tMap properties for Apache Spark Streaming

tMap

author
Talend Documentation Team
EnrichVersion
6.4
EnrichProdName
Talend MDM Platform
Talend Open Studio for MDM
Talend Real-Time Big Data Platform
Talend Data Management Platform
Talend Open Studio for ESB
Talend Data Fabric
Talend Big Data
Talend Data Services Platform
Talend ESB
Talend Data Integration
Talend Open Studio for Data Integration
Talend Big Data Platform
Talend Open Studio for Big Data
task
Data Governance > Third-party systems > Processing components (Integration) > tMap
Design and Development > Third-party systems > Processing components (Integration) > tMap
Data Quality and Preparation > Third-party systems > Processing components (Integration) > tMap
EnrichPlatform
Talend Studio

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

The Spark Streaming tMap component belongs to the Processing family.

The component in this framework is available in Talend Real Time Big Data Platform and Talend Data Fabric.

Basic settings

Map editor

It allows you to define the tMap routing and transformation properties.

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 Talend Studio User Guide.

  • 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.

Usage

Usage rule

It usually works with a Lookup Input component such as tMongoDBLookupInput to construct and consume a lookup flow. In this situation, you must use Reload at each row or Reload at each row (cache) to read data from the lookup flow. This approach ensures that no redundant records are stored in memory before being sent to tMap. For a use case in which tMap is used with a Lookup Input component, see Reading and writing data in MongoDB using a Spark Streaming Job. Note that Reload at each row or Reload at each row (cache) in a streaming Job is supported by the Lookup Input components only.

Spark Connection

You need to use the Spark Configuration tab in the Run view to 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: when using Google Dataproc, specify a bucket in the Google Storage staging bucket field in the Spark configuration tab; when using other distributions, use a tHDFSConfiguration component to specify the directory.

  • Standalone mode: you need to choose the configuration component depending on the file system you are using, such as tHDFSConfiguration or tS3Configuration.

This connection is effective on a per-Job basis.