tHiveOutput properties in Spark Streaming Jobs - 6.1

Talend Components Reference Guide

EnrichVersion
6.1
EnrichProdName
Talend Big Data
Talend Big Data Platform
Talend Data Fabric
Talend Data Integration
Talend Data Management Platform
Talend Data Services Platform
Talend ESB
Talend MDM Platform
Talend Open Studio for Big Data
Talend Open Studio for Data Integration
Talend Open Studio for Data Quality
Talend Open Studio for ESB
Talend Open Studio for MDM
Talend Real-Time Big Data Platform
task
Data Governance
Data Quality and Preparation
Design and Development
EnrichPlatform
Talend Studio

Component family

Databases / Hive

 

Basic settings

Hive storage configuration

Select the tHiveConfiguration component from which you want Spark to use the configuration details to connect to Hive.

 

HDFS Storage configuration

Select the tHDFSConfiguration component from which you want Spark to use the configuration details to connect to a given HDFS system and transfer the dependent jar files to this HDFS system.

Schema and 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. The schema is either Built-In or stored remotely in the Repository.

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. Related topic: see Talend Studio User Guide.

 

 

Repository: You have already created the schema and stored it in the Repository. You can reuse it in various projects and Job designs. Related topic: see Talend Studio User Guide.

 

Output source

Select the type of the output data you want tHiveOutput to change:

  • Hive table: the Database field, the Table name field and the Table format list are displayed. You need to enter the related information about the Hive database to be connected to and the Hive table you need to modify.

    By default, the format of the output data is JSON, but you can change it to ORC or Parquet by selecting the corresponding option from the Table format list.

  • ORC file: the Output folder field is displayed and the Hive storage configuration list is deactivated, because the ORC file should be stored in your HDFS system hosting Hive. You need to enter the directory in which the output data is written.

 

Save mode

Select the type of changes you want to make regarding the target Hive table.

Usage in Spark Streaming Jobs

In a Talend Spark Streaming Job, it is used as a start component and requires an output link. The other components used along with it must be Spark Streaming components, too. They generate native Spark code that can be executed directly in a Spark cluster.

This component should use a tHiveConfiguration component present in the same Job to connect to Hive.

This component, along with the Spark Streaming component Palette it belongs to, appears only when you are creating a Spark Streaming Job.

Note that in this documentation, unless otherwise explicitly stated, a scenario presents only Standard Jobs, that is to say traditional Talend data integration Jobs.

Log4j

If you are using a subscription-based version of the Studio, the activity of this component can be logged using the log4j feature. For more information on this feature, see Talend Studio User Guide.

For more information on the log4j logging levels, see the Apache documentation at http://logging.apache.org/log4j/1.2/apidocs/org/apache/log4j/Level.html.

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, one and only one file system related component from the Storage family is required in the same Job so that Spark can use this component to connect to the file system to which the jar files dependent on the Job are transferred:

This connection is effective on a per-Job basis.