tJMSInput properties for Apache Spark Streaming - 7.1

JMS

author
Talend Documentation Team
EnrichVersion
7.1
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 ESB
Talend Open Studio for MDM
Talend Real-Time Big Data Platform
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 tJMSInput running in the Spark Streaming Job framework.

The Spark Streaming tJMSInput component belongs to the Messaging family.

This component is available in Talend Real Time Big Data Platform and 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.

Message From

Type in the message source, exactly as expected by the server; this must include the type and name of the source. e.g.: queue/A or topic/testtopic

Note that the field is case-sensitive.

Timeout for Next Message (in sec)

Type in the number of seconds before passing to the next message.

Maximum Messages

Type in the maximum number of messages to be processed.

Message Selector Expression

Set your filter.

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 (columns) to be processed and passed on to the next component. When you create a Spark Job, avoid the reserved word line when naming the fields.

The schema of this component is read-only. You can click Edit schema to view the schema.

Advanced settings

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.

Usage

Usage rule

This component is used as a start component and requires an output 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.