Spring Blog

Recorded at SpringOne Platform 2016.
Speaker: Oleg Zhurakousky
Slides: http://www.slideshare.net/SpringCentral/spring-with-apachenifi

Spring Integration has long captured the hearts and minds of the developers world wide for its emphasis on simplicity, modularity and productivity when it comes to all things related to work-flow orchestration and complex event processing and is successfully used in a variety of Big Data solutions. Apache NiFi, on the other hand, is a new addition to the already rich Big Data technology stack.

Recorded at SpringOne Platform 2016.
Speaker: Andreas Evers, Ordina JWorks
Slides: http://www.slideshare.net/SpringCentral/writing-comprehensive-and-guaranteed-uptodate-rest-api-documentation

RESTful APIs are eating the world, yet all too often the documentation can cause indigestion for the APIs’ developers and their users. Developers have to deal with annotation overload, repetition, and an unpleasant writing environment. Users are then left with documentation that’s inaccurate and difficult to use. It doesn’t have to be this way.

Recorded at SpringOne Platform 2016.
Speaker: Christian Posta, Redhat
Slides: http://www.slideshare.net/SpringCentral/real-world-microservices-with-spring-cloud-netflix-oss-and-kubernetes

Building distributed systems - whether we call them SOA or microservices - is not easy. Open source communities like Spring, NetflixOSS and Kubernetes bring decades of experience building these systems, but the question always begs to be asked “do we implement these patterns in the application layer or in the infrastructure layer”?

Recorded at SpringOne Platform 2016.
Speakers: Eric Bottard & Ilayaperumal Gopinathan
Slides: http://www.slideshare.net/SpringCentral/orchestrate-all-the-things-with-spring-cloud-data-flow

What do things like Minecraft, a light bulb and your music library have in common? Well, nothing really. Until you come up with this crazy idea to link them together. This is where application orchestration comes in.

In this session, you’ll learn how Spring Cloud Data Flow allows easy composition of microservices together. As the spiritual successor to Spring XD and the natural sidekick of Spring Cloud Stream, Data Flow has been thought as a way to deploy, run and manage loosely coupled apps in the cloud.

We are pleased to announce that Spring Cloud Task 1.2.0.M1 is now available via Github and the Pivotal download repository. Many thanks to all of those who contributed to this release.

Spring Cloud Task 1.2.0.M1 offers the following features:

This is the first milestone for the 1.2.x line of Spring Cloud Task. Intended to continue the integrations required for Spring Cloud Data Flow, this release provides the following new features:

• Better DataSource integration between task and batch - This release makes configuring the DataSource used by the task/batch integration easier.

• Allows an external process to update the external execution id - Prior to this release, the external execution id (the execution id for the underlying platform) had to be updated by the task itself. In some use cases, this is not possible. This release exposes the ability to update the external execution id outside the scope of the task itself.

• Allow the user to configure a prefix for the task tables - Similar to how Spring Batch allows a user to configure a prefix for the batch repository tables, Spring Cloud Task now exposes the ability to configure a prefix for task repository tables as well.

• Add support for parent execution ids - In complex use cases, the ability for one task to launch multiple other tasks is very common. Spring Cloud Task has already supported this via partitioned batch jobs launching worker nodes as tasks. This release provides the ability within the task repository to associate the parent child relationship that occurs from that capability (both in the batch use case and with raw tasks).

• Upgrade to Spring Cloud Camden SR5 - This release is compatible with Spring Cloud Camden’s recent SR5 release.

Speaker: Josh Long

Hi Spring fans! In this tip, we’ll quickly look at how to integrate distributed tracing with Spring Cloud Sleuth and the OpenZipkin project.

Welcome to another installment of This Week in Spring! We’ve got a lot to cover this week so let’s get to it.

• Fred Melo is one of Pivotal’s brightest and when he talks about data, people listen. Here’s your chance to watch his SpringOne Platform talk on data microservices
• The RabbitMQ Team has been busy! The RMQ Java Client has some very nice new (non-breaking) new features in Metrics, logging and Java NIO, among other things.
• Expanding on the metrics support in RMQ Java Client 4.0, the Rabbit team blogs about using JMX, Spring Boot Actuator and Coda Hale’s awesome DropWizard Metrics library.
• I liked this SpringOne Platform talk, from Confluent’s Joe Kutner, I Can’t Believe it’s not a Queue: using Apache Kafka with Spring. It’s a focused look on Apache Kafka, one of the most capable and cloud-native message queues these days - Apache Kafka - with Spring.
• Check out Spring Integration and messaging guru Gary Russell’s SpringOne Platform look at Spring for Apache Kafka
• check out Spring and Spring for Apache Hadoop contributor Thomas Risberg’s look at building big-data pipelines with Spring
• our friend Toshiaki Maki has built a really cool Graphviz visualization of the beans in an application that’s accessible under the /beansviz endpoint.
• Spring Cloud contributor Vinicius Carvalho’s SpringOne Platform talk on evolutionary and resilient microservices is one to watch
• Spring Integration Java DSL lead Artem Bilan just announced Spring for Apache Kafka 1.1.3
• Spring Cloud ninja Marcin Grzejszczak just announced Spring Cloud Camden SR5
• the team behind Spring Tool Suite is cooking up some amazing stuff for Spring Tool Suite 4, and a huge part of that is an extraction of the plugins in STS as language servers that can be used with a variety of other IDEs, including Atom and Microsoft VS Code. As part of the that, the team has just released the Cloud Foundry manifest.yml editor
• Spring IO Platform lead Andy Wilkinson just announced Spring IO Platform Brussels RC1
• not one to rest on his laurels, Andy also just announced Spring IO Platform Athens SR3
• Are you going to be at the fabulous DevNexus event in Atlanta, GA, later this month? The Spring team will be there too!
• seems I missed a good one in January! Check out Muhammad Noor’s post on building reactive based microservices with Spring Cloud and
  Reactor
• Rashidi Zin put together a nice example of using Spring Data to support entity auditing. Very cool Rashidi!
• Bruno Krebs put together a very cool post on building a REST API wiht test-driven development (and Spring Boot, naturally)
• Mario-Leander Reimer’s cloud-native workshop at the OOP17 conference looks pretty darned interesting! I wish I’d been there. For those of us that weren’t, though, there is at least his Git repository.
• Speaking of DevNexus, this oldie-but-a-goodie, Keeping it Clean: Lessons from Open Source and How to Polish, from Spring Boot co-lead Phil Webb is definitely a good watch if you haven’t seen it
• While not, strictly speaking, related to Spring, continuous delivery is the grand unifiying theory behind microservices, agile, TDD, and so many other practices we take for granted today and few people introduce the rationale behind it better than Chris Farley
• This post has little to do with Spring itself but it is a very interesting look at the implications on performance in the JVM that don’t override #hashCode in certain contexts

Recorded at SpringOne Platform 2016.
Speaker: Joe Kutner, Confluent
Slides: http://www.slideshare.net/SpringCentral/i-cant-believe-its-not-a-queue-using-kafka-with-spring

Your existing message system is great, until it gets overloaded. Then what? That’s when you should try Kafka.

Kafka is designed to be resilient. It takes the stress out of moving from a Spring monolith into a scalable system of microservices. Since you can capture every event that happens in your app, it’s great for logging. You can even use Kafka’s distributed, ordered log to simulate production load in your staging environment.

Recorded at SpringOne Platform 2016.
Speaker: Fred Melo
Slides: http://www.slideshare.net/SpringCentral/architecting-for-cloud-native-data-data-microservices-done-right-using-spring-cloud-64889916

Microservices are definitely offering best practice guidance for those architecting cloud native applications. The ability to quickly create small services that can be individually deployed, configured and scaled, as building blocks for scalable, highly distributed and fault-tolerant systems has been causing every company to rethink on how to architect modern systems and making Spring Boot shine in popularity.