tFileOutputParquet properties for Apache Spark Batch - Cloud

These properties are used to configure tFileOutputParquet running in the Spark Batch Job framework.

The Spark Batch tFileOutputParquet component belongs to the File family.

The component in this framework is available in all subscription-based Talend products with Big Data and Talend Data Fabric.

Basic settings

Define a storage configuration component	Select the configuration component to be used to provide the configuration information for the connection to the target file system such as HDFS. If you leave this check box clear, the target file system is the local system. The configuration component to be used must be present in the same Job. For example, if you have dropped a tHDFSConfiguration component in the Job, you can select it to write the result in a given HDFS system.
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. The fields that come after are pre-filled in using the fetched data. For further information about the Hadoop Cluster node, see Managing Hadoop connection 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. This component does not support the Object type and the List type. Spark automatically infers data types for the columns in a PARQUET schema. In a Talend Job for Apache Spark, the Date type is inferred and stored as `int96`. This component offers the advantage of the dynamic schema feature. This allows you to retrieve unknown columns from source files or to copy batches of columns from a source without mapping each column individually. For further information about dynamic schemas, see Dynamic schema. This dynamic schema feature is designed for the purpose of retrieving unknown columns of a table and is recommended to be used for this purpose only; it is not recommended for the use of creating tables.
	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.
Folder/File	Browse to, or enter the path pointing to the data to be used in the file system. The button for browsing does not work with the Spark Local mode; if you are using the other Spark Yarn modes that Talend Studio supports with your distribution, ensure that you have properly configured the connection in a configuration component in the same Job. Use the configuration component depending on the filesystem to be used.
Action	Select an operation for writing data: Create: creates a file and write data in it. Overwrite: overwrites the file existing in the directory specified in the Folder field. Append: add data to the existing file if data already exists. Ignore: leave the existing file as is if data already exists.
Compression	By default, the Uncompressed option is active. But you can select the Gzip or the Snappy option to compress the output data.
Merge result to single file	Select this check box to merge the final part files into a single file and put that file in a specified directory. Once selecting it, you need to enter the path to, or browse to the folder you want to store the merged file in. This directory is automatically created if it does not exist. The following check boxes are used to manage the source and the target files: Remove source dir: select this check box to remove the source files after the merge. Override target file: select this check box to override the file already existing in the target location. This option does not override the folder. If this component is writing merged files with a Databricks cluster, add the following parameter to the Spark configuration console of your cluster: `spark.sql.sources.commitProtocolClass org.apache.spark.sql.execution.datasources.SQLHadoopMapReduceCommitProtocol` This parameter prevents the merge file including the log file automatically generated by the DBIO service of Databricks. If you select the Define column partition check box from the Advanced settings, you must clear the Remove source dir check box. The partitioning applies only to the unmerged files.

Advanced settings

Define column partitions	Select this check box and complete the table that is displayed using columns from the schema of the incoming data. The records of the selected columns are used as keys to partition your data.
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, 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.

Usage rule

This component is used as an end component and requires an input link.

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.