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tJavaRow properties for Apache Spark Streaming

These properties are used to configure tJavaRow running in the Spark Streaming Job framework.

The Spark Streaming tJavaRow 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 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:

  • 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.


Usage rule

This component is used as an intermediate step.

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.

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 Qubole, add a tS3Configuration to your Job to write your actual business data in the S3 system with Qubole. Without tS3Configuration, this business data is written in the Qubole HDFS system and destroyed once you shut down your cluster.
    • 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.


Knowledge of Spark and Java language is necessary.

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