tMapRDBOutput properties for Apache Spark Streaming - 7.0

MapRDB

author
Talend Documentation Team
EnrichVersion
7.0
EnrichProdName
Talend Big Data
Talend Big Data Platform
Talend Data Fabric
Talend Open Studio for Big Data
Talend Real-Time Big Data Platform
task
Data Governance > Third-party systems > Database components > MapRDB components
Data Quality and Preparation > Third-party systems > Database components > MapRDB components
Design and Development > Third-party systems > Database components > MapRDB components
EnrichPlatform
Talend Studio

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

The Spark Streaming tMapRDBOutput component belongs to the Databases family.

This component is available in Talend Real Time Big Data Platform 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 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.