Working with Self-Managed Nodes and Managing Kubernetes Deployments

Oracle University Podcast

In this episode, hosts Lois Houston and Nikita Abraham speak with senior OCI instructor Mahendra Mehra about the capabilities of self-managed nodes in Kubernetes, including how they offer complete control over worker nodes in your OCI Container Engine for Kubernetes environment. They also explore the various options that are available to effectively manage your Kubernetes deployments. OCI Container Engine for Kubernetes Specialist: https://mylearn.oracle.com/ou/course/oci-container-engine-for-kubernetes-specialist/134971/210836 Oracle University Learning Community: https://education.oracle.com/ou-community LinkedIn: https://www.linkedin.com/showcase/oracle-university/ X (formerly Twitter): https://twitter.com/Oracle_Edu Special thanks to Arijit Ghosh, David Wright, Radhika Banka, and the OU Studio Team for helping us create this episode. -------------------------------------------------------- Episode Transcript:

00:00

Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we’ll bring you foundational training on the most popular Oracle technologies. Let’s get started!

00:26

Nikita: Hello and welcome to the Oracle University Podcast! I’m Nikita Abraham, Principal Technical Editor with Oracle University, and with me is Lois Houston, Director of Innovation Programs.

Lois: Hi everyone! Last week, we discussed how OKE virtual nodes can offer you a complete serverless Kubernetes experience.

Nikita: Yeah, and in today’s episode, we’ll focus on self-managed nodes, where you get complete control over the worker nodes within your OKE environment. We’ll also talk about how you can manage your Kubernetes deployments.

00:57

Lois: To tell us more about this, we have Mahendra Mehra, a senior OCI instructor with Oracle University. Hi Mahendra! Welcome back! Let’s get started with self-managed nodes. Can you tell us what they are?

Mahendra: In Container Engine for Kubernetes, a self-managed node is essentially a worker node that you personally create and host on a compute instance or instance pool within the compute service.

Unlike managed nodes or virtual nodes, self-managed nodes are not grouped into node pools by default. They are often referred to as Bring Your Own Nodes, also abbreviated as BYON. If you wish to streamline administration and manage multiple self-managed nodes collectively, you can utilize the compute service to create a compute instance pool for hosting these nodes. This allows for greater flexibility and customization in your Kubernetes environment.

01:58

Nikita: Mahendra, what are some practical usage scenarios for OKE self-managed nodes?

Mahendra: These nodes offer a range of advantages for specific use cases. Firstly, for specialized workloads, leveraging the compute service allows you to configure compute instances with shapes and image combination that may not be available for managed nodes or virtual nodes.

This includes options like GPU shapes for hardware accelerated workloads or high frequency processor cores for demanding high-performance computing tasks. Secondly, if you require complete control over your compute instance configuration, self-managed nodes are the ideal choice. This gives you the flexibility to tailor each node to your specific requirements.

Additionally, self-managed nodes are particularly well suited for Oracle Cloud Infrastructure cluster networks. These nodes provide high bandwidth, low latency RDMA connectivity, making them a preferred option for certain networking setups.

Lastly, the use of compute instance pools with self-managed nodes enables the creation of infrastructure for handling complex distributed computing tasks. This can greatly enhance the efficiency of your Kubernetes environment. Consider these points carefully to determine the optimal use of OKE self-managed nodes in your deployments.

03:30

Lois: What do we need to consider before creating a self-managed node and integrating it into a cluster?

Mahendra: There are two crucial aspects to address. Firstly, you need to confirm that the cluster to which you plan to add a self-managed node is configured appropriately.

Secondly, it's essential to choose the right image for the compute instance hosting the self-managed node.

03:53

Nikita: Can you dive a little deeper into these prerequisites?

Mahendra: To successfully integrate a self-managed node into your cluster, you must ensure that the cluster is an enhanced cluster. This is a crucial prerequisite for the addition of self-managed nodes.

The flannel CNI plugin for pod networking should be utilized, not the VCN-native pod networking CNI plugin. This ensures optimal pod networking for your self-managed nodes. The control plane nodes of the cluster must be running Kubernetes version 1.25 or later. This is essential for compatibility and optimal performance.

Lastly, maintain compatibility between the Kubernetes version on control plane nodes and worker nodes with a maximum allowable difference of two minor versions. This ensures a smooth and stable operation of your Kubernetes environment. Keep these cluster requirements in mind as you prepare to add self-managed nodes to your OKE cluster.

04:55

Lois: What about the image requirements when creating self-managed nodes?

Mahendra: Choose either Oracle Linux 7 or Oracle Linux 8 image, for your self-managed nodes. Ensure that the selected image has a release date of March 28, 2023 or later.

Obtain the image OCID, also known as Oracle Cloud Identifier, from the respective sources. When specifying an image, be mindful of the Kubernetes version it contains.

It's your responsibility to select an image with a Kubernetes version that aligns with the Kubernetes version skew support policy. Keep in mind that the Container Engine for Kubernetes does not automatically check the compatibility. So it's up to you to ensure harmony between the Kubernetes version on the self-managed node and the cluster's control plane nodes. These considerations will help you make informed choices when configuring images for your self-managed nodes.

05:57

Nikita: I really like the flexibility and customization OKE self-managed nodes offer. Now I want to switch gears a little and ask you about OCI Service Operator for Kubernetes. Can you tell us a bit about it?

Mahendra: OCI Service Operator for Kubernetes is an open-source Kubernetes add-on that transforms the way we manage and connect OCI resources within our Kubernetes clusters. This powerful operator enables you to effortlessly create, configure, and interact with OCI resources directly from your Kubernetes environment, eliminating the need for constant navigation between the Oracle Cloud Infrastructure Console, CLI, or other tools. With the OCI Service Operator, you can seamlessly leverage kubectl to call the operator framework APIs, providing a streamlined and efficient workflow.

06:53

Lois: On what framework is the OCI Service Operator built?

Mahendra: OCI Service Operator for Kubernetes is built using the open-source Operator Framework toolkit. The Operator Framework manages Kubernetes-native applications called operators in an effective, automated, and scalable way. The Operator Framework comprises essential components like Operator SDK. This leverages the Kubernetes controller-runtime library, providing high-level APIs and abstractions for writing operational logic. Additionally, it offers tools for scaffolding and code generation.

07:35

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08:14

Nikita: Welcome back! Mahendra, are there any other components within OCI Service Operator to manage Kubernetes deployments?

Mahendra: The other essential component is Operator Lifecycle Manager, also abbreviated as OLM. OLM extends Kubernetes by introducing a declarative approach to install, manage, and upgrade operators within a cluster. The OCI Service Operator for Kubernetes is intelligently packaged as an Operator Lifecycle Manager bundle, simplifying the installation process on Kubernetes clusters. This comprehensive bundle encapsulates all necessary objects and definitions, including CRDs, RBACs, ConfigMaps, and deployments, making it effortlessly deployable on a cluster.

09:02

Lois: So much that users can take advantage of! What about OCI Service Operator’s integration with other OCI services?

Mahendra: One of its standout features is its seamless integration with a range of OCI services. The first one is Autonomous Database, specifically tailored for transaction processing, mixed workloads, analytics, and data warehousing. Enjoy automated patching, upgrades, and tuning, allowing routine maintenance tasks to be performed without human intervention.

The next on the list is MySQL HeatWave, a fully-managed Database Service designed for developing and deploying secure cloud-native applications using widely adopted MySQL open-source database. Third on the list is OCI Streaming service. Experience a fully managed, scalable, and durable solution for ingesting and consuming high-volume data streams in real time.

Next is Service Mesh. This service offers a set of capabilities to facilitate communication among microservices within a cloud-native application. The communication is centrally managed and secured, ensuring a smooth and secure interaction. The OCI Service Operator for Kubernetes serves as a versatile bridge, seamlessly connecting your Kubernetes clusters with these powerful Oracle Cloud Infrastructure services.

10:31

Nikita: That’s awesome! I’ve also heard about Ingress Controllers. Can you tell us what they are?

Mahendra: A Kubernetes Ingress Controller serves as the enforcer of rules defined in a Kubernetes Ingress. Its primary role is to manage, load balance, and route incoming traffic to specific service pods residing on worker nodes within the cluster. At the heart of this process is the Kubernetes Ingress Resource. Think of it as a blueprint, a rich configuration holding routing rules and options, specifically crafted for handling HTTP and HTTPS traffic. It serves as a powerful orchestrator for managing external communication with services inside the cluster.

11:15

Lois: Mahendra, how do Ingress Controllers bring about efficiency?

Mahendra: Efficiency comes with consolidation. With a single ingress resource, you can neatly gather routing rules for multiple services. This eliminates the need to create a Kubernetes service of type LoadBalancer for each service seeking external or private network traffic.

The OCI native ingress controller is a powerhouse. It crafts an OCI Flexible Load Balancer, your gateway to efficient request handling. The OCI native ingress controller seamlessly adapts to changes in routing rules with real-time updates.

11:53

Nikita: And what about integration with an OKE cluster?

Mahendra: Absolutely. It harmonizes with the cluster for streamlined traffic management. Operating as a single pod on a randomly selected worker node, it ensures a balanced workload distribution.

12:08

Lois: Moving on, let’s talk about running applications on ARM-based nodes and GPU nodes. We’ll start with ARM-based nodes.

Mahendra: Typically, developers use ARM-based worker nodes in Kubernetes cluster to develop and test applications. Selecting the right infrastructure is crucial for optimal performance.

12:28

Nikita: What kind of options do developers have when running applications on ARM-based nodes?

Mahendra: When it comes to running applications on ARM-based nodes, you have a range of options at your fingertips. First up, consider the choice between ARM-based bare metal shapes and flexible VM shapes. Each comes with its own unique advantages.

Now, let's talk about the heart of it all, the Ampere A1 Compute instances. These instances are driven by the cutting edge Ampere Altra processor, ensuring high performance and efficiency for your workloads. You must specify the ARM-based node pool shapes during cluster or node pool creation, whether you choose to navigate through the user-friendly console, leverage the flexibility of the API, or command with precision through the CLI, the process remains seamless.

13:23

Lois: Can you define pods to run exclusively on ARM-based nodes within a heterogeneous cluster setup?

Mahendra: In scenarios where a cluster comprises node pools with ARM-based shapes alongside other shapes, such as AMD64, you can employ a powerful tool called node selector in the pod specification. This allows you to precisely dictate that an application should exclusively run on ARM-based worker nodes, ensuring your workloads aligns with the desired architecture.

13:55

Nikita: And before we end this episode, can you explain why developers must run applications on GPU nodes?

Mahendra: Originally designed for graphics manipulations, GPUs prove highly efficient in parallel data processing. This makes them a top choice for deploying data-intensive applications. Our GPU nodes utilize cutting edge NVIDIA graphics cards ensuring efficient and powerful data processing. Seamless access to this computing prowess is made possible through CUDA libraries.

To ensure smooth integration, be sure to select a GPU shape and opt for an Oracle Linux GPU image preloaded with the essential CUDA libraries. CUDA here is Compute Unified Device Architecture, which is a parallel computing platform and application-programming interface model created by NVIDIA. It allows developers to use NVIDIA graphics-processing units for general-purpose processing, rather than just rendering graphics.

14:57

Nikita: Thank you, Mahendra, for another insightful session. We appreciate you joining us today.

Lois: For more information on everything we discussed, go to mylearn.oracle.com and search for the OCI Container Engine for Kubernetes Specialist course. You’ll find plenty of demos and skill checks to supplement your learning. Join us next week when we’ll discuss vital security practices for your OKE clusters on OCI. Until next time, this is Lois Houston…

Nikita: And Nikita Abraham, signing off!

15:28

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