July 31, 2023

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

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tLogRow properties for Apache Spark Batch

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

The Spark Batch
tLogRow component belongs to the Misc family.

The component in this framework is available in all subscription-based Talend products with Big Data
and Talend Data Fabric.

Basic settings

Define a storage configuration
component

Select the configuration component to be used to provide the configuration
information for the connection to the target file system such as HDFS.

If you leave this check box clear, the target file system is the local
system.

The configuration component to be used must be present in the same Job.
For example, if you have dropped a tHDFSConfiguration component in the Job, you can select it to write
the result in a given HDFS system.

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 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

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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Docs 7.x
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