tCollectAndCheck properties for Apache Spark Streaming

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

The Spark Streaming tCollectAndCheck component belongs to the Technical family.

This component is available in Talend Real Time Big Data Platform 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 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.
Micro batch separator	Enter the separator used to identify the end of a micro batch in the data stream.
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. The 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.

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.

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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