The latest version of Diffusion™ contains new features,
performance enhancements and bug fixes.
Note: This is a preview release which adds a number of the new features that will be available in
the full release of Diffusion 6.6.
This preview is supported for production use, except for the Kafka adapter which is beta software.
This release adds support for the popular MQTT messaging protocol, enabling MQTT devices to publish and subscribe to Diffusion topics.
Now you can gather and distribute real-time data to Internet of Things (IoT) devices and other remote hardware,
while still making use of
Diffusion's advanced data-wrangling and security capabilities. There is no need to install any Diffusion code on the remote devices.
MQTT is supported for production use.
See MQTT support for full details.
The Python SDK added in Preview 1 now has been expanded to support creating and removing topics.
The Python SDK is now supported for use in production.
See the Python SDK overview.
Features added in Preview 1
All the features added in Preview 1 are also available in Preview 2.
- Topic view inserts
Topic view inserts are a new addition to the data processing capabilities of topic views. Topic views
enable you to mirror selected source topics to another part of the topic tree, creating reference topics.
With a topic view insert, you can now merge data from topics other than the selected source topic into JSON reference topics.
You can insert whole values, or partial data specified with JSON pointers.
Advanced features include the ability to derive paths from values, and chaining multiple inserts.
For more information, see Insert clause.
- Kafka adapter
This release includes a beta version of a new integrated adapter to interface with Apache Kafka.
The adapter can translate data from Kafka topics to Diffusion topics, and from Diffusion to Kafka.
The new adapter has expanded configuration options, including the ability to configure how imported topics are structured,
and to import topics that match a regular expression.
For more information, see Kafka adapter.
- Time series topic enhancements
You can now update a time series topic via the standard topic update API, treating a time series topic
as if it were a single topic with the same event type as the time series. This means that when you update time series topics,
you can now use features like update constraints,
update streams and the addAndSet operation.
In addition, you can now create a time series event with a custom timestamp of your choice, instead of one based on the current
time. You could use this to load historical data into a time series topic, or for testing purposes.
- Other improvements
Session filters now support a new IN operator, so you can filter sessions based on a list of properties.
The Android SDK has been updated to take advantage of the latest desugaring support in Java 8+.
Update streams now respect topic conflation settings and can be used to update topics that with DONT_RETAIN_VALUE set to true.
The server will use less CPU when update traffic is very low.