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Hitachi Vantara Lumada and Pentaho Documentation

Kafka Consumer

The PDI client can pull streaming data from Kafka through a Kafka transformation. The parent Kafka Consumer step runs a child (sub-transformation) that executes according to message batch size or duration, letting you process a continuous stream of records in near real-time. The child transformation must start with the Get records from stream step.

You can configure the Kafka Consumer step to continuously ingest streaming data from your Kafka server. Depending on your setup, you can execute the transformation within PDI or within the Adaptive Execution Layer (AEL), using Spark as the processing engine. 

If you are using Spark as the processing engine, you must execute the child transformation according to Duration (ms) only.

In the Kafka Consumer step itself, you can define the number of messages to accept for processing, as well as the specific data formats to stream activity data and system metrics. You can set up this step to collect monitored events, track user consumption of data streams, and monitor alerts.

Additionally, from the Kafka Consumer step, you can select a step in the child transformation to stream records back to the parent transformation. This allows records processed by a Kafka Consumer step in a parent transformation to be passed downstream to any other steps included within the same parent transformation. 

Kafka records are stored within topics, and consist of a category to which the records are published. Topics are divided into a set of logs known as partitions. Kafka scales topic consumption by distributing partitions among a consumer group. A consumer group is a set of consumers sharing a common group identifier.

Before using the Kafka Consumer step, you must select and configure the shim for your distribution. For information on configuring a shim for a specific distribution, see Set Up Pentaho to Connect to a Hadoop Cluster.

Since the Kafka Consumer step continuously ingests streaming data, you may want to use the Abort step in either the parent or child transformation to stop consuming records from Kafka for specific workflows. For example, you can run the parent transformation on a timed schedule, or abort the child transformation if sensor data exceeds a preset range.


The Kafka Consumer step requires definitions for setup, batch, field, result fields, and Kafka-specific options to stream messages. 


Enter the following information in the Step name and Transformation fields:

Option Description

Step name

Specifies the unique name of the step on the canvas. The Step name is set to Kafka Consumer by default.


Specify the child transformation to execute by performing any of the following actions:

The selected child transformation must start with the Get Records from Stream step.

If you select a transformation that has the same root path as the current transformation, the variable ${Internal.Entry.Current.Directory} is automatically inserted in place of the common root path. For example, if the current transformation's path is /home/admin/transformation.ktr and you select a transformation in the directory /home/admin/path/sub.ktr, then the path is automatically converted to ${Internal.Entry.Current.Directory}/path/sub.ktr.

If you are working with a repository, you must specify the name of the transformation. If you are not working with a repository, you must specify the XML file name of the transformation.

Transformations previously specified by reference are automatically converted to be specified by the transformation name in the Pentaho Repository.

Create and Save a New Child Transformation

If you do not already have a child transformation, you can create one while setting up the Kafka Consumer step. When you click the New button, a new child transformation will automatically generate the required Get Records from Stream step in a new canvas tab. All your fields and types are customized in the child transformation's Get Records from Stream step to match the fields and types specified in the Fields tab of the parent Kafka Consumer step. 

  1. In the Kafka Consumer step, click New. The Save As dialog box appears.
  2. Navigate to the location where you want to save your new child transformation, then type in the file name.
  3. Click Save. A notification box displays informing you that the child transformation has been created and opened in a new tab. If you do not want to see this notification in the future, select the Don't show me this again check box.
  4. Click the new transformation tab to view and edit the child transformation. It automatically contains the Get Records from Stream step. Optionally, you can continue to build this transformation and save it.
  5. When finished, return to the Kafka Consumer step.


The Kafka Consumer step features several tabs. Each tab is described below.

Setup Tab


In this tab, define the connections used for receiving messages, topics to which you want to subscribe, and the consumer group for the topics.

Option Description


Select a connection type:

  • Direct - Specify the Bootstrap servers from which you want to receive the Kafka streaming data.
  • Cluster - Specify the Hadoop cluster configuration from which you want to retrieve the Kafka streaming data. In a Hadoop cluster configuration, you can specify information like host names and ports for HDFS, Job Tracker, security, and other big data cluster components. Multiple servers can be specified if these are part of the same cluster. For information on Hadoop clusters, see Set Up Pentaho to Connect to a Hadoop Cluster


Enter the name of each Kafka topic from which you want to consume streaming data (messages). You must include all topics that you want to consume.

Consumer group

Enter the name of the group of which you want this consumer to be a member.  Each Kafka Consumer step will start a single thread for consuming.

When part of a consumer group, each consumer is assigned a subset of the partitions from topics it has subscribed to, which locks those partitions.  Each instance of a Kafka Consumer step will only run a single consumer thread.


Batch Tab


Use this tab to designate how many messages to consume before processing. You can specify message count and/or a specific amount of time.

While either option will trigger consumption, the first satisfied option will start the transformation for the batch.

If you are using Spark as the processing engine, you must execute the sub-transformation according to Duration (ms) only.

Option Description

Duration (ms)

Specify a time in milliseconds. This value is the amount of time the step will spend collecting records prior to the execution of the transformation.

You must set this field if you are using Spark as your processing engine.

If set to a value of ‘0’, then Number of records triggers consumption.

Number of records

Specify a number. After every ‘X’ number of records, the specified transformation will be executed and these ‘X’ records will be passed to the transformation.

If set to a value of ‘0’ then Duration triggers consumption.

Offset Management Select an option to specify when the offset is moved.
  • Commit when record read – Moves the offset when the record is read (default).
  • Commit when batch completed – Moves the offset when the sub-transformation has finished processing. If the the sub-transformation fails, the offset is not moved forward.

Either the Duration or the Number of records option  must contain a value greater than ‘0’ to run the transformation.

Fields Tab


Use this tab to define the fields in the record format.

Option Description

Input name

The input name is received from the Kafka streams. The following are received by default:

  • key: Determines message distribution to partitions. If no key is present, messages are randomly distributed from partitions.
  • message: The individual message contained in a record. Each record consists of a key, a value, and a timestamp.
  • topic: The category to which records are published.
  • partition: An ordered sequence of records that is continuously appended. You cannot have more consumers than the number of partitions.
  • offset: A sequential ID number assigned to each record. It uniquely identifies each record within the partition.
  • timestamp: The time the message is received on the server.

Output name

The Output name can be mapped to subscriber and member requirements.


The Type field defines the data format for streaming the record. You must choose the same data type that produced the records. This field applies to the ‘key’ and ‘message’ input names. Options include:

  • String
  • Boolean
  • Number
  • Integer
  • Binary

Result fields Tab


Use this tab to select the step, from the child transformation, that will stream records back to the parent transformation. This allows records processed by a Kafka Consumer step in the parent transformation to be passed downstream to any other steps included within the same parent  transformation.

Option Description
Return fields from: Select the name of the step (from the child transformation) that will stream fields back to the parent transformation. The data values in these returned fields will be available to any subsequent downstream steps in the parent transformation. 

Options Tab


Use this tab to configure the property formats of the Kafka consumer broker sources.  A few of the most common property formats have been included for your convenience. You can enter any desired Kafka property. For further information on these input names, see the Apache Kafka documentation site:

Metadata Injection Support

All fields of this step support metadata injection. You can use this step with ETL Metadata Injection to pass metadata to your transformation at runtime.