We are pleased to announce that Spring Cloud Task 1.0.3.RELEASE and 1.1.0.M1 are now available via Maven Central, Github and the Pivotal download repository. Many thanks to all of those who contributed to this release.

What's new in Spring Cloud Task 1.0.3.RELEASE

1.0.3.RELEASE represents the next minor release for the 1.0.x branch. It's a minor update that cleans up the dependency management within the project to be in alignment with how the rest of the Spring Cloud portfolio manages it's dependencies.

What's new in Spring Cloud Task 1.1.0.M1

Spring Cloud Task 1.1.0's main theme is to…

I am pleased to announce that the second milestone for Spring for Apache Kafka version 1.1.0.M2 is now available in the spring milestone repo.

This includes some bug fixes and the following new features:

• The ability to process a batch of messages (introduced in the last milestone) is now available when using the @KafkaListener annotation, for example…​

  @KafkaListener(id = "list", topics = "myTopic", containerFactory = "batchFactory") public void listen(List list) { ... }

• You can now perform seek operations from the listener - this allows setting an initial offset when partitions are assigned by Kafka when using group management. You can also perform arbitrary seek operations after initialization.

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On behalf of the team, I am pleased to announce the release of the first release candidate of the Spring Cloud Stream Brooklyn release train. Spring Cloud Stream Brooklyn.RC1 is available for use in the Spring Milestone repository, a detailed description of its features can be found in the reference documentation. Release notes are available here and include important information on the migration path.

As this release follows closely the previous milestone release it contains a small number of fixes, and one major addition, which is support for Kafka 0.10 via drop-in dependency replacement.

We…

I'm pleased to announce the release of Spring Session 1.2.2.RELEASE. This release contains numerous bug fixes and trivial enhancements. Some of the highlights include:

• Fixes and improvements for supporting multiple sessions in the same browser when working with Tomcat 8.5 #605 #611 #615
• Improvements for JDBC support to work with DB2 #600
• Unregistered GemFire Instantiator causes deserialization issues on app restarts

Project Site | Reference | Help

It's my pleasure to announce that Spring REST Docs 1.1.2.RELEASE is available from Maven Central, JCenter, and our release repository. My thanks to everyone who contributed to this release by reporting bugs and opening pull requests.

This maintenance release includes a number of bug fixes and is a recommend upgrade for all Spring REST Docs users.

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On behalf of the team, I am pleased to announce that Milestone 1 of the Spring Cloud Camden Release Train is available today. The release can be found in our Spring Milestone repository. We’ve made numerous enhancements and bug fixes! You can check out the Camden.M1 release notes for more information.

The following modules were updated as part of Camden.M1:

Spring Cloud Build        1.2.0.RELEASE
Spring Cloud Stream       Brooklyn.M1
Spring Cloud Bus          1.2.0.M1
Spring Cloud Config       1.2.0.M1
Spring Cloud Netflix      1.2.0.M1
Spring Cloud Consul       1.1.0.M1
Spring Cloud…

On behalf of the team, I am pleased to announce the release of the first milestone of the Spring Cloud Stream Brooklyn release train. Spring Cloud Stream Brooklyn.M1 is available for use in the Spring Milestone repository, a detailed description of its features can be found in the reference documentation. Release notes are available here and include important information on the migration path.

From a Monolith to a Release Train

Spring Cloud Stream Brooklyn.M1 succeeds Spring Cloud Stream 1.0. The change in the naming scheme reflects the project's structural changes, in particular switching…

We are pleased to announce the 1.0.0.RC2 release candidate of Spring Cloud Data Flow for Mesos, a team effort that encompasses many new features under the hood.

This release candidate builds upon the recent 1.0 GA release of Spring Cloud Data Flow. Some highlights include:

• We now run the Spring Cloud Data Flow Server as a Docker image on Marathon, a container orchestration platform for Mesos.
• This release adds features to support stream partitioning and scaling

• Currently partitioning and scaling of sinks and processors are handled by using multiple application deployments, one for each app instance, identified by an index appended to the name.
• Scaling of sources is handled by using additional application instances.

* Streams are now deployed using Marathon [Application Groups](https://mesosphere.github.io/marathon/docs/application-groups.html) so it is easier to identify the different apps making up a stream. * We have added support for launching tasks using Chronos, a fault tolerant job scheduler for Mesos.

As part of this effort we have developed a simple Java client for interacting with the Chronos API. This Java client is included in the latest 1.0.2.RELEASE version of the Spring Cloud Deployer for Mesos project…

We are pleased to announce the general availability of Spring Cloud Data Flow for Cloud Foundry version 1.0.0.RELEASE.

Spring Cloud Data Flow for Cloud Foundry provides support for orchestrating long-running (streaming) and short-lived (task/batch) data microservices on Cloud Foundry runtime.

As the successor to Spring XD, this project benefits from a much more decoupled architecture, leveraging the Spring Cloud Deployer for Cloud Foundry library, which also goes GA today. More details about Spring Cloud Data Flow’s architecture and its ecosystem can be found in this blog.

• Stream and Batch/Task Processing are the primary functionalities in Spring Cloud Data Flow and they map to Cloud Foundry Diego’s LRPs and Tasks1 respectively.

• Includes developer toolkits to build streaming and batch/task pipelines using the DSL, Shell, REST-APIs, Dashboard, Flo Designer, or any combination of those.

• Facilitates test-driven-development at individual data pipeline components along with test fixtures to develop and test "data-centric" apps in isolation.

• Leverages Cloud Foundry’s runtime capabilities such as security, metrics, operational monitoring, scaling, and reliable execution of streaming and batch/task pipelines.

  …