# 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 subscription-based Talend products with Big Data and Talend Data Fabric. This component is not supported with Spark 4.0.x. ## Basic settings | Properties | Description | | --- | --- | | 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, enter the password in double quotes in the pop-up dialog box,
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 guidance | Description | | --- | --- | | **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 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. |