tBoundedStreamInput properties for Apache Spark Streaming - 7.2

Technical

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Data Governance > Third-party systems > Technical components
Data Quality and Preparation > Third-party systems > Technical components
Design and Development > Third-party systems > Technical components

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

The Spark Streaming tBoundedStreamInput component belongs to the Technical 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.

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.

Mode

Select the mode that you want to use to generate the data stream.

  • Use Inline Content: enter the data that you want to generate.

  • Use context variable: enter the name of variable to be used to provide data. This variable must have been defined in the Contexts tab of the current Job.

    The syntax to call a variable is context.VariableName.

In either mode, the data you provide must use the separators you have defined in the Row separator, Field Separator and Micro batch separator fields.

Usage

Usage rule

This component is used as a start component and requires an output link.

This component is added automatically to a test case being created to provide input data.

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.