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tDenormalize properties for Apache Spark Streaming

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

The Spark Streaming tDenormalize component belongs to the Processing family.

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

Basic settings

Properties Description

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.

    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.

  • 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.

To denormalize

In this table, define the parameters used to denormalize your columns.

Column: Select the column to denormalize.

Delimiter: Type in the separator you want to use to denormalize your data between double quotes.

Merge same value: Select this check box to merge identical values.

Usage

Usage guidance Description

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 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.

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