Spring Cloud Data Flow 2.3.0 GA released, Spring Cloud Data Flow is to build real-time data integration and data processing pipeline toolkit.
Version 2.3.0 offers many enhancements and changes to universal compatibility Spring Boot 2.2.x and Spring Cloud Hoxton.
Continued deployment tasks
With the development of application tasks more quickly to adapt to business needs, we need to be able to automatically use the new version features by Data Flow. Although the Data Flow task to support multiple versions of the registration application functionality over previous iterations, but lacks the command to remix previously used in the execution of line parameters, properties, and deploy applications to run their functional properties in a practical way. In this release, store and retrieve these values in the value list to determine whether the application needs to be upgraded and applied to the implementation of the new capability enables developers to create continuous deployment process for their mission-critical applications. All of these features can be easily obtained through RESTful API, so you can automate the entire CI / CD workflow Task of.
Improved Scheduler
Scheduling has been updated to support the mission of CI / CD function. Therefore, when Kubernetes or Cloud Foundry When Task-scheduler launch the application in the Task user application updates to the new version, the next Spring Cloud Data Flow in the first execution will automatically take advantage of these updates.
Improved monitoring
- Use Prometheus RSocket proxy as the default method, based on Prometheus mission to monitor short-term and long-term streaming applications on all supported platforms.
- The machine monitoring Spring Cloud Tasks Spring Cloud Batch and is complementary to the existing monitoring support via convection Spring Cloud Streams application program.
- We have some monitoring samples. You will find instructions on building a data flow monitoring architecture integrated custom workflows and tasks applications:
- stream-apps - How to enable monitoring of custom-built source, and the receiver processor applications.
- task-apps - how to enable monitoring of custom-built application tasks.
- Monitoring architecture allows to achieve flexibility autoscale flow conduit adapter.
Kubernetes deployment improvements
- Support Node Affinity, Pod Affinity and Anti-Affinity
- The ability to add multiple ports to service objects
- Allows custom containers used to deploy the mirror StatefulSet
- Extended API implementation
- Support for custom initialization container
For more updates, see:
https://spring.io/blog/2019/12/09/spring-cloud-data-flow-2-3-0-ga-released