# tHBaseConfiguration properties for Apache Spark Batch These properties are used to configure tHBaseConfiguration running in the Spark Batch Job framework. The Spark Batch tHBaseConfiguration 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. | | --- | --- | | Distribution | Select the cluster you are using from the drop-down list. The options in the
list vary depending on the component you are using. Among these options, the following
ones requires specific configuration:

If available in this Distribution drop-down list, the Microsoft HD Insight option allows you to use a Microsoft HD Insight cluster. For this purpose, you need to configure the connections to the HD Insight cluster and the Windows Azure Storage service of that cluster in the areas that are displayed. For detailed explanation about these parameters, search for configuring the connection manually on Talend Help Center (https://help.talend.com).
If you select Amazon EMR, find more details about Amazon EMR getting started in Talend Help Center (https://help.talend.com).
The Custom option allows you to connect to a cluster different from any of the distributions given in this list, that is to say, to connect to a cluster not officially supported by Talend .

Select Import from existing version to import an officially supported distribution as base and then add other required jar files which the base distribution does not provide.
Select Import from zip to import the configuration zip for the custom distribution to be used. This zip file should contain the libraries of the different Hadoop elements and the index file of these libraries.

Note that custom versions are not officially supported by Talend . Talend and its community provide you with the opportunity to connect to custom versions from the Studio but cannot guarantee that the configuration of whichever version you choose will be easy, due to the wide range of different Hadoop distributions and versions that are available. As such, you should only attempt to set up such a connection if you have sufficient Hadoop experience to handle any issues on your own.

Information noteNote:
In this dialog box, the active check box must be kept selected so as to import the jar files pertinent to the connection to be created between the custom distribution and this component.

For a step-by-step example about how to connect to a custom distribution and share this connection, see Hortonworks.

| | HBase version | Select the version of the Hadoop distribution you are using. The available
options vary depending on the component you are using. | | Zookeeper quorum | Type in the name or the URL of the Zookeeper service you use to coordinate the transaction
between your Studio and your database. Note that when you configure the Zookeeper, you might
need to explicitly set the *zookeeper.znode.parent*
property to define the path to the root znode that contains all the znodes created and used
by your database; then select the Set Zookeeper znode
parent check box to define this property. | | Zookeeper client port | Type in the number of the client listening port of the Zookeeper service you are
using. | | Use kerberos
authentication | If the database to be used is running with Kerberos security, select this
check box, then, enter the principal names in the displayed fields. You should be
able to find the information in the *hbase-site.xml* file of the
cluster to be used.

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.

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.

If you need to use a Kerberos keytab file to log in, select Use a keytab to authenticate. A keytab file contains
pairs of Kerberos principals and encrypted keys. You need to enter the principal to
be used in the Principal field and the access
path to the keytab file itself in the Keytab
field. This keytab file must be stored in the machine in which your Job actually
runs, for example, on a Talend
Jobserver.

Note that the user that executes a keytab-enabled Job is not necessarily
the one a principal designates but must have the right to read the keytab file being
used. For example, the username you are using to execute a Job is *user1* and the principal to be used is *guest*; in this
situation, ensure that *user1* has the right to read the keytab
file to be used. | | HBase parameters | If you need to use custom configuration for your database, complete this table with the
property or properties to be customized. Then at runtime, the customized property or
properties will override the corresponding ones used by the Studio. | ## Usage | **Usage rule** | This component is used with no need to be connected to other
components.

You must drop tHBaseConfiguration along with the
HBase-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. | | --- | --- | | Prerequisites | Before starting, ensure that you have met the Loopback IP prerequisites expected by your
database.

The Hadoop distribution must be properly installed, so as to guarantee the interaction
with *Talend Studio*. The following list presents MapR related information for
example.

Ensure that you have installed the MapR client in the machine where the Studio is, and added the MapR client library to the PATH variable of that machine. According to MapR's documentation, the library or libraries of a MapR client corresponding to each OS version can be found under MAPR_INSTALL\ hadoop\hadoop-VERSION\lib\native. For example, the library for Windows is \lib\native\MapRClient.dll in the MapR client jar file. For further information, see the following link from MapR: http://www.mapr.com/blog/basic-notes-on-configuring-eclipse-as-a-hadoop-development-environment-for-mapr.

Without adding the specified library or libraries, you may encounter the following error: no MapRClient in java.library.path.
Set the -Djava.library.path argument, for example, in the Job Run VM arguments area of the Run/Debug view in the Preferences dialog box in the Window menu. This argument provides to the Studio the path to the native library of that MapR client. This allows the subscription-based users to make full use of the Data viewer to view locally in the Studio the data stored in MapR.

For further information about how to install a Hadoop distribution, see the manuals
corresponding to the Hadoop distribution you are using. | | 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 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.

Actually, this component is relevant with the traditional on-premise Hadoop distributions only.
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. |