in Spark Batch Jobs - 6.1

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Design and Development

Component Family

Custom Code


Basic settings

Schema and Edit Schema

A schema is a row description. It defines the number of fields (columns) to be processed and passed on to the next component. The schema is either Built-In or stored remotely in the Repository.



Built-In: You create and store the schema locally for this component only. Related topic: see Talend Studio User Guide.



Repository: You have already created the schema and stored it in the Repository. You can reuse it in various projects and Job designs. Related topic: see Talend Studio User Guide.

When the schema to be reused has default values that are integers or functions, ensure that these default values are not enclosed within quotation marks. If they are, you must remove the quotation marks manually.

For more details, see


Click Edit schema to make changes to the schema. If the current schema is of the Repository type, three options are available:

  • View schema: choose this option to view the schema only.

  • Change to built-in property: choose this option to change the schema to Built-in for local changes.

  • Update repository connection: choose this option to change the schema stored in the repository and decide whether to propagate the changes to all the Jobs upon completion. If you just want to propagate the changes to the current Job, you can select No upon completion and choose this schema metadata again in the [Repository Content] window.

Click Sync columns to retrieve the schema from the previous component connected in the Job.

Note that the input schema and the output schema of this component can be different.


Map type

Select the type of the Map transformation you need to write. This allows the component to automatically select the method accordingly and declare the variables to be used in your custom code.

The available types are:

  • Map: it returns only one output record for each input record. It uses Spark's PairFunction method

  • FlatMap: it returns 0 or more output records for each input record. It uses Spark's FlatMapFunction method.

For further information about these methods, see Apache Spark's documentation about its Java API in


Generate code

Click this button to automatically generate the code in the Code field to map the columns of the input schema with those of the output schema. This generation does not change anything in your schema.

The generated sample code shows what the pre-defined variables are for the input and the output RDDs and how these variables can be used.



Write the custom body of the method you have selected from the Map type drop-down list. You need to use the input schema and the output schema to manage the columns of the input and the output RDD records. This custom code is applied on a row-by-row basis in the RDD records.

For example, the input schema contains a user column, then you need to use the input.user variable to get the user column of each input record.

For further information about the available variables in writing the custom code, see the default comment displayed in this field.

Advanced settings


Enter the Java code to import, if necessary, external libraries used in the Code field of the Basic settings view.

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.

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 further information about variables, see Talend Studio User Guide.

To enter a global variable (for example COUNT of tFileRowCount) in the Code box, you need to type in the entire piece of code manually, that is to say ((Integer)globalMap.get("tFileRowCount_COUNT")).

Usage in Spark Batch Jobs

In a Talend Spark Batch Job, this component is used as an intermediate step and other components used along with it must be Spark Batch components, too. They generate native Spark Batch code that can be executed directly in a Spark cluster.

This component, along with the Spark Batch component Palette it belongs to, appears only when you are creating a Spark Batch Job.

Note that in this documentation, unless otherwise explicitly stated, a scenario presents only Standard Jobs, that is to say traditional Talend data integration Jobs.

Spark Connection

You need to use the Spark Configuration tab in the Run view to define the connection to a given Spark cluster for the whole Job. In addition, since the Job expects its dependent jar files for execution, one and only one file system related component from the Storage family is required in the same Job so that Spark can use this component to connect to the file system to which the jar files dependent on the Job are transferred:

This connection is effective on a per-Job basis.


If you are using a subscription-based version of the Studio, the activity of this component can be logged using the log4j feature. For more information on this feature, see Talend Studio User Guide.

For more information on the log4j logging levels, see the Apache documentation at


Knowledge of Spark and Java language is necessary.