# tHBaseInput properties for Apache Spark Batch These properties are used to configure tHBaseInput running in the Spark Batch Job framework. The Spark Batch tHBaseInput component belongs to the Databases family. The component in this framework is available in all subscription-based Talend products with Big Data and Talend Data Fabric. ## Basic settings | Properties | Description | | --- | --- | | Storage configuration | Select the tHBaseConfiguration component from which the
Spark system to be used reads the configuration information to connect to HBase. | | Property type | Either Built-In or Repository.

Built-In: No property data stored centrally.

Repository: Select the repository file where the
properties are stored. | | ![](https://help.qlik.com/talend/en-US/components/8.0/Resources/images/Save_Icon.png) | Click this icon to open a database connection wizard and store the
database connection parameters you set in the component Basic
settings view.

For more information about setting up and storing database connection
parameters, see [Centralizing database metadata](https://help.qlik.com/talend/r/en-US/8.0/studio-user-guide/centralizing-database-metadata). | | Schema and Edit schema |

A schema is a row description. It defines the number of fields (columns) to be processed and passed on to the next component. When you create a Spark Job, avoid the reserved word line when naming the fields.

Click Edit schema to make changes to the schema. If the current schema is of the Repository type, three options are available:
- View schema: choose this option to view the schema only.
- Change to built-in property: choose this option to change the schema to Built-in for local changes.
- Update repository connection: choose this option to change the schema stored in the repository and decide whether to propagate the changes to all the Jobs upon completion.
  
  If you just want to propagate the changes to the current Job, you can select No upon completion and choose this schema metadata again in the Repository Content window.
Built-In: You create and store the schema locally for this component only.
Repository: You have already created the schema and stored it in the Repository. You can reuse it in various projects and Job designs.

| | Table name | Type in the name of the table from which you need to extract columns. | | Mapping | Complete this table to map the columns of the table to be used with the schema columns you
have defined for the data flow to be processed. | | Is by filter | Select this check box to use filters to perform fine-grained data selection from your
database, such as selection of keys, or values, based on regular expressions.

Once selecting it, the Filter table that is used to
define filtering conditions becomes available.

This feature leverages filters provided by HBase and subject to constraints explained in
Apache HBase documentation. Therefore, advanced knowledge of HBase is required to make full
use of these filters. | | Logical operation | Select the operator you need to use to define the logical relation between filters. This
available operators are:

And: every defined filtering conditions must be satisfied. It represents the relationship FilterList.Operator.MUST_PASS_ALL
Or: at least one of the defined filtering conditions must be satisfied. It represents the relationship: FilterList.Operator.MUST_PASS_ONE

| | Filter | Click the button under this table to add as many rows as required, each row representing a
filter. The parameters you may need to set for a filter are:

Filter type: the drop-down list presents pre-existing filter types that are already defined by HBase. Select the type of the filter you need to use.
Filter column: enter the column qualifier on which you need to apply the active filter. This parameter becomes mandatory depending on the type of the filter and of the comparator you are using. For example, it is not used by the Row Filter type but is required by the Single Column Value Filter type.
Filter family: enter the column family on which you need to apply the active filter. This parameter becomes mandatory depending on the type of the filter and of the comparator you are using. For example, it is not used by the Row Filter type but is required by the Single Column Value Filter type.
Filter operation: select from the drop-down list the operation to be used for the active filter.
Filter Value: enter the value on which you want to use the operator selected from the Filter operation drop-down list.
Filter comparator type: select the type of the comparator to be combined with the filter you are using.

Depending on the Filter type you are using,
some or each of the parameters become mandatory. For further information, see [HBase filters](https://help.qlik.com/talend/en-US/components/8.0/hbase/thbaseinput-thbaseinput-hbase-filters-standard-component-bigdata-this) | | Partition by table regions | Select this check box to read in parallel the data from an HBase table using
its number of regions. | | Die on HBase error | Select the checkbox to stop the execution of the Job when an error
occurs.

Clear the checkbox to skip any rows on error and complete the process for
error-free rows. When errors are skipped, you can collect the rows on error using a Row > Reject link. | ## Usage | Usage guidance | Description | | --- | --- | | **Usage rule** | This component is used as a start component and requires an
output link.

This component uses a tHBaseConfiguration component present in the same Job to connect to
HBase.

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. |