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tCassandraConfiguration properties for Apache Spark Batch

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

The Spark Batch tCassandraConfiguration component belongs to the Storage and the Databases families.

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

Basic settings

Property type

Either Built-In or Repository.

Built-In: No property data stored centrally.

Repository: Select the repository file where the properties are stored.

Host

Enter the URL of the Cassandra server you need the Job to connect to.

Port

Type in the listening port number of the Cassandra server to be connected to.

Username

Fill in this field with the username for the Cassandra authentication.

Password

Fill in this field with the password for the Cassandra authentication.

To enter the password, click the [...] button next to the password field, and then in the pop-up dialog box enter the password between double quotes and click OK to save the settings.

Configuration

Add the Spark properties relating to Cassandra to this table and give them the values you want to use in order to override the default ones at runtime.

  • For example, if you need to define the Cassandra consistency level for reading, select the input_consistency_level property in the Property name column and enter the numeric level value in the Value column.

The following list presents the numerical values you can put and the consistency levels they signify:

  • 0: ANY,

  • 1: ONE,

  • 2: TWO,

  • 3: THREE,

  • 4: QUORUM,

  • 5: ALL,

  • 6: LOCAL_QUORUM,

  • 7: EACH_QUORUM,

  • 8: SERIAL,

  • 9: LOCAL_SERIAL,

  • 10: LOCAL_ONE

For further details about each of the consistency policies, see Datastax documentation about Cassandra.

For further information for all the properties listed in this table and their default values, see https://github.com/datastax/spark-cassandra-connector/blob/master/doc/1_connecting.md.

Usage

Usage rule

This component is used with no need to be connected to other components.

You need to drop tCassandraConfiguration along with the Cassandra-related subJob to be run in the same Job so that the configuration is used by the whole Job at runtime.

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.

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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