Thomas Risberg

Dear Spring community,

The Spring team hereby announces that the Spring for Apache Hadoop project will reach End-Of-Life status twelve months from today on April 5th, 2019. We will publish occasional 2.5.x maintenance releases as needed up until that point and will then move the project to the attic. The current Spring for Apache Hadoop 2.5.0 release is built using Apache Hadoop version 2.7.3 and should be compatible with the latest releases of the most popular Hadoop distributions.

The Spring Cloud Stream App Starter - HDFS Sink applications rely on the Spring for Apache Hadoop project. Moving forward, we will port the required capabilities directly onto the HDFS App Starter project. Spring Cloud Data Flow would continue to orchestrate the HDFS sink applications into coherent streaming pipelines.

On behalf of the team, I am pleased to announce the general availability of Spring Cloud Data Flow 1.2.3.

Local Server: Quick Start, Getting Started Guide

This is a maintenance release where we focused on improving the reference documentation. Other improvements include addition of an OSS licensed JDBC driver for Microsoft SQL Server. The latest local deployer version can now handle apps built using Spring Boot 2.0 milestone releases.

More details can be found in the release notes.

Feedback is important. Please reach out to us in StackOverflow and GitHub for questions and feature requests. We also welcome contributions! Any help improving the Spring Cloud Data Flow ecosystem is appreciated.

We are pleased to announce the Spring for Apache Hadoop 2.5.0 GA release.

The 2.5 version is primarily a bug fix and version upgrade release.

See the release changelog for details.

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

• 2.5.0.RC1 (default - Apache Hadoop stable 2.7.3)
• 2.5.0.RC1-cdh5 (Cloudera CDH 5.10)
• 2.5.0.RC1-hdp26 (Hortonworks HDP 2.6)

We discussed the future of the Spring for Apache Hadoop Project in the 2.5.0 RC1 announcement.

On behalf of the team, I am pleased to announce the general availability of Spring Cloud Data Flow 1.2.2.

Local Server: Quick Start, Getting Started Guide

Due to the popular demand from the community, the primary goal of this maintenance release is to bring Spring Boot 1.5.4 compatibility in Spring Cloud Data Flow.

• Since it includes refactoring efforts to adapt to a bug fix introduced in a Spring Data maintenance release that is pulled in by Spring Boot, this is not a simple version upgrade.
• As reported by the community, a side effect of this update surfaced pagination problems in the clients. It has been reworked both in the client and server-side.

New release candidate for Spring for Apache Hadoop 2.5

We are pleased to announce the Spring for Apache Hadoop 2.5 release candidate release.

The 2.5 version is primarily a bug fix and version upgrade release.

See the release changelog for details.

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

• 2.5.0.RC1 (default - Apache Hadoop stable 2.7.3)
• 2.5.0.RC1-cdh5 (Cloudera CDH 5.10)
• 2.5.0.RC1-hdp26 (Hortonworks HDP 2.6)

On behalf of the team, I am excited to announce the release of the third milestone of Spring Cloud Data Flow 1.2.

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.

Highlights of the 1.2 M3 release:

Companion Metadata Artifact

As part of the long awaited feature to improve access to app properties info for both shell and Dashboard, we are introducing a new optional artifact for both Stream and Task applications - we are calling it the “companion metadata artifact”. Through this functionality, the streaming and task applications and their properties are first-class citizens for both Docker and Maven based application artifacts.

On behalf of the team, I am excited to announce the release of the first milestone of Spring Cloud Data Flow 1.2.

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.

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 1.2.0.M1 release:

Core

• Introduce dedicated prefixes for deployment properties. Using the deployer properties is as simple as deployer.<appname>.xxx as opposed to app.<appname>.spring.cloud.deployer.xxx
• Introduce a new REST-API controller and shell support to cleanup Task Executions
• Foundation work to consolidate the use of controllers between Task deployments and Task Executions
• Consolidate REST-API call traces and return codes for consistency
• Adds role-based access control to define who has access to create, deploy, destroy, or view streams/tasks. This works seamlessly in coordination with the supported authentication methods.
• Performance optimizations to “stream list” operation. Instead of making individual calls for each app associated with the stream, the newly introduced MultiStateAppDeployer SPI operation invokes a call per stream that queries all the application statuses in a single network call
• Improves error reporting for “stream list” operation

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.

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.