Spring Cloud Data Flow 1.5.0 Released
Spring Cloud Data Flow 1.5.0 Released
Spring Cloud Data Flow just came out with a sizable point release. Let's see what 1.5 offers for UI, Kubernetes, and Spring ecosystem support.
Join the DZone community and get the full member experience.Join For Free
How do you break a Monolith into Microservices at Scale? This ebook shows strategies and techniques for building scalable and resilient microservices.
Here Are the Highlights
- UI Improvements
- Spring Boot, Spring Cloud Stream 2.0, and Spring Cloud Task 2.0 Support
- Updated Application Starters
- Metrics Improvements
- Nested splits for Composed Tasks
- Kubernetes Improvements
- Updated File Ingest sample
We have continued to improve the UI/UX of the Dashboard. We hope that you will immediately notice an overall lighter weight design. The Tasks tab has been rewritten to match the UX styling of the other tabs. A new paginator component has been added to all the list pages. Switching from a list of 20, 30, 50, or 100 items per page is possible. This further simplifies the bulk operation workflows.
The updated Stream Builder tab makes it easy to deploy Stream Definitions and update deployed streams. You can edit application and deployment properties as well as change the version of individual applications in the stream and re-deploy. Data Flow’s integration with Skipper handles the upgrade process, allowing for easy rollback in case the upgrade doesn’t go as planned. The Stream Builder tab also includes many optimizations, including better form validation and eager error reporting. Try it out!
There has also been a significant amount of refactoring to optimize the code base and prepare for future extensions and feature additions. End-to-end testing with Selenium and SauceLabs has also been added.
Spring Boot and Spring Cloud Stream 2.0 Support
We now support deploying Spring Boot 2.0 and Spring Cloud Stream 2.0-based applications. Read the section on Metrics for related features.
Updated Application Starters
A new GA release of the Stream App Starters-Celsius.SR2 fixes a bug using Rabbit source/sink apps on PCF and updates the python apps. A new GA release of Task App Starters-Clark SR1 removes some outdated tasks and includes a new release of the composted task runner.
The release train Dearborn M1 updates the task application starters to be based on Spring Boot & Spring Cloud Task 2.0.
The Spring Cloud Stream Application Initializr has been updated to support customizing Darwin-based apps.
An updated Spring Cloud Stream Application Initializr now lets you add micrometer libraries to both Boot 1.5- and 2.0-based applications.
Another work in progress that is quite interesting to follow is the use of the Promregator project to monitor applications deployed on Cloud Foundry by using Prometheus. Follow these instructions to kick the tires.
The 2.0 RC1 release of Metrics Collector is based on Spring Boot 2.0 and Spring Cloud Stream 2.0. The Metrics Collector server supports collecting metrics from streams that contain only Boot 1.x or 2.x apps as well as streams that contain a mixture of Boot versions. A consistent representation of the throughput rates will be captured and propagated over to Data Flow’s Dashboard.
Nested Splits for Composed Tasks
Due to popular demand, this release added DSL support to interpret “nested splits” in composed tasks. The Flo Dashboard and Shell tooling automatically adapt to nested splits.
Here is how it looks in the Flo Dashboard for the DSL expression:
<<extractFromFTP && cleanseFiles || extractFromS3 && splitTransform> && merge || extractfromOracle>
To use this feature, you have to register the
1.1.1.RELEASE version of the Composed Task Runner in SCDF.
For Maven users:
And for Docker users:
- The client and the cluster version compatibility have improved due to Core Workload APIs going GA. For example, a StatefulSet deployment for a partitioned streaming-pipeline dynamically resolves the version compatibility.
- Extending the annotation support added to the “pod” configurations, it is now also possible to add custom annotations to “jobs” deployment.
- Deploying with custom liveness and readiness probe ports is now supported.
- While using Skipper with Data Flow, it is already possible to target application deployment to multiple platform backends. However, we did not support targeting multiple Kubernetes platforms. Now you can.
- The Kubernetes server now supports using a private Docker registry on a per-application basis.
Updated File Ingest sample
A common use case is to detect new files on an FTP site, download them, and launch a batch job. We have added a new File Ingest sample for this use case. In the coming months, we will continue to improve the design and features. You can follow along here.
Other Bits and Bobs
- A growing number of new issues dealt with the ability to individually and globally override
JAVA_OPTSfor applications running on Cloud Foundry. We added a deployer property (
deployer.yourapp.cloudfoundry.javaOpts) to support setting this specific environment variable.
- Switched to Hikari connection pool and restructure code to use fewer connections.
- Several bug fixes in underlying deployer libraries.
Stay in Touch…
Please try it out, share your feedback, and consider contributing to the project!
Published at DZone with permission of Mark Pollack . See the original article here.
Opinions expressed by DZone contributors are their own.