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 Talend products with Big Data and Talend Data Fabric.

Important: Talend does not support the import of schema for complex data types such as array, struct and map.

Basic settings

Hive storage configuration	Select the tHiveConfiguration component from which you want Spark to use the configuration details to connect to Hive.
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.
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. ORC file: the Output folder field is displayed and the Hive storage configuration list is deactivated, because the ORC file should be stored in your HDFS system hosting Hive. You need to enter the directory in which the output data is written.
Save mode	Select the type of changes you want to make regarding the target Hive table. When filling a newly created table which is still empty, you must not only use the Append mode but also select the Retrieve table schema from Hive metastore check box.
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

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 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 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-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.

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 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-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.