Ongoing Tasks: Queue ETL Overview

  • Message brokers are high-throughput, distributed messaging services that host data they receive from producer applications and serve it to consumer clients via FIFO data queue/s.
  • RavenDB takes the role of a Producer in this architecture, via ETL tasks that -
    • Extract data from specified document collections.
    • Transform the data to new JSON objects.
    • Load the JSON objects to the message broker.
  • RavenDB wraps JSON objects as CloudEvents messages prior to loading them to the designated broker, using the CloudEvents Library.
  • Supported message brokers currently include Apache Kafka and RabbitMQ.

  • In this page:

Supported Message Brokers

Queue applications that RavenDB can currently produce data for include Apache Kafka and RabbitMQ.

Ongoing Tasks

Ongoing Tasks

  1. Ongoing Tasks
    Click to open the ongoing tasks view.
  2. Add a Database Task
    Click to create a new ongoing task.

Define ETL Task

Define ETL Task

  1. Kafka ETL
    Click to define a Kafka ETL task.
  2. RabbitMQ ETL
    Click to define a RabbitMQ ETL task.


After preparing a JSON object that needs to be sent to a message broker, the ETL task wraps it as a CloudEvents message using the CloudEvents Library.

To do that, the JSON object is provided with additional required attributes, added as headers to the message, including:

Attribute Type Description Default Value
Id string Event Identifier Document Change Vector
Type string Event Type "ravendb.etl.put"
Source string Event Context <ravendb-node-url>/<database-name>/<etl-task-name>

Optional Attributes

The optional partitionkey attribute can also be added. It is currently only implemented by Kafka.

Optional Attribute Type Description Default Value
partitionkey string events relationship/grouping definition Document ID

Task Statistics

Use Studio's ongoing tasks stats view to see various statistics related to data extraction, transformation, and loading to the target broker.

Queue Brokers Stats

Ongoing Tasks