DevConf.US 2021

While “software is eating the world”, those who are able to best manage the huge mass of data will emerge out on the top. The batch processing model has been faithfully serving us for decades. However, it might have reached the end of its usefulness for all but some very specific use-cases. As the pace of businesses increases, most of the time, decision makers prefer slightly wrong data sooner, than 100% accurate data later. Stream processing - or data streaming - exactly matches this usage: instead of managing the entire bulk of data, manage pieces of them as soon as they become available. In this talk, I’ll define the context in which the old batch processing model was born, the reasons that are behind the new stream processing one, how they compare, what are their pros and cons, and a list of existing technologies implementing the latter with their most prominent characteristics. I’ll conclude by describing in detail one possible use-case of data streaming that is not possible with batches: display in (near) real-time all trains in Switzerland and their position on a map. I’ll go through the all the requirements and the design. Finally, using an OpenData endpoint and the Hazelcast platform, I’ll try to impress attendees with a working demo implementation of it.

This panel of experts will discuss Red Hat's new Automotive Initiative, an effort across Red Hat to develop an in-vehicle operating system based on Red Hat Enterprise Linux. The eventual offering will be certifiable as functionally safe via ISO 26262, a first for Linux, and will enable the car to function as an edge device in a larger system - an evolution of current methods toward ubiquitous computing and services built on cloud services, AI and machine learning, and best of all, open source licenses and development practices. Join us to learn more about the car of the future and Red Hat's role in its development.

This is the tale of instrumenting cri-o with vendor neutral OpenTelemetry. With an abundance of blogs, documentation, and examples to follow it seemed an easy task, but as it turned out there was a sharp learning curve. From spans to traces and receivers, collectors, and exporters this session will outline the paths taken and lessons learned. It will demonstrate and detail the importance of observability throughout a distributed system, starting from the ground up with cri-o. The audience will leave with the knowledge and motivation required to instrument code with OpenTelemetry. The importance of OpenTelemetry to the open source ecosystem will also be discussed. The more services and components that add OpenTelemetry tracing, the easier and more efficient Kubernetes will become. This opens doors for further collaboration and innovation.
Slides: https://docs.google.com/presentation/d/1qOnAj0znJ4ayopyMm9nqnHq0RmURkqFpZILxbpidmOg/edit?usp=sharing
Demo Video: https://youtu.be/fYewA6lURE0

REST is great, except you can not ask what you really want. You get what the server gives you. Find out how to make your API queryable allowing your consumenrs to get exactly what they what. MicroProfile GraphQL fix the over and under fetch problem that exist in today's APIs, and it's easy to write! “GraphQL is a data query language developed internally by Facebook in 2012 before being publicly released in 2015. It provides an alternative to REST and ad-hoc web service architectures.” In this talk, we will Go through the basics of GraphQL Discuss the differences with REST Go through a basic example in Java. We will look at the following GraphQL concepts: Query Fragment and Named Filtering Variables Mutation Error Handling Introspections