Building and Tuning High Performance Java Platforms

This session will cover various GC tuning techniques and how to best build platform engineered systems; in particular the focus will be on tuning large scale JVM deployments. It is true that half of the world's Java applications have 2.x more hardware provisioned than what they really need, and 90% of these applications miss their SLA. In this session we showcase why this is the case, and demonstrate sizing techniques on how to platform engineer such systems without the need for over-provisioning and hence meeting SLA in a more feasible way. Come to this session to learn about GC tuning recipe that can give you the best configuration for latency sensitive applications. While predominantly most enterprise class Java workloads can fit into a scaled-out set of JVM instances of less than 4GB JVM heap, there are workloads in the in memory database space that require fairly large JVMs. We will look at various Java platform scales, some holding a few large JVMs of 90GB heap space, while other platforms are of thousands of JVM instances of less than 4GB heap space on each. We will also take a close look at an example XYZCars.com where a microservices approach was designed and deployed, we will talk through how to more correctly deploy microservices without out causing fragmentation of scale, and hence without impeding performance. In this session we take a deep dive into the issues and the optimal tuning configurations for tuning large JVMs in the range of 4GB to 360GB, using the GC tuning recipes that were gained over the past 15 years of GC tuning engagements. You should be able to walk away with the ability to commence a decent GC tuning exercise on your own.

The session summarizes the techniques and the necessary JVM options needed to accomplish these complex tuning tasks, in addition to various virtualization best practices when dealing with such large Java systems. Naturally when tuning large scale JVM platforms, the underlying hardware tuning cannot be ignored, hence the session will take detour from the traditional GC tuning talks out there and dive into how you optimally size a platform for enhanced memory and CPU consumption.



Source: https://www.youtube.com/watch?v=IGS-rqSjmFk

Modern Java Component Design with Spring Framework 4.2

Spring's programming and configuration model has a strong design philosophy with respect to application components and configuration artifacts. Spring's annotation-based component story is fine-tuned for source code readability as well as consistency across an entire application's codebase. This session presents selected Spring Framework 4 component model highlights, with a focus on the upcoming Spring Framework 4.2 and a selection of Java 8 enabled features, illustrated with many code examples and noteworthy design considerations.

Getting Started with Spring Cloud

Spring Cloud 1.0 is here! It offers a powerful way to create and consume microservices. As you introduce new services, you introduce integration problems: services can be shaky, they can disappear and - as they're often exposed over HTTP - they require a bit more footwork than in-process method invocations. In this webinar, we'll focus specifically on how Spring Cloud integrates service registration (e.g. Eureka), declarative REST clients (with Netflix's Feign), reactive programming and the circuit breaker pattern with Hystrix to support easy, robust service-to-service invocations, and messaging microservices with Spring Cloud Stream. This is a deep dive on how to make connect and consume microservices, and is a natural next step after the introduction to building microservices with Spring Boot.

Lattice: A Cloud-Native Platform for Your Spring Applications

Lattice is a cloud-native application platform that enables you to run your applications in containers like Docker, on your local machine via Vagrant. Lattice includes features like:

Cluster scheduling
HTTP load balancing
Log aggregation
Health management

Lattice does this by packaging a subset of the components found in the Cloud Foundry elastic runtime. The result is an open, single-tenant environment suitable for rapid application development, similar to Kubernetes and Mesos Applications developed using Lattice should migrate unchanged to full Cloud Foundry deployments.

Lattice can be used by Spring developers to spin up powerful micro-cloud environments on their desktops, and can be useful for developing and testing cloud-native application architectures. Lattice already has deep integration with Spring Cloud and Spring XD, and you’ll have the opportunity to see deep dives into both at this year’s SpringOne 2GX. This session will introduce the basics:

Installing Lattice
Lattice’s Architecture
How Lattice Differs from Cloud Foundry
How to Package and Run Your Spring Apps on Lattice

Developer Experience with Spring Cloud

12 Factor, or Cloud Native Apps for Spring Developers

The third platform, characterized by a fluid infrastructure where virtualized servers come into and out of existence, and workloads are constantly being moved about and scaled up and down to meet variable demand, calls for new design patterns, processes and even culture. One of the most well known descriptions of these new paradigms is the Twelve Factor App (12factor.net), which describes elements of cloud native applications. Many of these needs are squarely met through the Spring Framework, others require support from other systems. In this session we will examine each of the twelve factors and present how Spring, and platforms such as Cloud Foundry satisfy them, and in some cases we’ll even suggest that responsibility should shift from Spring to platforms. At the conclusion you will understand what is needed for cloud-native applications, why and how to deliver on those requirements.

Spring Boot for DevOps

So you've decided to go cloud native. You've got a number of microservices that your company builds and runs. They interact with each other in various ways. You've got testing and staging and production environments that may have taken a lot of effort to get right. How does an individual developer work on just one of those services without stomping on other developers using a shared environment? In this session we will explore a range of options for development, starting with deploying everything locally, through stubbing, to local development solution that allows a developer to run their service in their IDE and have the whole system interact on their local machine!

Spring Boot Micro-services, Containers, and Kubernetes

This session is to learn how to create a Java-based microservice using Spring Boot, containerize it using Maven plugins, and subsequently deploy a fleet of microservices and dependent components such as Redis using Kubernetes. Spring Boot makes creating microservices fast and easy - when it comes to running a single instance. Like most Java application, the harder part is usually the clustering and fail-over configurations. First, we'll go over how get started with Spring Boot, and, subsequently, using Maven plugins to generate and create Docker images during the build process. Next, we'll go over some basic architecture and configurations, such as: - Configuring Spring Session - Using Redis as the session store - Testing the configuration locally with container linking - Tips and tricks for faster startup (/dev/./urandom is your friend) Finally, with the images, we'll deploy the microservice into Kubernetes: - Defining pods and services - Linking microservices to Redis using Kubernetes - Perform rolling upgrades of the application - Canary new versions of the microservices into the fleet Best part - we can visualize all of these activities happening in Kubernetes.