tCollectAndCheck properties for Apache Spark Batch - 6.5

Technical

author
Talend Documentation Team
EnrichVersion
6.5
EnrichProdName
Talend Big Data
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task
Data Governance > Third-party systems > Technical components
Data Quality and Preparation > Third-party systems > Technical components
Design and Development > Third-party systems > Technical components
EnrichPlatform
Talend Studio

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

The Spark Batch tCollectAndCheck component belongs to the Technical family.

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

Basic settings

Separator

Enter character, string or regular expression to separate fields for the transferred data.

Line separator

The separator used to identify the end of a row.

Use context variable

If you have already created the context variable representing the reference file to be used, select this check box and enter this variable in the Variable name field that is displayed.

Then syntax to call a variable is context.VariableName.

For further information about variables, see Talend Studio User Guide.

Reference data

If you do not want to use context variables to represent the reference data to be used, enter this reference data directly in this field.

Keep the order from the reference

If the RDDs to be checked are sorted, select this check box to keep your reference data ordered.

Advanced settings

When the reference is empty, expect no incoming value

By default, this check box is clear, meaning that when an field in the reference data is empty, the test expects an equally empty field in the incoming datasets being verified in order to validate the test result.

If you want the test to expect no value when the reference is empty, select this check box.

Usage

Usage rule

This component is used as an end component and requires an input link.

This component is added automatically to a test case being created to show the test result in the console of the Run view.

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.