OperatorCon
Online Sessions

Challenges in Building a Multi-Cloud-Provider Platform With Managed Kubernetes

Building a cloud-agnostic platform used to be a challenging task as one had to deal with a large number of different cloud APIs and service offerings. Today, as most Cloud providers are offering a managed Kubernetes solution (e.g., GKE, AKS, or EKS), it seems like developers could simply build a platform based on Kubernetes and be cloud-agnostic. While this assumption is mostly correct, there are still a number of differences and pitfalls when deploying across those managed Kubernetes solutions.

This talk discusses the experiences made while building the ArangoDB Managed Service offering across and GKE, AKS, or EKS.

While the (managed) Kubernetes API being a great abstraction from the actual cloud provider, a number of challenges remain including for example networking, autoscaler, cluster provisioning, or node sizing. This talk provides an overview of those challenges and also discuss how they were solved as part the ArangoDB managed Service.

Infrastructure Automation Operators

By Ken Owens, Mastercard

Infrastructure teams are siloed and even within the silos there are further silos. With the evolution of infrastructure as code and IaaS services, orchestration and automation of infrastructure services is segmented and often isolated. Early on, we determined to look at the orchestration and management of a deployment as a set of custom resources that need to be orchestrated to deliver a set of services. This presentation will describe the service catalog we developed as a set of CRDs. We will present the architecture we leveraged to accomplish this pattern: front-end (how infrastructure is consumed) and Back-end (how infrastructure is provisioned).

Introduction to KUDO - Kubernetes Operators The Easy Way

Kubernetes Operators are the next phase of the journey towards automating complex applications in containers. The Kudo project introduces an easy way to build Kubernetes operators using declarative YAML. Many Operators that exist today handle initial deployment, but they don’t provide automation for tasks like binary upgrades, configuration updates, and failure recovery. Implementing a production-grade controller for a complex workload typically requires thousands of lines of code and many months of development. As a result, the quality of operators that are available today varies. The Kudo project provides a universal operator to enable automated creation of operators for Kubernetes, in most cases just using YAML. In this talk, I’ll introduce the Kudo project, and demo the creation of a Kubernetes operator using Kudo.

When and Why Build Operators

In order to meet the increasing requirements of running more analytical jobs to get more insights into organisations data and reap benefits from it it makes more sense to move my Bigdata pipeline to cloud as it meets requirements of building and deploy analytical jobs within seconds with simplified user experience, scalability, and reliability. But Its a huge challenge for Cloud providers to meet all expectations like meetings standards of 99.9999 availability, reduced operations. Kubernetes and related technologies have been really helpful to bring down operations and failure rates but adopting Kubernetes is not only the Mantra which help you achieve all your goals but how you design your components on top of it also mater. One of such component which has been very helpful is Kubernetes operators. The purpose of this talk is to discuss what are scenarios where it make sense to create an Operator.

Build Your Operator With the Right Tool

You want to build a Kubernetes Operator for your software. Which tool to choose? Operator SDK with Helm, Ansible, or Go? Or maybe start from scratch with Python, Java, or any other programming language? And what is the right phase in the Operator Capability/Maturity Model that you should provide?

In this session, you’ll learn:

• Different ways of building Kubernetes Operators
• Demo of building the same Operator using different tools
• Methods used by the most popular Operators (Couchbase, Prometheus, MongoDB)
• Operator Capability Model and how it affects your toolkit
• Hazelcast’s journey with Hazelcast Operator

Using an Operator With a Well-Known Java Application Server

Containerization of a traditional Java EE Application Server such as WebLogic needs a mind-shift of how applications should be developed an how existing environments can be transformed to a managed Kubernetes Docker platform.

To manage a traditional style platform as a WebLogic Application Server requires managing it with “knowledge from the field” and thats why Oracle developed an operator specialized for installing, configuring and operating a WebLogic Server in a Kubernetes Cluster, such as:

• Simpler WebLogic management in Kubernetes
• Kubernetes resources are allocated for WebLogic domain(s)
• Manages overall WebLogic environment through Kubernetes APIs (1) Load Balancer, Network, (2) Ingress Controllers,
• Security, (3) HA restart, upgrade, scaling (4) Persistent storage
• Ensurance all best practices are followed

The session will tell you all you need to setup an operator such as these, from a commercial product to what it is now , an OpenSource initiative developed and maintained.

3 Years of Running Operators in Production – A Reflection

In 2016, we were driven by the idea to find a generic way to run Kubernetes clusters everywhere. So far so good….but how exactly? Could it make sense to run Kubernetes in Kubernetes? Well, let’s give it a try. This is how we started to build our first Kubernetes Operator to manage Kubernetes in Kubernetes in a scalable way. Early 2017, we first went into production with about a dozen of clusters on AWS. Meanwhile, we manage hundreds of clusters across large-scale multi-cloud deployments. More than three years after the start of this exciting journey, we look back on what we have learned and show how we plan to extend the operator paradigm to the edge.