tMapRDBOutput properties for Apache Spark Batch

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

The Spark Batch tMapRDBOutput component belongs to the Databases family.

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

Basic settings

Storage configuration	Select the tMapRDBConfiguration component from which the Spark system to be used reads the configuration information to connect to MapRDB.
Schema et 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.
	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.
Table name	Type in the name of the table in which you need to write data. This table must already exist.
Table Namespace mappings	Enter the string to be used to construct the mapping between an Apache HBase table and a MapR table. For the valid syntax you can use, see http://doc.mapr.com/display/MapR40x/Mapping+Table+Namespace+Between+Apache+HBase+Tables+and+MapR+Tables.
Row key column	Select the column used as the row key column of the table. Then if needs be, select the Store row key column to HBase column check box to make the row key column a column belonging to a specific column family.
Families	Complete this table to map the columns of the table to be used with the schema columns you have defined for the data flow to be processed. The Column column of this table is automatically filled once you have defined the schema; in the Family name column, enter the column families you want to create or use to group the columns in the Column column. For further information about a column family, see Apache documentation at Column families.

Advanced settings

Use batch mode	Select this check box to activate the batch mode for data processing.

Usage

Usage rule	This component is used as an end component and requires an input link. This component uses a tMapRDBConfiguration component present in the same Job to connect to MapR-DB.
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 uses a tMapRDBConfiguration component present in the same Job to connect to MapR-DB.

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.