tExtractPositionalFields properties for Apache Spark Batch - 7.0

Processing (Integration)

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7.0
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Data Governance > Third-party systems > Processing components (Integration)
Data Quality and Preparation > Third-party systems > Processing components (Integration)
Design and Development > Third-party systems > Processing components (Integration)

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

The Spark Batch tExtractPositionalFields component belongs to the Processing family.

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

Basic settings

Prev.Comp.Column list

Select an incoming field from the Field list to extract.

Customize

Select this check box to customize the data format of the positional file and define the table columns:

Column: Select the column you want to customize.

Size: Enter the column size.

Padding char: Type in between inverted commas the padding character used, in order for it to be removed from the field. A space by default.

Alignment: Select the appropriate alignment parameter.

Pattern

Enter the pattern to use as basis for the extraction.

A pattern is length values separated by commas, interpreted as a string between quotes. Make sure the values entered in this fields are consistent with the schema defined.

Die on error

Select the check box to stop the execution of the Job when an error occurs.

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.

Click Sync columns to retrieve the schema from the previous component connected in the Job.

 

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.

Advanced settings

Advanced separator (for number)

Select this check box to change the separator used for numbers. By default, the thousands separator is a comma (,) and the decimal separator is a period (.).

Trim Column

Select this check box to remove leading and trailing whitespace from all columns.

Check each row structure against schema

Select this check box to check whether the total number of columns in each row is consistent with the schema. If not consistent, an error message will be displayed on the console.

Usage

Usage rule

This component is used as an intermediate 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 store (technical preview) for Job deployment in the Spark configuration tab.
    • When using other 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.

This connection is effective on a per-Job basis.