tWriteXMLFields properties for Apache Spark Streaming - 7.1

Processing (Integration)

Version
7.1
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Data Governance > Third-party systems > Processing components (Integration)
Data Quality and Preparation > Third-party systems > Processing components (Integration)
Design and Development > Third-party systems > Processing components (Integration)

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

The Spark Streaming tWriteXMLFields component belongs to the Processing family.

The streaming version of this component is available in Talend Real Time Big Data Platform and in Talend Data Fabric.

Basic settings

Output type

Select the type of the data to be outputted into the target file. The data is byte arrays if you select byte.

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.

The schema of this component is read-only. You can click Edit schema to view the schema.

When the output type is String, the read-only single column is messageContent. This column is used to provide strings to the output components such as tJMSOutput.

When the output type is byte, the read-only single column is serializedValue. This column is used to provide byte arrays to the output components such as tKafkaOutput.

The output schema and its read-only column can be seen by clicking the Row > Output link to the component that follows in the same Job. The schema is displayed in the Basic settings tab of the Component view

Row tag

Specify the tag that will wrap data and structure per row.

Custom encoding

You may encounter encoding issues when you process the stored data. In that situation, select this check box to display the Encoding list.

Select the encoding from the list or select Custom and define it manually. This field is compulsory for database data handling. The supported encodings depend on the JVM that you are using. For more information, see https://docs.oracle.com.

Advanced settings

Root tags

Select this check box to change the separator used for numbers. By default, the thousands separator is a comma (,) and the decimal separator is a period (.).

Output format

Define the output format.

  • Column: The columns retrieved from the input schema.

  • As attribute: select check box for the column(s) you want to use as attribute(s) of the parent element in the XML output.

Note:

If the same column is selected in both the Output format table as an attribute and in the Use dynamic grouping setting as the criterion for dynamic grouping, only the dynamic group setting will take effect for that column.

Use schema column name: By default, this check box is selected for all columns so that the column labels from the input schema are used as data wrapping tags. If you want to use a different tag than from the input schema for any column, clear this check box for that column and specify a tag label between quotation marks in the Label field.

Use dynamic grouping

Select this check box if you want to dynamically group the output columns. Click the plus button to add one ore more grouping criteria in the Group by table.

Column: Select a column you want to use as a wrapping element for the grouped output rows.

Attribute label: Enter an attribute label for the group wrapping element, between quotation marks.

Usage

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-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.