KubeCon + CloudNativeCon India 2024

OpenTelemetry has transformed the observability landscape by moving beyond the traditional three pillars of traces, metrics, and logs. With these core components now stable, the project is expanding its reach to encompass even more facets of system monitoring and performance analysis. In this session, we will explore the latest advancements in OpenTelemetry, including the integration of profiling, which offers deeper insights into application performance by capturing detailed execution data. Additionally, we'll discuss the emerging concept of entities, which promises to provide a more holistic view of system components and their interactions. By expanding its capabilities, OpenTelemetry is not just enhancing the granularity and scope of observability but also setting the stage for more comprehensive and actionable insights. Join us to learn how these new developments can help you achieve unparalleled visibility into your systems and drive better operational outcomes.

Have you felt left out of Kubernetes development because you're not a GoLang expert? The good news is you don’t need to be. While GoLang has traditionally been the go-to language for creating Kubernetes-native tools like Helm, Argo CD, and Prometheus, Kubernetes is language-agnostic and can be interacted with via its declarative REST API in any language. This session will explore the growing ecosystem of Kubernetes application development beyond Go. We’ll review popular Kubernetes libraries and frameworks in languages like Python, JavaScript, and Java. To illustrate, we’ll demo the Java Operator SDK, showcasing how to build Kubernetes applications without relying on Go. Whether you code in Python, JavaScript, or Java, this talk will give you the tools and confidence to develop Kubernetes-native applications in your preferred language.

Imagine the k8s resource model only had “spec” and not the “status”. How operationally challenging would it have been to infer this critical information from out of band approaches? While Gitops frameworks work very well to translate a pre-defined intended state, it fails to provide visibility into the actual state of the system. This entails a need to build tools to improve infrastructure observability. Gitops, also fails to address immediate operational control and recovery in the event of outages and offers a broken experience. Gitops has been the primary developer experience for Walmart running over 200k workloads and 10k deployments per day on 1000s of k8s clusters across public and private cloud. This talk highlights our learnings on building various ops tooling which provides deployment/running state of the workloads to complement our Gitops based deployment pipelines.

Rolling out a new service, adding databases clusters, are always challenging. The recommended way is to start with diverting small % of traffic to the new service and increase rollout % if things look well. If things don’t go well, the rollout % should be reduced. This is generally done through feature flags and manually monitoring the service with an observability tool. Feature flags enable remote update of rollout % without doing new deployments. OpenFeature is a new CNCF project which is trying to standardise feature flagging. It also has built-in hooks for OpenTelemetry so that feature flag values are available by default in tracing & metrics data captured by OpenTelemetry. In this talk, I will share how you can leverage open standards like OpenFeature (for feature flagging) and OpenTelemetry ( for observability) to better manage rollouts. You can enable automatic increase or decrease of rollout % by tying observability with feature flagging.

Inference engines are used to generate prediction or deduce new information based on certain rules and data. With rise in the number of applications that benefit from inferencing, there is a definitive need to build Inferencing Services that are robust, performant (latency optimized) and can seamlessly scale on demand. In this session, we will discuss a proven set of procedures and guidelines for building and managing inference pipelines on Kubernetes. We will discuss details of the underlying hardware (GPU/CPU/memory) and K8s configuration requirements of some of the well-known Inference engines. We will demonstrate how robust and fault tolerant pipelines for LLM and RAG can be built using basic K8s constructs like operators, statefulsets and persistent volumes; along with enabling automated monitoring, triaging and remediation of the failed hardware and software components.

We, Priyanka and Jason, have been involved in Kubernetes release cycles for years, taking on roles from shadow to Release Lead and Emeritus Advisor. While we've worked with Kubernetes Enhancement Proposals (KEPs) throughout this time, it wasn’t until the 1.29 release, while prepping for press interviews, that we really dug into them—and wow, did we learn a lot! KEPs aren’t just feature proposals; they give you a peek into the entire lifecycle of a feature—designs, alternatives, testing, and final code. They’re a goldmine for understanding Kubernetes, whether you’re new or a seasoned contributor. We’ll walk you through some real KEPs and share our approach to reading and making sense of them. If you’re curious about Kubernetes and want to explore its features more deeply, join us. There’s so much to discover, and we’re excited to share what we’ve learned with you!