tJava properties in Spark Streaming Jobs - 6.1

Talend Components Reference Guide

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Design and Development


The streaming version of this component is available in the Palette of the studio on the condition that you have subscribed to Talend Real-time Big Data Platform or Talend Data Fabric.

Component family

Custom Code


 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. The schema is either Built-In or stored remotely in the Repository.

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.

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. 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.



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's Java API, see Apache's Spark documentation in

Advanced settings


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.


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

Usage in Spark Streaming Jobs

In a Talend Spark Streaming Job, it is used as an end component and requires an input link. The other components used along with it must be Spark Streaming components, too. They generate native Spark code that can be executed directly in a Spark cluster.

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

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 = 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<inputStruct,RecordOut_tJava_1>{
   private ContextProperties context = null;
   private java.util.List<org.apache.avro.Schema.Field> fieldsList;
   public mapInToOut(JobConf job) {
       this.context = new ContextProperties(job);
   public RecordOut_tJava_1 call(inputStruct origStruct) {		
      if (fieldsList == null) {
          this.fieldsList = (new inputStruct()).getSchema()
      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

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