tLogRow properties for Apache Spark Batch

Logs and errors (Integration)

author
Talend Documentation Team
EnrichVersion
6.4
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Talend Big Data
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Talend Data Integration
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task
Data Governance > Third-party systems > Logs and errors components (Integration)
Data Quality and Preparation > Third-party systems > Logs and errors components (Integration)
Design and Development > Third-party systems > Logs and errors components (Integration)
EnrichPlatform
Talend Studio

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. The schema is either Built-In or stored remotely in the Repository.

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. Related topic: see Talend Studio User Guide.

 

Repository: You have already created the schema and stored it in the Repository. You can reuse it in various projects and Job designs. Related topic: see Talend Studio User Guide.

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

You need to use the Spark Configuration tab in the Run view to 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: when using Google Dataproc, specify a bucket in the Google Storage staging bucket field in the Spark configuration tab; when using other distributions, use a tHDFSConfiguration component to specify the directory.

  • Standalone mode: you need to choose the configuration component depending on the file system you are using, such as tHDFSConfiguration or tS3Configuration.

This connection is effective on a per-Job basis.