Recorded at SpringOne Platform 2016. Speaker: Agim Emruli Slides: http://www.slideshare.net/SpringCentral/spring-cloud-on-aws

Spring Cloud allows to implement cloud native application across different cloud platforms. Spring Cloud has also a dedicated support for running applications on the Amazon Web Service platform. This talk will introduce common patterns and best-practices to run the application the AWS cloud and how to use the platform provided services efficiently.

Recorded at SpringOne Platform 2016. Speaker: Chris Richardson Slides: http://www.slideshare.net/SpringCentral/developing-microservices-with-aggregates

The Domain Model pattern is a great way to develop complex business logic. Unfortunately, a typical domain model is a tangled, birds nest of classes. It can’t be decomposed into microservices. Moreover, business logic often relies on ACID transactions to maintain consistency. Fortunately, there is a solution to this problem: aggregates. An aggregate is an often overlooked modeling concept from the must read book Domain Driven Design. In…

Recorded at SpringOne Platform 2016. Speakers: Marcin Grzejszczak, Reshmi Krishna Slides: http://www.slideshare.net/SpringCentral/implementing-microservices-tracing-with-spring-cloud-and-zipkin

2015 was the year of microservices. It’s already common knowledge that creating distributed systems is not easy. It’s 2016 and it’s time to show how that complexity can be contained.

Service Discovery and Registry (Zookeeper / Consul / Eureka), easy request sending with client side load balancing (Feign + Ribbon), request proxying with Zuul. Everything is easy with Spring Cloud. Just add a dependency…

Recorded at SpringOne Platform 2016. Slides: http://www.slideshare.net/SpringCentral/how-to-properly-blame-things-for-causing-latency

Latency analysis is the act of blaming components for causing user perceptible delay. In today's world of microservices, this can be tricky as requests can fan out across polyglot components and even data-centers. In many cases, the root source of latency isn't a component, but rather a link between components.

This session will overview how to debug latency problems, using call graphs created by Zipkin. We'll use trace zipkin itself, setting up from scratch…

Recorded at SpringOne Platform 2016. Speaker: Clint Checketts, Domo.com Slides: http://www.slideshare.net/SpringCentral/implementing-config-server-and-extending-it

Spring Cloud Config Server is a great tool for getting your application configuration under source control. We'll start with the quick overview of Spring's Environment and properties and how config server enhances those, while keeping the same model.

Next we'll discuss what concerns you need to take into account when rolling out config server like security, encryption, and location of your repositories (if using Git).

Finally we…

Recorded at SpringOne Platform 2016. Speaker: Tomas Lin, Netflix Slides: http://www.slideshare.net/SpringCentral/putting-a-spel-on-spinnaker-evolving-an-expression-language-for-continuous-delivery-at-netflix

Spinnaker is an open source, multi-cloud continuous delivery platform. It is used by over 90% of cloud deployments at Netflix. The Spring Expression Language (SpEL) is heavily leveraged to fully articulate and extend Spinnaker’s automated deployment pipelines. SpEL is used heavily to interact with pipeline stages and triggers for variable substitution, information retrieval and…

On behalf of the community, I am happy to announce the release of Spring Cloud Stream Brooklyn.SR2 and Chelsea.M1 release trains. As part of the next installment to Brooklyn release train, Brooklyn.SR2 adds a number of new features and fixes. The next release train Chelsea.M1 builds on top of Spring Boot 1.5 and a contains a core improvement listed below.

What is new?

A few highlights of the improved areas:

• Schema Registry Support: fixed issues related to using the Schema Registry server, in particular support for MySQL and Postgresql;
• Aggregate Applications: improved support for property propagation via namespaces;
• Reactive support: Aligns declarative (e.g. reactive) handler support with imperative mode, which makes it easier to seamlessly adopt a functional programming model. In particular, this adds support for the following idiom:
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On behalf of the Spring Integration team I’d like to announce that Spring AMQP 1.7.0.RELEASE is now available.

This release is mainly an intermediate version between 1.6.x and 2.0 for Spring Boot 1.5 and IO Platform Brussels dependencies compatibility.

However, there are several improvements and new features to explain here:

• Upgrade to Amqp Client 4.0 with appropriate RabbitConnectionFactoryBean changes

• Upgrade to Log4j 2.7 and required breaking change fix for the log4j2.AmqpAppender

• Upgrade to Spring Retry 1.2 with important StatefulRetryOperationsInterceptor.setUseRawKey(true) for backward compatibility

• a new spring-rabbit-junit artifact is provided with several utilities (like BrokerRunning @Rule) which can be useful for testing Spring AMQP applications

• The SimpleMessageListenerContainer can now be started without queues to listen to at all. They can be provided later at runtime via addQueues()

• a ConnectionNameStrategy is provided for the ConnectionFactory to allow to identify application connections in the Broker or other monitoring and tracing tools

  …

We are pleased to announce the general availability of Spring Cloud Data Flow for Cloud Foundry version 1.1.0.RELEASE.

In addition to all the bells and whistles of Spring Cloud Data Flow 1.1.2 that this release builds upon, the Cloud Foundry specifics include:

• Memory Optimizations: After much troubleshooting, we are pleased to deliver a memory efficient SCDF server in this release. With the changes now, regardless of how many stream/task deployments and concurrency, we have observed the memory usage of the server stay within the 200MB range as opposed to 1G or over to an eventual crash in previous 1.0.0.RELEASE. If you experienced spurious crashes previously, we highly recommend upgrading to 1.1.0.RELEASE right away.

• Maven Cache and Disk Optimizations: We have improved the way Maven caching is applied and used. There is a new LRU (Least Recently Used) design on top of the maven cache to allow us to be more proactive about the cache size as opposed to letting it grow indefinitely, which if not governed, leads to out of disk space errors for the application container where the server is running.

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