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Spring Cloud Data Flow 1.6 RC1 released

The Spring Cloud Data Flow team is pleased to announce the release of 1.6 RC1. Follow the Getting Started guides for Local Server, Cloud Foundry, and Kubernetes.

Here are the release highlights:

PCF Scheduler

The Pivotal Cloud Foundry implementation of Scheduler improved on a few fronts to enhance the developer experience. Validation of the cron-expression and proactive measures to prevent the scheduler service from creating incorrect schedules is now part of this release.

Dashboard

The stream deployment history is available for review from the Dashboard. It is convenient to review the context-specific history of a stream from a central location; especially, when the CI/CD systems continually deploy new version application artifacts that belong to the stream.

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Spring REST Docs 2.0.2.RELEASE

It’s my pleasure to announce that Spring REST Docs 2.0.2.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 4 bug fixes and documentation improvements. It is a recommend upgrade for all Spring REST Docs 2.x users.

GitHub | Issues | Documentation | Twitter | Gitter | Stack Overflow

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Spring REST Docs 1.2.5.RELEASE

It’s my pleasure to announce that Spring REST Docs 1.2.5.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 handful of bug fixes and documentation improvements. It is a recommend upgrade for all Spring REST Docs 1.x users.

GitHub | Issues | Documentation | Twitter | Gitter | Stack Overflow

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This Week in Spring - July 17th, 2018

Hi Spring fans and welcome to another installment of This Week in Spring! Can you believe we’re already midway through the year?? Stunning.

This week I’m in San Francisco preparing for a video shoot next week and a joint presentatio with Google’s Ray Tsang at next week’s Google NEXT and tomorrow it’s off to Portland, OR, for the epic OSCON event where I’ll be doing a book signing at the O’Reilly booth on Wednesday and I’ll be doing a talk, Reactive Spring, on thursday. As usual, if you’re in the region and want to chat or grab a Voodoo doughnut, don’t hesitate to say hi!

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Spring Batch 4.1.0.M2 Released

We are pleased to announce that Spring Batch 4.1.0.M2 is now available on Github and the Pivotal download repository. Many thanks to all of those who contributed to this release!

What’s new?

Here are the highlights of this release:

  • Simplify remote partitioning
  • Add a new JSON item writer
  • Add support for validating items with the Bean Validation API

Simplify remote partitioning

In the 4.1.0.M1 release, we created new APIs to simplify the configuration of a remote chunking step. In this milestone, we continued this effort to simplify remote partitioning through two new builders: RemotePartitioningMasterStepBuilder and RemotePartitioningWorkerStepBuilder.

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Spring Cloud Data Flow 1.6 M2 released

The Spring Cloud Data Flow team is pleased to announce the release of 1.6 M2. Follow the Getting Started guides for Local Server, Cloud Foundry, and Kubernetes.

Here are the highlights

  • Task Scheduling on PCF

  • Angluar 6 update

  • App Hosting Tool

Task Scheduling on PCF

We are happy to introduce the native integration of PCF Scheduler in the SCDF for Cloud Foundry implementation!

A typical workflow for batch data processing involves scheduling batch applications. For example, the scheduler system accepts a cron expression and launches the application whenever the expression matches the current time.

Data Flow provides the ability to schedule and unschedule a task definition. The schedule is based on a cron expression. Building upon the PCF Java Client the team has created a portable scheduler interface in the Spring Cloud Scheduler SPI project (Service Provider Interface) and an implementation for PCF, Spring Cloud Scheduler for Cloud Foundry. The Dashboard provides access to schedule and unschedule a task as shown in the screenshot below.

Create Schedule
List and Delete Schedules
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Spring Cloud Stream Elmhurst.SR1 Released

On behalf of the team, I am pleased to announce the availability of Spring Cloud Stream Elmhurst.SR1 release train. This is the first maintenance release on the Elmhurst line and includes several bug fixes and minor enhancements in core Spring Cloud Stream (2.0.1.RELEASE), Kafka binder (2.0.1.RELEASE), and the Rabbit binder (2.0.1.RELEASE). Please take a look at the release notes for more details. The release can be found in maven central. Detailed descriptions can be found in the reference documentation.

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This Week in Spring - July 10th, 2018

Hi Spring fans! Welcome to another installment of This Week in Spring! This week I’m in sunny (warm!) Los Angeles for the epic SpringOne Tour Los Angeles event and then it’s off to Boston for meetings. We’ve got a lot to cover, as usual, so let’s get to it!

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Spring Cloud Data Flow 1.6 M1 and 1.5.2 released

The Spring Cloud Data Flow team is pleased to announce the 1.6 M1 release and 1.5.2 release.

For 1.6 M1, follow the Getting Started guides for Local Server, Cloud Foundry, and Kubernetes.

For 1.5.2, follow the Getting Started guides for Local Server, Cloud Foundry, and Kubernetes.

Areas of improvement for 1.6 M1:

  • DSL and deployment property parsing

  • Task Execution status

  • Composed Task Runner security

  • Dashboard

  • Kubernetes deployments

DSL and deployment property parsing

Launching Tasks with custom arguments is a great approach to influence the Task application with differing behaviors at runtime. Imagine influencing the batch-job (running as a Task) that accepts timezone as an argument to perform timezone specific data processing. In this release, we have adapted the parsing logic to include key-value pairs as values. Thanks to the community for reporting, giving us feedback, and sharing of their use-cases.

While reviewing the parsing rules for in-line vs. property files based properties for stream and task definitions, the community has found a difference in behavior, and that we have documented it for general guidance.

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