July 31, 2023

tLogRow properties for Apache Spark Streaming – Docs for ESB File Delimited 7.x

tLogRow properties for Apache Spark Streaming

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

The Spark Streaming
tLogRow component belongs to the Misc 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.

Sync columns Click to synchronize the output file schema with the input file schema. The
Sync function is available only when the component is linked with the preceding
component using a Row connection.
Basic Displays the output flow in basic mode.
Table Displays the output flow in table cells.
Vertical

Displays each row of the output flow as a key-value list.

With this mode selected, you can choose to show either the
unique name or the label of component, or both of them, for each output
row.

Separator

(For Basic mode only)

Enter the separator which will delimit data on the Log
display.

Print header

(For Basic mode only)

Select this check box to include the header of the input flow in the output
display.

Print component unique name in front of each output
row

(For Basic mode only)

Select this check box to show the unique name the component in
front of each output row to differentiate outputs in case several tLogRow components are used.

Print schema column name in front of each
value

(For Basic mode only)

Select this check box to retrieve column labels from output
schema.

Use fixed length for values

(For Basic mode only)

Select this check box to set a fixed width for the value
display.

Advanced settings

Use local timezone for date Select this check box to use the local date of the machine in which your Job is executed. If leaving this check box clear, UTC is automatically used to format the Date-type data.

Usage

Usage rule

This component is used as an intermediate or an end 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.


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