tCassandraInput properties in Spark Batch 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 / Cassandra

 

Basic settings

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.

The schema of this component dose not support the Object type and the List type.

 

Keyspace

Type in the name of the keyspace from which you want to read data.

 

Column family

Type in the name of the column family from which you want to read data.

 

Selected column function

Select the columns about which you need to retrieve the TTL (time to live) or the writeTime property.

The TTL property determines the time for records in a column to expire; the writeTime property indicates the time when a record was created.

For further information about these properties, see Datastax's documentation for Cassandra CQL.

 

Filter function

Define the filters you need to use to select the records to be processed.

The component generates the WHERE ALLOW FILTERING clause using the filters you put and thus this filter function is subject to the limit of this Cassandra clause.

 

Order by clustering column

Select how you need to sort the retrieved records. You can select NONE so as not to sort the data.

 

Use limit

Select this check box to display the Limit per partition field, in which you enter the number of the rows to be retrieved starting from the first row.

Usage in Spark Batch Jobs

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

This component should use one and only one tCassandraConfiguration component present in the same Job to connect to Cassandra. More than one tCassandraConfiguration components present in the same Job fail the execution of the Job.

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