# tJava properties for Apache Spark Streaming These properties are used to configure tJava running in the Spark Streaming Job framework. The Spark Streaming tJava component belongs to the Custom Code family. This component is available in Talend Real Time Big Data Platform and Talend Data Fabric. ## Basic settings | Schema et 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.

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



Note that if the input value of any non-nullable primitive field is
null, the row of data including that field will be rejected. | | --- | --- | | | 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. | | Code | Type in the Java code you want to execute to process the incoming RDD
from the input link or even create new RDDs out of this input one.

You need to leverage the schema, the link and the component name to
write the custom code. For example, if this component is labeled
*tJava\_1* and the connection to it
is labeled *row1*, then the class of
the input RDD is *row1Struct* and the
input RDD itself is available with the *rdd\_tJava\_1* variable.

For more detailed instructions, see the default comment provided in
the Code field of this
component.

For further information about Spark Java API, see
Apache's Spark documentation in https://spark.apache.org/docs/latest/api/java/index.html. | ## Advanced settings | Classes | Define the classes that you need to use in the code written in the Code field in the Basic settings
view.

It is recommended to define new classes in this field, instead of in the Code field, so as to avoid
eventual exceptions in serialization. | | --- | --- | | Import | Enter the Java code to import, if necessary, external libraries
used in the Code field of the Basic settings view. | ## Usage | **Usage rule** | This component is used as an end component and requires an input link. | | --- | --- | | Code example | In the Code field of the Basic settings view,
enter the following code to create an output RDD by using custom transformations on the
input RDD. mapInToOut is a class to be defined in the
Classes field in the Advanced settings
view.

```
outputrdd_tJava_1 = rdd_tJava_1.map(new mapInToOut(job));
```

In
the Classes field of the Advanced settings
view, enter the following code to define the mapInToOut
class:

```
public static class mapInToOut implements
org.apache.spark.api.java.function.Function{

private ContextProperties context = null;
private java.util.List fieldsList;

public mapInToOut(JobConf job) {
this.context = new ContextProperties(job);
}

@Override
public RecordOut_tJava_1 call(inputStruct origStruct) {

if (fieldsList == null) {
this.fieldsList = (new inputStruct()).getSchema()
.getFields();
}

RecordOut_tJava_1 value = new RecordOut_tJava_1();

for (org.apache.avro.Schema.Field field : fieldsList) {
value.put(field.pos(), origStruct.get(field.pos()));
}

return value;

}
}
``` | | 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. |