# tJMSOutput properties for Apache Spark Streaming (deprecated) Availability-note[Deprecated](https://help.talend.com/r/en-US/Cloud/labels-on-talend-help-center/availability-deprecated) This content is deprecated. These properties are used to configure tJMSOutput running in the Spark Streaming Job framework. Information noteDeprecation: Spark Streaming is deprecated beginning with the R2026-09 release. Use Spark Structured Streaming instead. 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 | Properties | Description | | --- | --- | | 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, enter the password in double quotes in the pop-up dialog box,
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 | Properties | Description | | --- | --- | | 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 checkbox 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. | | 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 guidance | Description | | --- | --- | | **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 on-premises 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 Apache Spark Batch or tS3Configuration Apache Spark Batch.

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. |