tFlumeOutput properties for Apache Spark Streaming - 6.5

Flume

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

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 to be used.

The RPC client of Flume allows tFlumeOutput to send data to Flume. For further information about this RPC client, see the Flume documentation at https://flume.apache.org/FlumeDeveloperGuide.html.

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.

Built-In: You create and store the schema locally for this component only.

Repository: You have already created the schema and stored it in the Repository. You can reuse it in various projects and Job designs.

This read-only line column is used by tFlumeOutput to write the body of a Flume event. Note that you must define a same line column in the schema of the preceding component to send data to this read-only column.

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 arrays to be outputted.

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, 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.

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.

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 Installing external modules.