Spring Team
Thomas Risberg

Thomas Risberg

Software Engineer focusing on Big Data

New Hampshire, USA

My current focus is on the "Spring Cloud Data Flow", "Spring for Apache Hadoop" and "Spring Data JDBC Extensions" projects. I'm a co-author of "Spring Data, Modern Data Access for Enterprise Java" published by O'Reilly Media in 2013 and "Professional Java Development with the Spring Framework" published by Wiley in 2005.
Blog Posts by Thomas Risberg

Spring Cloud Data Flow for Kubernetes 1.1 RC1 released

On behalf of the team, I am pleased to announce the release of the first release candidate of Spring Cloud Data Flow for Kubernetes 1.1.

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

The most significant change for this release can be found in the Spring Cloud Deployer for Kubernetes project. Thanks to community contributions from Donovan Muller and Rémon (Ray) Sinnema, we have added support for defining volumes and volume mounts for deployed apps. We support the volume types that have a model supported by the Fabric8 Kubernetes client’s kubernetes-model.

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Spring Cloud Data Flow for Kubernetes 1.1 M2 released

On behalf of the team, I am pleased to announce the release of the second milestone of Spring Cloud Data Flow for Kubernetes 1.1.

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

Note: A great way to start using this new release(s) is to follow the release matrix on the project page, which includes the download coordinates and the links to the reference guide.

The most significant changes for this release can be found in the Spring Cloud Deployer for Kubernetes project. Thanks to several community contributions, we have significantly improved the customization options available for launching Kubernetes apps. We now support resource requests in addition to resource limits and the imagePullPolicy can now be specified. You can also specify the startup command and the entryPoint type used for the Docker image as well as override exposed ports and specify environment variables when deploying apps. For detailed list of deployer improvements review the changes listed in the Spring Cloud Deployer for Kubernetes 1.1.0.M1 marker.

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Spring Cloud Data Flow for Kubernetes 1.1 M1 and 1.0.1 GA released

On behalf of the team, I am pleased to announce the release of the first milestone of Spring Cloud Data Flow for Kubernetes 1.1 along with a 1.0.1 maintenance release.

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

Note: A great way to start using this new release(s) is to follow the release matrix on the project page, which includes the download coordinates and the links to the reference guide.

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Spring Cloud Data Flow for Apache Mesos 1.0 GA released

On behalf of the team, I am excited to announce the General Availability of Spring Cloud Data Flow for Apache Mesos 1.0.

Spring Cloud Data Flow for Apache Mesos provides support for orchestrating long-running (streaming) and short-lived (task/batch) data microservices on Apache Mesos. We launch stream apps using Application Groups on Marathon and tasks as Chronos one-time jobs. The release includes a template JSON script for deploying the Spring Cloud Data Flow server on Marathon. We also include sample scripts for deploying Redis, MySQL and Rabbit MQ on Marathon to be used for testing a complete deployment. In addition to running on an Apache Mesos cluster, we also support running on a Mesosphere DC/OS cluster.

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Spring Cloud Data Flow 1.1 M1 and 1.0.1 GA released

On behalf of the team, I am excited to announce the release of the first milestone of Spring Cloud Data Flow 1.1 along with a 1.0.1 maintenance release for the 1.0 version.

Note: A great way to start using this new release(s) is to follow the release matrix on the project page, which includes the download coordinates and the links to the reference guide.

1.1 M1 release

Over the last few weeks, we have added new features and improvements to the overall orchestration of data microservices. The following new features were included in the 1.1.0.M1 release:

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Spring Cloud Data Flow for Mesos 1.0 RC2 released

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 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.
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Spring Cloud Data Flow for Kubernetes 1.0 GA released

We are pleased to announce the release of Spring Cloud Data Flow’s Kubernetes 1.0.0.RELEASE.

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

This project was originally conceptualized by the community and we are thankful to Florian Rosenberg for his early contributions that eventually made it into the official Spring Cloud Deployer for Kubernetes project. Building upon this theme, we recently bumped into Donovan Muller’s blog, where he walks through his experience extending the Spring Cloud Deployer project for OpenShift, paving the path for Spring Cloud Data Flow to orchestrate data microservices on OpenShift.

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Spring for Apache Hadoop 2.4.0 GA released

We are pleased to announce the Spring for Apache Hadoop 2.4.0 GA release.

In addition to version upgrades for Hadoop distros Spring for Apache Hadoop 2.4 adds the following improvements:

  • Add build support for HDP 2.4 [SHDP-557]
  • Create utils sub-project [SHDP-556]
  • Fix issue where NetworkUtils may find wrong default address [SHDP-546]
  • Missing trailing slash on spring.yarn.applicationDir path fails to localize [SHDP-562]

See the release changelog for details.

We continue to provide version specific artifacts with their respective transitive dependencies in the Spring IO milestone repository:

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Spring Cloud Data Flow for Apache Mesos 1.0.0.RC1 released

I am pleased to announce the 1.0.0.RC1 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 1.0.0.RC1 release of Spring Cloud Data Flow. Some highlights include:

  • Improves orchestration mechanics by inheriting latest improvements to spring-cloud-deployer contracts and the supported functionalities.
  • Builds upon feature-toggle support from Spring Cloud Data Flow’s core project to conditionally refine what features get included in each release
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Spring Cloud Data Flow for Kubernetes 1.0.0.RC1 released

I am pleased to announce the 1.0.0.RC1 release candidate of Spring Cloud Data Flow for Kubernetes, a team effort that encompasses many new features under the hood.

This release candidate builds upon the recent 1.0.0.RC1 release of Spring Cloud Data Flow and also adds Cloud Foundry specific refinements. Some highlights include:

  • Implements the TaskLauncher SPI to support launching of short-lived applications a.k.a tasks. They will be created as Kubernetes jobs and each task launch will create a new pod as part of the Kubernetes job
  • Adds support to configure command line args as a separate set of properties to be passed to an application when it’s deployed. This is an important requirement for running Spring Batch jobs orchestrated as tasks using Spring Cloud Task programming model
  • Adds support for RDBMS “datasource” to supplement persistent repository requirements
  • Adds /info API to query health checks and to supplement probing requirements
  • Health endpoint improvements to standardize probing over readiness of all the applications orchestrated in Kubernetes
  • Adds docker image for Spring Cloud Data Flow’s Kubernetes server so it can be deployed as a Kubernetes replication controller and service
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