tJMSOutput properties for Apache Spark Streaming - 7.1

JMS

author
Talend Documentation Team
EnrichVersion
7.1
EnrichProdName
Talend Big Data
Talend Big Data Platform
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Talend Data Integration
Talend Data Management Platform
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task
Data Governance > Third-party systems > Messaging components (Integration) > JMS components
Data Quality and Preparation > Third-party systems > Messaging components (Integration) > JMS components
Design and Development > Third-party systems > Messaging components (Integration) > JMS components
EnrichPlatform
Talend Studio

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

The Spark Streaming tJMSOutput 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

Module List

Select the library to be used from the list.

Context Provider

Type in the context URL, for example com.tibco.tibjms.naming.TibjmsInitialContextFactory. However, be careful, the syntax can vary according to the JMS server used.

Server URL

Type in the server URL, respecting the syntax, for example tibjmsnaming://localhost:7222.

Connection Factory JDNI Name

Type in the JDNI name.

Use Specified User Identity

If you have to log in, select the check box and type in your login and password.

To enter the password, click the [...] button next to the password field, and then in the pop-up dialog box enter the password between double quotes and click OK to save the settings.

Message Type

Select the message type, either: Topic or Queue.

To

Type in the message target, as expected by the server.

Processing Mode

Select the processing mode for the messages.

Raw Message or Message Content

Schema and Edit Schema

A schema is a row description, it defines the number of fields that will be processed and passed on to the next component.

The tJMSOutput schema is read-only. It is made of one column: message when the processing mode is Raw Message or messageContent when this mode is Message Content.

Since the message column requires valid JMS messages as input, you need to use a tJava component to write these JMS messages, while when the messageContent column is used, you can use a Write component to provide data.

Advanced settings

Delivery Mode

Select a delivery mode from this list to ensure the quality of data delivery:

Not Persistent: This mode allows data loss during the data exchange.

Persistent: This mode ensures the integrity of message delivery.

Use SSL/TLS

Select this check box to enable the SSL or TLS encrypted connection.

Then you need to use the tSetKeystore component in the same Job to specify the encryption information.

For further information about tSetKeystore, see tSetKeystore.

Properties

Click the plus button underneath the table to add lines that contains username and password required for user authentication.

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

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:
  • Yarn mode (Yarn client or Yarn cluster):
    • When using Google Dataproc, specify a bucket in the Google Storage staging bucket field in the Spark configuration tab.

    • When using HDInsight, specify the blob to be used for Job deployment in the Windows Azure Storage configuration area in the Spark configuration tab.

    • When using Altus, specify the S3 bucket or the Azure Data Lake Storage for Job deployment in the Spark configuration tab.
    • When using Qubole, add a tS3Configuration to your Job to write your actual business data in the S3 system with Qubole. Without tS3Configuration, this business data is written in the Qubole HDFS system and destroyed once you shut down your cluster.
    • When using on-premise distributions, use the configuration component corresponding to the file system your cluster is using. Typically, this system is HDFS and so use tHDFSConfiguration.

  • Standalone mode: use the configuration component corresponding to the file system your cluster is using, such as tHDFSConfiguration or tS3Configuration.

    If you are using Databricks without any configuration component present in your Job, your business data is written directly in DBFS (Databricks Filesystem).

This connection is effective on a per-Job basis.