# tJavaRow properties for Apache Spark Batch These properties are used to configure tJavaRow running in the Spark Batch Job framework. The Spark Batch tJavaRow component belongs to the Custom Code family. The component in this framework is available in all subscription-based Talend products with Big Data and Talend Data Fabric. ## 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. When you create a Spark
Job, avoid the reserved word line when naming the
fields. | | --- | --- | | | Built-In: You create and store the schema locally for this component
only. | | | Repository: You have already created the schema and stored it in the
Repository. You can reuse it in various projects and Job designs.

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 information, see the related
description of retrieving table schemas in Talend Studio User Guide. | | | Click Edit
schema to make changes to the schema. If the current schema is of the Repository type, three options are available:



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:



For further information about these methods, see Apache Spark's
documentation about its Java API in https://spark.apache.org/docs/latest/api/java/index.html. | | 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. | | Code | 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 | Import | Enter the Java code to import, if necessary, external libraries
used in the Code field of the Basic settings view. | | --- | --- | ## 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.

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 | **Usage rule** | This component is used as an intermediate step.

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 | 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:



This connection is effective on a per-Job basis. | | Limitation | Knowledge of Spark and Java language is necessary. |