Spring Boot 1.5.1 released

On behalf of the Spring Boot team, and everyone that has contributed, I am pleased to announce that Spring Boot 1.5.1 has been released and is available now from repo.spring.io, Maven Central and Bintray. This release adds a significant number of new features and improvements. For full upgrade instructions and “new and noteworthy” features please see the release notes.

What’s new in 1.5

Apache Kafka Support

Spring Boot 1.5 includes auto-configuration support for Apache Kafka via the spring-kafka project. To use Kafka simply include the spring-kafka dependency and configure the appropriate spring.kafka.* application properties.


Webinar: Spring Boot 1.5 and Pivotal Cloud Foundry

Spring Boot and Pivotal Cloud Foundry users won’t want to miss Spring team’s Madhura Bhave and Pieter Humphrey as they tour through the Spring Boot 1.5 release.

Inspired in part by cool community open source work from codecentric.de, one of the hottest new directions that the two teams are working on is the integration of Spring Boot Actuators with Pivotal Cloud Foundry.

Attendees will be given direct linkage to product management - this is your chance to influence future integration direction! You’ll also walk away understanding all the highlights of the Spring Boot 1.5 release, including exciting improvements in Kafka and Pivotal Cloud Foundry support.


What's New in Spring Data Release Ingalls?

As you probably have seen, we have just announced the GA release of Spring Data release train Ingalls. As the release is packed with way too many features to cover them in a release announcement, I would like to use this post to take a deeper look at the changes and features that come with the 15 modules on the train.


A very fundamental change in the release train’s dependencies is the upgrade to Spring Framework 4.3 (currently 4.3.6) as the baseline. Other dependency upgrades are mostly driven by major version bumps of the underlying store drivers and implementations that need to be reflected in potential breaking changes to the API exposed by those modules.

Ingalls also ships with a new Spring Data Module: Spring Data LDAP. The Spring LDAP project has shipped Spring Data repository support for quite a while. After a couple of glitches and incompatibilities we decided to move LDAP repository support into a separate Spring Data module so that it is more closely aligned to the release train.

Another big change to the module setup is that Spring Data for Apache Cassandra has now become a core module, which means it now has been and is going to be maintained by the Spring Data team at Pivotal. A great chance to thank the previous core maintainers David Webb and Matthew T. Adams for all their efforts.

Besides those very fundamental changes, the team has been working on a whole bunch of new features:

  • Use of method handles for property access in conversion subsystem.

  • Support for XML and JSON based projections for REST payloads (Commons)

  • Cross-origin resource sharing with Spring Data REST

  • More MongoDB Aggregation Framework operators for array, arithmetic, date and set operations.

  • Support for Redis Geo commands.

  • Upgrade to Cassandra 3.0 with support for query derivation in repository query methods, User-defined types, Java 8 types (Optional, Stream), JSR-310 and ThreeTen Backport.

  • Support for Javaslang’s Option, collection and map types for repository query methods.

These are the ones that I would like to discuss in the remainder of this post.


SpringOne Platform 2016 Replay: Migrating from Spring XD to Spring Data Cloud Flow

Recorded at SpringOne Platform 2016.
Speakers: Katie Mooney; Director, Marketing Operations, zData; Cahlen Humphreys; Big Data Solutions Architect, zData & Dillon Woods; Chief Technology Officer, zData
Slides: http://www.slideshare.net/SpringCentral/lessons-learned-migrating-from-spring-xd-to-spring-data-cloud-flow-katie-mooney-dillon-woods-cahlen-humphreys

The goal of this session is to teach companies how they can use real-time data to make predictions with Spring Cloud Data Flow, Spark 2.0 and Spark ML. It will cover how to train your model using Spark ML in Spark 2.0. Once trained we will show you how to make real-time predictions using the model and Spring Cloud Data Flow.


SpringOne Platform 2016 Replay: Reactive Kafka

Recorded at SpringOne Platform 2016.
Speaker: Rajini Sivaram
Slides: http://www.slideshare.net/SpringCentral/reactive-kafka

Apache Kafka is a distributed, scalable, high-throughput messaging bus. Over the last few years, Kafka has emerged as a key building block for data-intensive distributed applications. As a high performance message bus, Kafka enables the development of distributed applications using the microservices architecture.

Reactive Streams simplifies the development of asynchronous systems using non-blocking back pressure. The reactive framework enables the development of asynchronous microservices by providing a side-effect free functional API with minimal overhead that supports low latency, non-blocking end-to-end data flows.


SpringOne Platform 2016 Replay: Task Madness - Modern on demand processing

Recorded at SpringOne Platform 2016.
Speakers: Michael Minella,Glenn Renfro
Slides: http://www.slideshare.net/SpringCentral/task-madness-modern-on-demand-processing

The flexibility promised by cloud computing is something that hasn’t been fully available in many use cases yet. Most application models are still based on long running containers, robbing users of that very flexibility. Spring Cloud Task, a new project in the Spring portfolio provides both functional and non-functional capabilities for building short lived, cloud-native, microservices. This talk will introduce the project as well as look at example applications. Since we’re in Vegas, we’re going to put that on demand processing to good use by demonstrating how we used it to generate a March Madness NCAA Men’s Basketball Tournament bracket. We’ll review how it worked, why Spring Cloud Task was the best fit for this type of application, and how we did this past March!


SpringOne Platform 2016 Replay: Secure & Dynamic App Config at GapTech with Spring Cloud, Vault and Consul

Recorded at SpringOne Platform 2016.
Speaker: Nivesh Gopathi, Gap Tech
Slides: http://www.slideshare.net/SpringCentral/secure-dynamic-app-config-at-gaptech

As organizations move to audit trails over managed processes, continuous delivery with aggressive MTTR, security first doctrines and ephemeral instances, a key enabler is a platform ability for dynamic application configuration and securely bootstrapping application secrets. In this session, we will go over what the use cases for dynamic configuration and application secrets management are with some high level requirements, how we are collaboratively solving for these at enterprise scale using Spring Cloud Config, Vault and Consul and what’s coming next.