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tMapRStreamsInput properties for Apache Spark Streaming

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

The Spark Streaming tMapRStreamsInput component belongs to the Messaging family.

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

Basic settings

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.

Note that the schema of this component is read-only. It stores the messages sent from the message producer.

Output type

Select the type of the data to be sent to the next component.

Typically, using String is recommended, because tMapRStreamsInput can automatically translate the MapR Streams byte[] messages into strings to be processed by the Job. However, in case that the format of MapR Streams messages is not known to tMapRStreamsInput, such as Protobuf, you can select byte and then use a Custom code component such as tJavaRow to deserialize the messages into strings so that the other components of the same Job can process these messages.

Topic name

Enter the name of the topic from which tMapRStreamsInput receives the feed of messages. You must enter the name of the stream to which this topic belongs. The syntax is path_to_the_stream:topic_name.

Starting from

Select the starting point from which the messages of a topic are consumed.

In MapR Streams, the increasing ID number of a message is called offset. When a new consumer group starts, from this list, you can select beginning to start consumption from the oldest message of the entire topic, or select latest to wait for a new message.

Note that the consumer group takes into account only the offset-committed messages to start from.

Each consumer group has its own counter to remember the position of a message it has consumed. For this reason, once a consumer group starts to consume messages of a given topic, a consumer group recognizes the latest message only with regard to the position where this group stops the consumption, rather than to the entire topic. Based on this principle, the following behaviors can be expected:

  • If you are resuming an existing consumer group, this option determines the starting point for this consumer group only if it does not already have a committed starting point. Otherwise, this consumer group starts from this committed starting point. For example, a topic has 100 messages. If an existing consumer group has successfully processed 50 messages, and has committed their offsets, then the same consumer group restarts from the offset 51.

  • If you create a new consumer group or reset an existing consumer group, which, in either case, means this group has not consumed any message of this topic, then when you start it from latest, this new group starts and waits for the offset 101.

Set number of records per second to read from each Kafka partition

Enter this number in double quotation marks to limit the size of each batch to be sent for processing.

For example, if you put 100 and the batch value you define in the Spark configuration tab is 2 seconds, the size from a partition for each batch is 200 messages.

If you leave this check box clear, the component tries to read all the available messages in one second into one single batch before sending it, potentially resulting in Job that stops responding in case of a huge quantity of messages.

Advanced settings

Consumer properties

Add the MapR Streams consumer properties you need to customize to this table.

Custom encoding

You may encounter encoding issues when you process the stored data. In that situation, select this check box to display the Encoding list.

This encoding is used by tMapRStreamsInput to decode the input messages.


Usage rule

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

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.


The Hadoop distribution must be properly installed, so as to guarantee the interaction with Talend Studio . The following list presents MapR related information for example.

  • Ensure that you have installed the MapR client in the machine where Talend Studio is, and added the MapR client library to the PATH variable of that machine. According to MapR's documentation, the library or libraries of a MapR client corresponding to each OS version can be found under MAPR_INSTALL\ hadoop\hadoop-VERSION\lib\native. For example, the library for Windows is \lib\native\MapRClient.dll in the MapR client jar file.

    Without adding the specified library or libraries, you may encounter the following error: no MapRClient in java.library.path.

  • Set the -Djava.library.path argument, for example, in the Job Run VM arguments area of the Run/Debug view in the Preferences dialog box in the Window menu. This argument provides to Talend Studio the path to the native library of that MapR client. This allows the subscription-based users to make full use of the Data viewer to view locally in Talend Studio the data stored in MapR.

For further information about how to install a Hadoop distribution, see the manuals corresponding to the Hadoop distribution you are using.

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