# tHiveConfiguration properties for Apache Spark Streaming (deprecated) Availability-note[Deprecated](https://help.talend.com/r/en-US/Cloud/labels-on-talend-help-center/availability-deprecated) This content is deprecated. These properties are used to configure tHiveConfiguration running in the Spark Streaming Job framework. Information noteDeprecation: Spark Streaming is deprecated beginning with the R2026-09 release. Use Spark Structured Streaming instead. The Spark Streaming tHiveConfiguration component belongs to the Storage family. This component is available in Talend Real-Time Big Data Platform and Talend Data Fabric. ## Basic settings | Properties | Description | | --- | --- | | Distribution and Version | Select the Hadoop distribution you are using for Hive.

Note that the Hive version required by Spark must be 0.13+.

Select the HBase version of the distribution you are using. | | Hive thrift
metastore | Enter the location of the metastore of the Hive system to be used by
specifying the name of its Host and the number
of its listening Port. If HA metastore has been
defined for this Hive system, select the Enable high
availability check box and in the field that is displayed, enter the
URIs of the multiple remote metastore services, each being separated with a
comma(,). | | Use Kerberos authentication | If you are accessing a Hive metastore running with Kerberos security,
select this checkbox.

Then you need to enter the Hive principal that should have been defined in
the *hive-site.xml* file of the cluster to be used.

Hive principal uses the value
of *hive.metastore.kerberos.principal*. This is
the service principal of the Hive metastore. | | Force MapR Ticket authentication | If this cluster is a MapR cluster of the version 5.0.0 or later, you can set the
MapR ticket authentication configuration in addition or as an alternative by following
the explanation in [Connecting to a security-enabled MapR](https://help.qlik.com/talend/en-US/components/8.0/mapr/connecting-to-security-enabled-mapr).

Keep in mind that this configuration generates a new MapR security ticket for the username
defined in the Job in each execution. If you need to reuse an existing ticket issued for the
same username, leave both the Force MapR ticket
authentication check box and the Use Kerberos
authentication check box clear, and then MapR should be able to automatically
find that ticket on the fly. | ## Usage | Usage guidance | Description | | --- | --- | | **Usage rule** | This component is used with no need to be connected to other components.

You need to drop tHiveConfiguration
along with the Hive-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 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. | | 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 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. |