tMQTTInput properties for Apache Spark Streaming - Cloud

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

The Spark Streaming tMQTTInput 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

Broker URL	Enter the location of the MQTT broker to be used to route the published messages to the subscriber (the tMQTTInput component).
Topic	Enter the topic you want tMQTTInput to subscribe to.
QoS	Enter, without quotation marks around, the numeric level of QoS (Quality of Service) to be assigned to the message to be used. This quality level indicates how responsive you want MQTT to be to the message delivery request: 0: it means a message may not be delivered or is delivered only once. 1: it means a message is delivered at least once. 2: it means a message is delivered exactly once. For further explanation about the different levels of QoS, see http://www-01.ibm.com/support/knowledgecenter/SSFKSJ_8.0.0/com.ibm.mq.dev.doc/q029090_.htm.
Include topic column	Select this check box to add a topic column to the schema to send the name of the topic along with its messages to the following component.
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. This read-only payload column is used to carry the body of the MQTT message to be processed. The input message body can use very different data formats. For example, if its format is JSON, you need to use tExtractJSONField following tMQTTInput to extract the data to be processed from this body.

Advanced settings

Encoding	Select the encoding from the list or select Custom and define it manually. This encoding is used by tMQTTInput to decode the input message arrays.

Encoding

Select the encoding from the list or select Custom and define it manually.

This encoding is used by tMQTTInput to decode the input message arrays.

Usage

Usage rule	This component is used as a start component and requires an output link. At runtime, the tMQTTInput component keeps listening to the topic and reads new messages once they are buffered in this topic. 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.
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 Talend Studio. For details, see Installing external modules.

Usage rule

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

At runtime, the tMQTTInput component keeps listening to the topic and reads new messages once they are buffered in this topic.

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.

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 Talend Studio. For details, see Installing external modules.