tFlumeInput properties in Spark Streaming Jobs - 6.1

Talend Components Reference Guide

EnrichVersion
6.1
EnrichProdName
Talend Big Data
Talend Big Data Platform
Talend Data Fabric
Talend Data Integration
Talend Data Management Platform
Talend Data Services Platform
Talend ESB
Talend MDM Platform
Talend Open Studio for Big Data
Talend Open Studio for Data Integration
Talend Open Studio for Data Quality
Talend Open Studio for ESB
Talend Open Studio for MDM
Talend Real-Time Big Data Platform
task
Data Governance
Data Quality and Preparation
Design and Development
EnrichPlatform
Talend Studio

Warning

The streaming version of this component is available in the Palette of the studio on the condition that you have subscribed to Talend Real-time Big Data Platform or Talend Data Fabric.

Component Family

Messaging / Flume

 

Basic settings

Host and Port

Enter the hostname and the port of the machine used as the sink (the data output point bound to the channel of a Flume agent) to receive data from Flume.

  • If you select As Receiver from the Type drop-down list, this machine must be one of the machines on which a Spark worker runs and the hostname must be the same as the one used by the resource manager of the Spark cluster to be used.

  • If you select As Sink from the Type drop-down list, this machine must be a sink in a Flume agent and be accessible to the Spark cluster.

 

Type

Select the approach to read data from Flume.

  • As Receiver: this is the Push-based approach typically employed by Flume. In this approach, a machine from the Spark cluster is set up as an agent to receive data pushed by Flume and the Spark Streaming Job you are designing reads data from this agent.

  • As Sink: this is the Pull-based approach. In this approach, a machine is set up as sink to buffer data pushed by Flume and the Spark Streaming Job you are designing pulls data from this sink.

For further information about these two approaches, see https://spark.apache.org/docs/1.3.1/streaming-flume-integration.html.

 

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. The schema is either Built-In or stored remotely in the Repository.

Built-In: You create and store the schema locally for this component only. Related topic: see Talend Studio User Guide.

Repository: You have already created the schema and stored it in the Repository. You can reuse it in various projects and Job designs. Related topic: see Talend Studio User Guide.

This read-only line column is used by tFlumeInput to automatically extract the body of an input Flume event and construct an RDD along with the other columns used to store the header of the same event.

Advanced settings

Encoding

Select the encoding from the list or select Custom and define it manually.

This encoding is used by tFlumeInput to decode the input event arrays.

Usage in Spark Streaming Jobs

In a Talend Spark Streaming Job, it is used as a start component and requires an output link. The other components used along with it must be Spark Streaming components, too. They generate native Spark code that can be executed directly in a Spark cluster.

At runtime, the tFlumeInput component keeps listening to the sink and reads new events once they are buffered in this sink.

This component, along with the Spark Streaming component Palette it belongs to, appears only when you are creating a Spark Streaming 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.

Log4j

If you are using a subscription-based version of the Studio, the activity of this component can be logged using the log4j feature. For more information on this feature, see Talend Studio User Guide.

For more information on the log4j logging levels, see the Apache documentation at http://logging.apache.org/log4j/1.2/apidocs/org/apache/log4j/Level.html.

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, one and only one file system related component from the Storage family is required in the same Job so that Spark can use this component to connect to the file system to which the jar files dependent on the Job are transferred:

This connection is effective on a per-Job basis.

Limitation

Due to license incompatibility, one or more JARs required to use this component are not provided. You can install the missing JARs for this particular component by clicking the Install button on the Component tab view. You can also find out and add all missing JARs easily on the Modules tab in the Integration perspective of your studio. For details, see https://help.talend.com/display/KB/How+to+install+external+modules+in+the+Talend+products or the section describing how to configure the Studio in the Talend Installation Guide.