Distributed deployment of microservices across multiple OpenShift clusters | DevNation Tech Talk

What's your favorite IDE? VS Code? IDEA? Eclipse? Visual Studio? The right IDE is fundamental to your productivity as a developer, but you might need something else to become more outstanding. Why don't we take a look at your terminal? Come to this session to learn eleven CLI tools that will boost your developer productivity.

We will dissect the world famous todo app that provides a REST API (which is the foundation of microservices) with data backed by Apache Cassandra. We will leverage the TODO MVC and the TODO backend projects with the back end that we will build with Quarkus and Cassandra. Attendees will get an overview of Cassandra, including the driver for Quarkus. Through live coding (that attendees can try out later) in a cloud-based environment, primarily in Quarkus and Cassandra, attendees will understand how to implement and connect the APIs to the backend and leverage the generic client(s)provided. After attending this session attendees will walk away with a good understanding of implementing microservices using Cassandra and Quarkus. They will also get a working knowledge of how Astra (Cassandra as a service) can be leveraged in other solutions.

Every software developer wants more productivity. What if the only commands you needed to deploy were "git commit" and "git push"? Join us as we walk you through a live demonstration of how you can ship your lovely application code from your local machine to a free OpenShift cluster, fully automated through GitHub Actions. By the end of this session, you'll have a sound understanding of building a GitHub Action workflow for your codebase that leverages OpenShift to deploy your application.

Since moving to a 6 monthly release cadence, the Java platform is evolving more dynamically than ever before. It can be quite a challenge to stay on top of all the changes and new features. In this talk we're going to explore the most important developments in the Java API: which classes have been added, and what has been removed? Join Duke, the Java mascot, for a trip to space and learn which exciting new APIs provided by the Java platform can help him with his journey: The Java Vector API for utilizing the SIMD capabilities of modern CPU architectures The Foreign Linker API for integrating with native code The JFR Event Streaming API for publishing JDK Flight Recorder Events We'll also take a look at some useful changes to the Java runtime, such as CDS archives for a faster spaceship..., uhm, application launch!

Did you know that OpenJDK comes with Java Flight Recorder (JFR), an embedded production time profiler? Cryostat provides easy and secure access to JFR across container boundaries so you can profile that performance bottleneck, or find that annoying bug. Join this session to learn about using Cryostat to profile Java applications in production on OpenShift.

This document discusses Apache Kafka and Red Hat OpenShift Streams for Apache Kafka. It begins with an overview of what Apache Kafka is and its common use cases. It then demonstrates how Red Hat OpenShift Streams provides a managed Apache Kafka cluster as a service, including a dedicated cluster, configuration management, metrics, monitoring and other features to provide a streamlined developer experience. It concludes with information on trying OpenShift Streams for Apache Kafka and additional resources.

With Kubernetes, implementing security policies can be challenging. First, developers, administrators, and security teams need to understand security policies in collaboration to have the best chance of successful adoption. Next, policy enforcement needs to integrate with developer workflows. Lastly, policies need to contain corrective action that is as close to the developer as possible. KubeLinter solves these problems by linting Kubernetes YAML files and Helm charts at the source: the developer. In this session, we will evaluate KubeLinter by moving through a hands-on demo of the application, showing a use case for local machines and CI pipeline integration, and chatting about how best to integrate it into your organization: KubeLinter, and its default checks How you can leverage the application in your day-to-day operations The open source StackRox community

Many modern video games are constantly evolving post-release. New maps, game modes, and game balancing adjustments are rolled out, often on a weekly basis. This continuous iteration to improve player engagement and satisfaction requires data-driven decision making based on events and telemetry captured during gameplay, and from community forums and discussions. In this session you will learn how OpenShift Streams for Apache Kafka and Kafka Streams can be used to analyze real-time events and telemetry reported by a game server, using a practical example that encourages audience participation. Specifically you’ll learn how to: Provision Kafka clusters on OpenShift Streams for Apache Kafka. Develop a Java application that uses Kafka Streams and Quarkus to process event data. Deploy the application locally, or on OpenShift and connect it to your OpenShift Streams for Apache Kafka Cluster.

Dual writes are a common source of issues in distributed event-driven applications. A dual write occurs when an application has to change data in two different systems - for instance, when an application needs to persist data in the database and send a Kafka message to notify other systems. If one of these two operations fail, you might end up with inconsistent data which can be hard to detect and fix. OpenShift Streams for Apache Kafka is Red Hat's fully hosted and managed Apache Kafka service targeting development teams that want to incorporate streaming data and scalable messaging in their applications, without the burden of setting up and maintaining a Kafka cluster infrastructure. Debezium is an open source distributed platform for change data capture. Built on top of Apache Kafka, it allows applications to react to inserts, updates, and deletes in your databases. In this session you will learn how you can leverage OpenShift Streams for Apache Kafka and Debezium to avoid the dual write issue in an event-driven application using the outbox pattern. More specifically, we will show you how to: Provision a Kafka cluster on OpenShift Streams for Apache Kafka. Deploy and configure Debezium to use OpenShift Streams for Apache Kafka. Refactor an application to leverage Debezium and OpenShift Streams for Apache Kafka to avoid the dual write problem.