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 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:
|
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:
This connection is effective on a per-Job basis. |