tMapRStreamsOutput properties for Apache Spark Streaming

MapRStreams

author
Talend Documentation Team
EnrichVersion
6.5
EnrichProdName
Talend Open Studio for Big Data
Talend Real-Time Big Data Platform
Talend Big Data Platform
Talend Data Fabric
Talend Big Data
task
Data Governance > Third-party systems > Messaging components (Integration) > MapRStreams components
Data Quality and Preparation > Third-party systems > Messaging components (Integration) > MapRStreams components
Design and Development > Third-party systems > Messaging components (Integration) > MapRStreams components
EnrichPlatform
Talend Studio

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

The Spark Streaming tMapRStreamsOutput component belongs to the Messaging family.

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

Basic settings

Schema and Edit schema

A schema is a row description. It defines the number of fields (columns) to Repository. When you create a Spark Job, avoid the reserved word line when naming the fields.

Note that the schema of this component is read-only. It stores the messages to be published.

Topic name

Enter the name of the topic you want to publish messages to. This topic must already exist. You must enter the name of the stream to which this topic belongs. The syntax is path_to_the_stream:topic_name.

Compress the data

Select the Compress the data check box to compress the output data.

Advanced settings

Producer properties

Add the MapR Streams producer properties you need to customize to this table.

For further information about the producer configuration you can define in this table, see the section describing the important producer configuration properties for MapR Streams in MapR documentation at MapR Streams Overview.

Connection pool

In this area, you configure, for each Spark executor, the connection pool used to control the number of connections that stay open simultaneously. The default values given to the following connection pool parameters are good enough for most use cases.

  • Max total number of connections: enter the maximum number of connections (idle or active) that are allowed to stay open simultaneously.

    The default number is 8. If you enter -1, you allow unlimited number of open connections at the same time.

  • Max waiting time (ms): enter the maximum amount of time at the end of which the response to a demand for using a connection should be returned by the connection pool. By default, it is -1, that is to say, infinite.

  • Min number of idle connections: enter the minimum number of idle connections (connections not used) maintained in the connection pool.

  • Max number of idle connections: enter the maximum number of idle connections (connections not used) maintained in the connection pool.

Evict connections

Select this check box to define criteria to destroy connections in the connection pool. The following fields are displayed once you have selected it.

  • Time between two eviction runs: enter the time interval (in milliseconds) at the end of which the component checks the status of the connections and destroys the idle ones.

  • Min idle time for a connection to be eligible to eviction: enter the time interval (in milliseconds) at the end of which the idle connections are destroyed.

  • Soft min idle time for a connection to be eligible to eviction: this parameter works the same way as Min idle time for a connection to be eligible to eviction but it keeps the minimum number of idle connections, the number you define in the Min number of idle connections field.

Usage

Usage rule

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

This component needs a Write component such as tWriteJSONField to define a serializedValue column in the input schema to send serialized data.

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.

Prerequisites

The Hadoop distribution must be properly installed, so as to guarantee the interaction with Talend Studio . The following list presents MapR related information for example.

  • Ensure that you have installed the MapR client in the machine where the Studio is, and added the MapR client library to the PATH variable of that machine. According to MapR's documentation, the library or libraries of a MapR client corresponding to each OS version can be found under MAPR_INSTALL\ hadoop\hadoop-VERSION\lib\native. For example, the library for Windows is \lib\native\MapRClient.dll in the MapR client jar file. For further information, see the following link from MapR: http://www.mapr.com/blog/basic-notes-on-configuring-eclipse-as-a-hadoop-development-environment-for-mapr.

    Without adding the specified library or libraries, you may encounter the following error: no MapRClient in java.library.path.

  • Set the -Djava.library.path argument, for example, in the Job Run VM arguments area of the Run/Debug view in the [Preferences] dialog box in the Window menu. This argument provides to the Studio the path to the native library of that MapR client. This allows the subscription-based users to make full use of the Data viewer to view locally in the Studio the data stored in MapR.

For further information about how to install a Hadoop distribution, see the manuals corresponding to the Hadoop distribution you are using.