The Spring Cloud Data Flow team is pleased to announce the GA release of 2.2.0.
In this GA release, Spring Cloud Data Flow team has worked on some of the key features including task application management, stability on the product by increasing the acceptance tests coverage across platforms (local, Kubernetes and Cloud Foundry), bug fixes and enhancements.
Task Application Management
It is now possible to stop and delete task executions using the SCDF Dashboard and the Shell.
We have added support for task application monitoring using micrometer integration. The core of the Micrometer integration landed in Spring Cloud Task’s 2.2.0 release-line, which by the way is a requirement if you are going to try out the Task-metrics and the SCDF integration.
For convenience, we have a sample application that builds on the compatible upstream versions of Spring Boot, Spring Batch, and Spring Cloud Task. With this application launched in SCDF, you are now able to instrument metrics with InfluxDB as the backend, and likewise visualize the statistics through Grafana dashboard as shown below.
Accessing the stream/task application logs
Spring Cloud Data Flow dashboard shows the stream and task applications logs. This feature is available in local, Kubernetes and Cloud Foundry platforms. Spring Cloud Data Flow’s dashboard now exposes the stream and task application logs. This functionality is powered by a new REST API available from Spring Cloud Data Flow.
You can watch this video for some of the dashboard improvements:
Patching Database Drivers
The community members and the customers who attempted to patch Spring Cloud Data Flow with proprietary database drivers have had the option to clone and build the project locally.
There’s another option, though. The procedure is now documented for both Maven and Gradle users.
We improved our quality of Acceptance tests running against the supported platforms Local, Pivotal Cloud Foundry, Kubernetes (GKE and PKS). This gives us confidence in shipping quality product which we continue to embrace.
New recipes are added to help developers configuring and managing the stream applications with Apache Kafka, RabbitMQ and Amazon Kinesis. There is also a recipe on File ingest and ETL processing on Kubernetes and Cloud Foundry. See Spring Cloud Data Flow website. Given all the content is coded as markdown files, it is easy for the community to contribute, so feel free to start the dialog with pull-requests - we are looking forward to your contributions here