# tHiveOutput properties for Apache Spark Batch These properties are used to configure tHiveOutput running in the Spark Batch Job framework. The Spark Batch tHiveOutput 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. Information noteImportant: Talend does not support the import of schema for complex data types such as array, struct, and map. ## Basic settings | Properties | Description | | --- | --- | | Hive storage configuration | Select the tHiveConfiguration
component from which you want Spark to use the configuration details to connect to
Hive.

If you are running your Spark Job on Spark Universal with Yarn Cluster
mode, this property is not available. The Hive storage configuration
directly comes from the XML files inside the Hadoop configuration JAR
file. For more information, see [Defining Yarn cluster connection
parameters with Spark Universal](https://help.qlik.com/talend/r/en-US/8.0/studio-user-guide/defining-yarn-cluster-connection-parameters-with-spark-universal). | | HDFS Storage configuration | Select the tHDFSConfiguration component from which you want Spark to use the configuration
details to connect to a given HDFS system and transfer the dependent jar files to this HDFS
system. This field is relevant only when you are using an on-premises distribution.

If you are running your Spark Job on Spark Universal with Yarn Cluster
mode, this property is not available. The HDFS storage configuration
directly comes from the XML files inside the Hadoop configuration JAR
file. For more information, see [Defining Yarn cluster connection
parameters with Spark Universal](https://help.qlik.com/talend/r/en-US/8.0/studio-user-guide/defining-yarn-cluster-connection-parameters-with-spark-universal). | | 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.

| | Output source | Select the type of the output data you want tHiveOutput to change:

Hive table: the Database field, the Table name field, the Table format list and the Enable Hive partitions check box are displayed. You need to enter the related information about the Hive database to be connected to and the Hive table you need to modify.

By default, the format of the output data is JSON, but you can change it to ORC or Parquet by selecting the corresponding option from the Table format list.

| | Save mode | Select the type of changes you want to make regarding the target Hive table:

Create: creates the target Hive table and writes data in this table.
Append: adds data to an existing table.
Overwrite: overwrites the data of the existing table.
Create if it does not exist: creates a Hive table and adds data in it if there is no existing table. If there is an existing table, the input data is not saved.
This property is available when you have installed the 8.0.1-R2024-03 Studio Monthly or a later one provided by Talend. For more information, check with your administrator.

| | Enable Hive partitions | Select the Enable Hive partitions check box and in
the Partition keys table, define partitions for
the Hive table you are creating or changing. In the Partition
keys table, select columns from the input schema of
tHiveOutput to use as partition keys.

Bear in mind that:

When the Save mode to be used is Append, meaning that you are adding data to an existing Hive table, the partition columns you select in the Partition keys table must be already partition keys of the Hive table to be updated.
A partitioned Hive table created by tHiveOutput can only be read by tHiveInput due to Spark specific limitations. If you need to read your partitioned table through Hive itself, it is recommended to use tHiveRow or tHiveCreateTable in a Standard Job to create this table and then use tHiveOutput to append data to it.
Defining columns as partition keys does not alter your data but only create subfolders using the partition keys and put data in them.

| ## Advanced settings | Properties | Description | | --- | --- | | Sort columns alphabetically | Select this check box to sort the schema columns in the alphabetical order. If you leave this check box clear, these columns stick to the order defined in the schema editor. | ## Usage | Usage guidance | Description | | --- | --- | | **Usage rule** | This component is used as an end component and requires an input link.

This component should use a tHiveConfiguration component present in the same Job to connect to
Hive.

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. |