tMap properties for Apache Spark Batch

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

The Spark Batch tMap component belongs to the Processing family.

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

Basic settings

Map editor	It allows you to define the tMap routing and transformation properties but note that only the Load once lookup model is supported by the Spark Batch Jobs. For further information about this Load once lookup model, see the related description of Handling Lookups. When you click the Property Settings button at the top of the input area, a Property Settings dialog box is displayed in which you can set the following parameters: If you do not want to handle execution errors, select the Die on error check box (selected by default). It will kill the Job if there is an error. To maximize the data transformation performance in a Job that handles multiple lookup input flows with large amounts of data, you can select the Lookup in parallel check box. Temp data directory path: enter the path where you want to store the temporary data generated for lookup loading. For more information on this folder, see Solving memory limitation issues in tMap use. Max buffer size (nb of rows): enter the size of physical memory, in number of rows, you want to allocate to processed data.
Mapping links display as	Auto: the default setting is curves links Curves: the mapping display as curves Lines: the mapping displays as straight lines. This last option allows to slightly enhance performance.
Preview	The preview is an instant shot of the Mapper data. It becomes available when Mapper properties have been filled in with data. The preview synchronization takes effect only after saving changes.
Use replicated join	Select this check box to perform a replicated join between the input flows. By replicating each lookup table into memory, this type of join doesn't require an additional shuffle-and-sort step, thus speeding up the whole process. You need to ensure that the entire lookup tables fit in memory.
Max buffer size (nb of rows)	Type in the size of physical memory, in number of rows, you want to allocate to processed data.

Usage

Usage rule	This component is used as an intermediate step. 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 intermediate step.

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.

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