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tCollectAndCheck properties for Apache Spark Batch

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 Talend products with Big Data and Talend Data Fabric.

Basic settings

Configuration

Select this check box to retrieve connection information and credentials from a configuration component. You must select this check box for the following type of input data to be checked:
  • HBase
  • JDBC
  • MySQL
  • Redshift
  • Snowflake

In the drop-down list that appears, select the configuration component from which you want Spark to use the configuration details to connect to the database. For example, if you want to check Snowflake data, you have to select the tSnowflakeConfiguration component.

Information noteNote: If you want to retrieve data from S3, you do not have to use tS3Configuration, you only have to enter the full path of the file in the Path or table name field from the Basic settings view.

Type of input

Select the type of input data to be checked from the drop-down list.

Path or table name

Enter the path to the file or the table to be checked in double quotation marks.

Separator

Enter a character, a string, or a 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.

Information noteNote:

Due to the migration of the component from RDD to dataset, the pattern for a date can only be yyyy-MM-dd, and the pattern for a timestamp can only be yyyy-MM-dd HH:mm:ss.

This applies for Spark 2.1 or a higher version.

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

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-premises 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 Apache Spark Batch or tS3Configuration Apache Spark Batch.

    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.

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