tJavaRow properties for Apache Spark Batch

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 Retrieving table schemas.

• 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.

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.

Import

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

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 more information about variables, see Using contexts and variables.

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 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

This connection is effective on a per-Job basis.

Limitation

Knowledge of Spark and Java language is necessary.