tFlumeOutput
Acts as interface to integrate Flume and the Spark Streaming Job developed with the
Studio to continuously send data to a given Flume agent.
tFlumeOutput
receives RDDs from its preceding component, constructs Flume events out of these RDDs and
sends them to the source (input point) of a Flume agent.
tFlumeOutput properties for Apache Spark Streaming
These properties are used to configure tFlumeOutput running in the Spark Streaming Job framework.
The Spark Streaming
tFlumeOutput component belongs to the Messaging family.
The streaming version of this component is available in Talend Real Time Big Data Platform and in
Talend Data Fabric.
Basic settings
|
Host and Port |
Enter the hostname and the port of the machine used as the RPC client of the Flume system The RPC client of Flume allows tFlumeOutput to send data |
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Schema and Edit |
A schema is a row description. It defines the number of fields Built-In: You create and store the schema locally for this component Repository: You have already created the schema and stored it in the This read-only line column is used by tFlumeOutput to write the body of a Flume event. Note that you must The other columns are added as header to the event to be outputted. |
Advanced settings
|
Encoding |
Select the encoding from the list or select Custom and define it manually. This encoding is used by tFlumeOutput to encode the event |
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Connection pool |
In this area, you configure, for each Spark executor, the connection pool used to control
|
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Evict connections |
Select this check box to define criteria to destroy connections in the connection pool. The
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Usage
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Usage rule |
This component is used as an end component and requires an input link. This component, along with the Spark Streaming component Palette it belongs to, appears Note that in this documentation, unless otherwise explicitly stated, a scenario presents |
|
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. |
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Limitation |
Due to license incompatibility, one or more JARs required to use |
Related scenarios
No scenario is available for the Spark Streaming version of this component
yet.