Spring Framework 4.1.3 released

On behalf of the team I am pleased to announce the immediate availability of Spring Framework 4.1.3.

Spring Framework 4.1.3 is our third maintenance release in the 4.1.x line and comes with over 50 fixes and improvements. It was initially scheduled for late December but we decided to release it early to incorporate user-suggested and user-contributed improvements right in time for the scheduled Spring Boot 1.2 release this week.

We are still planning on another release later this month (4.1.4) at which point 4.1.x will turn into a maintenance branch, with active development on 4.2 happening on master.

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SpringOne2GX 2014 Replay: Spring XD - A Guided Tour

Spring XD - A Guided Tour

Recorded at SpringOne2GX 2014.

Speakers: Patrick Peralta, David Turanski

Big Data Track

Slides: http://www.slideshare.net/SpringCentral/spring-xd-guided-tour

What happens when a Stream is deployed to a Spring XD cluster? How does Stream processing and data partitioning work? How does the cluster recover when a Spring XD container goes down? How does Spring XD create and manage application contexts? What is a Plugin? How does Spring XD support extensibility? Our experienced guides will take you on a tour of the Spring XD runtime environment, navigating Streams and observing how Modules thrive in their natural habitat. We will explore the role of ZooKeeper, Spring Integration, and Spring Boot through beautiful panoramas, code samples, and daring demonstrations.

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SpringOne2GX 2014 Replay: Implementing the Lambda Architecture with Spring XD

Recorded at SpringOne2GX 2014.

Speaker: Carlos Queiroz

Slides: http://www.slideshare.net/SpringCentral/spring-one2gx-2014carlosqueiroz

Big Data Track

The lambda architecture has been proposed as a general purpose data system that aims to solve the problem of computing arbitrary functions on an arbitrary dataset in (near) real-time. In this talk we introduce the lambda architecture and show how it can be implemented with SpringXD, GemFireXD and Hadoop (HDFS and MapReduce) as the foundation of the architecture implementation. To validate the architecture we introduce a CDR (Call Detail Record) mining application as use case of the lambda architecture. We finalise by showing a demo of the CDR (Call Detail Record) mining application.

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SpringOne2GX 2014 Replay: Spring XD for Real-time Hadoop Workload Analysis

Recorded at SpringOne2GX 2014.

Speakers: Vineet Goel, Girish Lingappa, Rodrigo Meneses

Slides: http://www.slideshare.net/SpringCentral/spring-one2gx-2014springxdhadoopworkloadanalysis

Big Data Track

As Hadoop goes mainstream in enterprise big data deployments, IT organizations expect and demand enhanced operational management of their Hadoop clusters in production. Admins require more than just cluster health monitoring; they need the ability to do real time workload analysis for performance tuning and troubleshooting. Real-time log analysis of jobs at a user or application level can allow admins to manage and tune workloads better, especially in multi-tenancy Hadoop cluster services. Join us to learn how Pivotal team leveraged Spring XD data ingestion and batch processing framework, GemFire XD & other components to solve this interesting challenge on a large 1000-node (Analytics Workbench) cluster. Using Spring XD to ingest YARN service and MapReduce application logs through a real-time data pipeline into HDFS, the team leveraged familiar SQL-based queries to analyze fine-grained cluster utilization.

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SpringOne2GX 2014 Replay: Develop powerful Big Data Applications easily with Spring XD

Recorded at SpringOne2GX 2014.

Speakers: Mark Fisher and Mark Pollack

Slides: http://www.slideshare.net/SpringCentral/develop-powerful-big-data-applications-easily-with-springxd

Big Data Track

Spring XD aims to provide a one stop shop for writing and deploying Big Data Applications. It provides a scalable, fault tolerant, distributed runtime for Data Ingestion, Analytics, and Workflow Orchestration using a single programming, configuration and extensibility model. By not requiring developers to rationalize all of this themselves across the many different solutions available today, Spring XD greatly reduces the inherent complexity of Big Data development. It's all built on proven projects like Spring Integration, and Spring Batch. You'll see for yourself how this heritage combines to provide a scalable runtime environment, that is easily configured and assembled via a simple DSL.

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Spring for Android 2.0.0.M2 released

I am pleased to announce that Spring for Android 2.0.0.M2 is now available in the Spring milestone repository. Highlights include:

  • Support for the Android port of HttpClient 4.3 via HttpComponentsClientHttpRequestFactory
  • Support for HttpClient 4.0 included with Android is now deprecated but will remain available through HttpComponentsAndroidClientHttpRequestFactory.
  • HTTP PATCH support in RestTemplate
  • The type conversion package from Spring Core is now available in Spring for Android Core.
  • Many improvements and fixes from Spring 3.2 are now merged into Spring for Android to bring baseline compatibility to Spring 3.2, while certain RestTemplate features from Spring 4.1 have been included to support the new HttpClient.
  • Other bug fixes and improvements
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