tJava

Extends the functionalities of a Talend Job using custom Java
commands.

tJava enables you to enter personalized
code in order to integrate it in Talend program. You can execute
this code only once.

Depending on the Talend
product you are using, this component can be used in one, some or all of the following
Job frameworks:

Standard: see tJava Standard properties.

The component in this framework is available in all Talend
products.
Spark Batch: see tJava properties for Apache Spark Batch.

The component in this framework is available in all subscription-based Talend products with Big Data
and Talend Data Fabric.
Spark Streaming: see tJava properties for Apache Spark Streaming.

This component is available in Talend Real Time Big Data Platform and Talend Data Fabric.

tJava Standard properties

These properties are used to configure tJava running in the Standard Job framework.

The Standard
tJava component belongs to the Custom Code family.

The component in this framework is available in all Talend
products.

Basic settings

Code	Type in the Java code you want to execute according to the task you need to perform. For further information about Java functions syntax specific to Talend , see Talend Studio Help Contents (Help > Developer Guide > API Reference). For a complete Java reference, check http://docs.oracle.com/javaee/6/api/ Note: This component offers the advantage of the dynamic schema feature. This allows you to retrieve unknown columns from source files or to copy batches of columns from a source without mapping each column individually. For further information about dynamic schemas, see Talend Studio User Guide. Note: If your custom Java code references `org.talend.transform.runtime.api.ExecutionStatus`, change it to `org.talend.transform.runtime.common.MapExecutionStatus`.

Code

Type in the Java code you want to execute according to the task
you need to perform. For further information about Java functions syntax
specific to
Talend
, see
Talend Studio
Help Contents (Help > Developer Guide > API Reference).

For a complete Java reference, check http://docs.oracle.com/javaee/6/api/

Note:

This component offers the advantage of the dynamic schema
feature. This allows you to retrieve unknown columns from source files or
to copy batches of columns from a source without mapping each column
individually. For further information about dynamic schemas, see

Talend Studio
User Guide.

Note: If your custom Java code references
org.talend.transform.runtime.api.ExecutionStatus, change it
to
org.talend.transform.runtime.common.MapExecutionStatus.

Advanced settings

Import	Enter the Java code to import, if necessary, external libraries used in the Code field of the Basic settings view.
tStatCatcher Statistics	Select this check box to gather the Job processing metadata at a Job level as well as at each component level.

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.

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.

Usage

Usage rule	This component is generally used as a one-component subJob.
Limitation	You should know Java language.

Printing out a variable content

The following scenario is a simple demo of the extended application of the
tJava component. The Job aims at printing out the
number of lines being processed using a Java command and the global variable provided in
Talend Studio
.

Setting up the Job

Select and drop the following components from the Palette onto the design workspace: tFileInputDelimited, tFileOutputExcel, tJava.
Connect the tFileInputDelimited to the
tFileOutputExcel using a Row Main connection. The content from a delimited
txt file will be passed on through the connection to an xls-type of file
without further transformation.
Then connect the tFileInputDelimited
component to the tJava component using a
Trigger > On
Subjob Ok link. This link sets a sequence ordering tJava to be executed at the end of the main
process.

Configuring the input component

Set the Basic settings of the tFileInputDelimited component.
Define the path to the input file in the File
name field.

The input file used in this example is a simple text file made of two
columns: Names and their respective
Emails.
Click the Edit Schema button, and set the
two-column schema. Then click OK to close
the dialog box.
When prompted, click OK to accept the
propagation, so that the tFileOutputExcel
component gets automatically set with the input schema.

Configuring the output component

Set the output file to receive the input content without changes. If the file does
not exist already, it will get created.

In this example, the Sheet name is
Email and the Include
Header box is selected.

Configuring the tJava component

Then select the tJava component to set
the Java command to execute.
In the Code area, type in the following
command:

String var = "Nb of line processed: "; var = var + globalMap.get("tFileInputDelimited_1_NB_LINE"); System.out.println(var);

1
2
3

String var = "Nb of line processed: ";
var = var + globalMap.get("tFileInputDelimited_1_NB_LINE");
System.out.println(var);

In this use case, we use the NB_Line variable. To
access the global variable list, press Ctrl + Space bar on your keyboard and
select the relevant global parameter.

Executing the Job

Press Ctrl+S to save
your Job.
Press F6 to execute
it.

The content gets passed on to the Excel file defined and the Number of lines
processed are displayed on the Run
console.

tJava properties for Apache Spark Batch

These properties are used to configure tJava running in the Spark Batch Job framework.

The Spark Batch
tJava 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 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: 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.
	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’s 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.

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));

1	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&lt;inputStruct,RecordOut_tJava_1&gt;{

     private ContextProperties context = null;
     private java.util.List&lt;org.apache.avro.Schema.Field&gt; 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;

      }
}

public static class mapInToOut implements

org.apache.spark.api.java.function.Function<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);

}

@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:

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-premise
  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 or
tS3Configuration.

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.

Limitation

Knowledge of Spark and Java language is required.

Related scenarios

No scenario is available for the Spark Batch version of this component
yet.

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: 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.
	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.

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

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

1	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&lt;inputStruct,RecordOut_tJava_1&gt;{

     private ContextProperties context = null;
     private java.util.List&lt;org.apache.avro.Schema.Field&gt; 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;

      }
}

public static class mapInToOut implements

org.apache.spark.api.java.function.Function<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);

}

@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

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-premise
  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 or
tS3Configuration.

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.

Related scenarios

No scenario is available for the Spark Streaming version of this component
yet.

Document get from Talend https://help.talend.com

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Docs 7.x

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tJava – Docs for ESB 7.x

tJava

tJava Standard properties

Basic settings

Advanced settings

Global Variables

Usage

Printing out a variable content

Setting up the Job

Configuring the input component

Configuring the output component

Configuring the tJava component

Executing the Job

tJava properties for Apache Spark Batch

Basic settings

Advanced settings

Usage

Related scenarios

tJava properties for Apache Spark Streaming

Basic settings

Advanced settings

Usage

Related scenarios

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