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
message body sent from the message producer.

Starting offset

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.

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.

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

Enter this number within 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 hanging
in case of a huge quantity of messages.

Consumer properties

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

For further information about the consumer properties you can define in this table, see
the MapR Streams documentation at MapR Streams Overview.

Use hierarchical mode

Select this check box to map the binary (including hierarchical) Avro schema to the
flat schema defined in the schema editor of the current component. If the Avro
message to be processed is flat, leave this check box clear.

Once selecting it, you need set the following parameter(s):

• Local path to the avro
  schema: browse to the file which defines the
  schema of the Avro data to be processed.

• Mapping: create the map
  between the schema columns of the current component and the data stored
  in the hierarchical Avro message to be handled. In the
  Node column, you need to
  enter the JSON path pointing to the data to be read from the
  Avro message.

Usage rule

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

Spark Connection

This connection is effective on a per-Job basis.

Prerequisites

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 the 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
  hadoophadoop-VERSIONlib
  ative. For example, the library for
  Windows is lib
  ativeMapRClient.dll in the MapR
  client jar file. For further information, see the following link from MapR: http://www.mapr.com/blog/basic-notes-on-configuring-eclipse-as-a-hadoop-development-environment-for-mapr.

  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 the 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 the
  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.