# tMapRDBConfiguration properties for Apache Spark Batch These properties are used to configure tMapRDBConfiguration running in the Spark Batch Job framework. The Spark Batch tMapRDBConfiguration 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. ## 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.

The properties are stored centrally under the Hadoop
Cluster node of the Repository
tree. | | --- | --- | | Distribution and
Version | Select the MapR distribution to be used. Only MapR V5.2 onwards is supported
by the MapRDB components.

If the distribution you need to use with your MapRDB database is not
officially supported by this MapRBD component, that is to say, this distribution is MapR
but is not listed in the Version drop-down list of
this components or this distribution is not MapR at all, select Custom.

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.

In Talend Exchange, members of Talend community have shared some ready-for-use configuration zip files which you can download from this Hadoop configuration list and directly use them in your connection accordingly. However, because of the ongoing evolution of the different Hadoop-related projects, you might not be able to find the configuration zip corresponding to your distribution from this list; then it is recommended to use the Import from existing version option to take an existing distribution as base to add the jars required by your distribution.

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 .

| | 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 user name 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.

For further information about how Kerberos can be configured for your
database in a MapR cluster, see [Configuring Kerberos
Authentication](http://doc.mapr.com/display/MapR/Configure+Kerberos+Authentication+for+the+HBase+REST+Gateway). | | HBase Properties | 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 those corresponding ones defined
earlier for your database.

For example, you need to define the value of the *dfs.replication* property as *1* for the database configuration. Then you need to add one
row to this table using the plus button and type in the name and the
value of this property in this row. | ## Usage | **Usage rule** | This component is used only with the other MapRDB components to provide MapR-DB
connection to Spark. | | --- | --- | | 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 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-premise distributions, use the configuration component corresponding to the file system your cluster is using. Typically, this system is HDFS and so use .
Standalone mode: use the configuration component corresponding to the file system your cluster is using, such as tHDFSConfiguration or tS3Configuration.

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. |