CloudPro #54: Build your own copilot with Microsoft Azure AI Studio
Understanding DNS in Kubernetes
Welcome to the 54th edition of CloudPro! Today, we’ll talk about:
⭐Masterclass:
From Fragile to Faultless: Kubernetes Self-Healing In Practice
What is the difference between a root process and a containerized root process?
Load balancing and scaling long-lived connections in Kubernetes
🔍Secret Knowledge:
⚡Techwave:
🛠️HackHub: Best Tools for the Cloud
Cheers,
Editor-in-Chief
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⭐MasterClass: Tutorials & Guides
⭐Managing Kubernetes resources across multiple clusters
Managing Kubernetes resources across multiple clusters can quickly become overwhelming using traditional command-line tools like kubectl. These tools are powerful but often require complex and repetitive commands, which can be error-prone and difficult to manage at scale. This blog delves into the limitations of these traditional methods and introduces modern tools that provide more efficient, centralized, and secure management solutions.
Modern Kubernetes dashboards like Lens and Devtron offer user-friendly interfaces and advanced features to simplify cluster management. Lens acts as an Integrated Development Environment (IDE) for Kubernetes, allowing users to manage multiple clusters, visualize real-time statistics, and perform tasks without complex commands. Devtron, an open-source platform, enhances cluster management with features like CI/CD pipelines, GitOps, and robust RBAC. Both tools provide centralized dashboards for seamless resource management, making them ideal for organizations handling numerous Kubernetes clusters.
⭐From Fragile to Faultless: Kubernetes Self-Healing In Practice
In this blog, Grzegorz Głąb and Nibir Bora from City Storage Systems discuss their experience with managed Kubernetes distributions, particularly Azure Kubernetes Service (AKS). They highlight that while managed services are convenient, they are not free from occasional failures such as hardware faults, kernel misconfigurations, and network bottlenecks. To combat these issues, their team developed a self-healing framework to automate the detection and resolution of common failure modes, reducing operational burdens and improving system reliability.
The framework consists of detectors that identify specific failure conditions and fixers that implement remediation steps. This system allows for swift responses to both temporary and permanent issues, ensuring minimal impact on business continuity. By continuously enhancing and expanding their self-healing framework, the team has managed to significantly reduce support workload and improve the resilience of their Kubernetes platform. Their approach demonstrates that investing in automation for self-healing is crucial for maintaining reliable and scalable systems in the face of inevitable cloud and infrastructure challenges.
⭐What is the difference between a root process and a containerized root process?
A root process is a process running with UID 0 on a Linux system, granting it full administrative privileges, allowing it to access and modify all system resources. These processes have unrestricted control over the system, making them powerful but also potentially dangerous if misused or compromised.
A containerized root process, while also running with UID 0 within the container, operates under additional layers of isolation provided by Linux namespaces and control groups (cgroups). These mechanisms restrict the containerized root process's visibility and access to the broader system resources, effectively confining it to a controlled environment. Despite this, containerized root processes can still pose security risks due to the potential for escaping these isolation boundaries, exploiting vulnerabilities in the kernel, and gaining broader system access.
Thus, while containerized root processes are isolated, they still run with high privileges, and maintaining them securely requires careful configuration and management to mitigate the risks associated with root-level access within containers.
⭐Best practices and tools for CI/CD pipelines
Optimizing Kubernetes application delivery involves implementing best practices and leveraging effective tools to streamline continuous integration (CI) and continuous delivery (CD) pipelines. Kubernetes, with its powerful container orchestration capabilities, necessitates a robust CI/CD pipeline to automate the building, testing, and deployment of applications. This pipeline should include a container registry, configuration manager, Helm for packaging applications, and support for multiple cluster environments.
Managing CI/CD pipelines in Kubernetes presents challenges, especially with microservices architectures and Kubernetes' dynamic nature. Dependency management, deployment coordination, and ensuring system stability are critical for microservices. Kubernetes' fluid environment requires CI/CD pipelines to adapt to changes and handle transitions smoothly. Tool selection and integration add another layer of complexity, requiring careful consideration to match project needs.
To optimize Kubernetes CI/CD pipelines, adopting best practices such as GitOps for managing configurations as code, utilizing Infrastructure as Code (IaC) tools, and implementing observability for monitoring performance is essential. Tools like Jenkins X, Argo CD, and Spinnaker offer tailored solutions for Kubernetes deployments, each with unique features and advantages. Ensuring security through image scanning, secrets management, and role-based access control (RBAC) further enhances the robustness of the CI/CD pipeline.
⭐Load balancing and scaling long-lived connections in Kubernetes
Kubernetes is great for deploying applications, using Services and Deployments to manage and scale them. Deployments ensure the right number and type of Pods are running, while Services act like load balancers, distributing traffic among Pods. However, Kubernetes doesn't handle long-lived connections well, such as HTTP/2, gRPC, or database connections, which can lead to uneven distribution of requests.
For better load balancing of long-lived connections, consider using client-side load balancing or a proxy. This involves configuring your application to maintain and distribute connections or using external tools like Envoy or HAProxy. Another solution is to use a service mesh like Istio or Linkerd, which manages traffic inside your cluster and balances requests automatically. These approaches ensure that all Pods and servers are utilized effectively, preventing bottlenecks and resource underutilization.
🔍Secret Knowledge: Learning Resources
🔍Argo Rollouts - What is it, how it works & tutorial
Argo Rollouts is a Kubernetes tool designed to enhance the deployment process by implementing advanced rollout strategies like blue-green and canary deployments. Unlike the default Kubernetes Deployment object, which supports only rolling updates and complete recreation, Argo Rollouts allows you to gradually shift traffic to a new release, minimizing the risk of widespread issues. This is achieved through a Kubernetes controller and a set of Custom Resource Definitions (CRDs), with the primary CRD being Rollout. The Rollout CRD replaces the standard Deployment object, enabling you to define sophisticated update strategies that improve deployment reliability and performance.
Once you create a Rollout object in your cluster, the Argo controller manages the creation, replacement, and removal of Pods as needed. You can control the rollout process using Argo's Kubectl plugin, either manually or through automation based on metrics from external sources, such as HTTP request data from an Ingress controller. This allows for precise management of deployment progression and automatic rollback in case of failures. By using Argo Rollouts, you gain robust features that make it easier to manage application updates, ensuring a smoother and safer deployment process at scale.
🔍Understanding DNS in Kubernetes
DNS (Domain Name System) is crucial in Kubernetes for service discovery, enabling applications to connect using human-readable domain names instead of IP addresses. CoreDNS, the default DNS provider, runs as a deployment in the Kubernetes cluster, allowing pods to communicate with each other and external services seamlessly.
Kubernetes uses DNS policies to control how DNS resolution is handled. The default policy, ClusterFirst, directs DNS queries to the internal DNS service, facilitating internal communication within the cluster. Other policies, like Default and None, offer flexibility by allowing pods to inherit the node's DNS settings or use custom configurations, respectively. This system ensures efficient and flexible network communication in Kubernetes environments.
At the Kubernetes 1.0 celebration, the speaker delved into the project's journey and the substantial effort behind its development. Drawing from experiences with Borg and Omega, Google's internal systems for managing large-scale compute clusters, the team recognized that Borg's control plane architecture wasn't well-suited for its actual use cases. This realization led to the development of Omega, which improved upon Borg's design. Omega introduced concepts like a Paxos-based key/value store and asynchronous controllers, which were later refined and incorporated into Kubernetes.
From mid-2013 to the first half of 2014, the team rapidly developed Kubernetes, leveraging their prior knowledge. They sketched out the API and built prototypes, collaborating with Docker to integrate their container technology. By the second half of 2014, Kubernetes was launched as an open-source project, with the team continuously iterating on the design and implementation. The following months were spent solidifying the control plane, refining the API, and enhancing usability. This groundwork allowed Kubernetes to launch as a production-ready system in July 2015, marking a significant milestone in container orchestration.
🔍Otterize's journey in cloud-native packet management
In this blog post, Nic Vermandé explores Kubernetes networking fundamentals and Otterize’s role in enhancing network security. Kubernetes abstracts networking to simplify scalability and flexibility, using the Container Network Interface (CNI) to support various networking solutions like Flannel for basic setups or Cilium for advanced needs. The blog provides a detailed packet walk through Kubernetes, from a user’s request to an application through an Ingress Controller, showing how traffic moves and how Network Policies are used to secure communication.
The post explains the core components of Kubernetes networking, such as Pod and Service networking, and how kube-proxy and Ingress Controllers manage traffic. It also delves into how Otterize improves security by generating adaptive Network Policies based on actual traffic patterns. By analyzing traffic flows and using tools like tcpdump, the blog illustrates the packet journey through Kubernetes and demonstrates how Otterize’s Network Mapper and Client Intents Operator help automate the creation of effective security policies for cloud-native applications.
🔍Monitoring PostgreSQL Database on Kubernetes using eBPF
In this blog post, we explore how to monitor PostgreSQL databases running on Kubernetes using eBPF technology. eBPF, or Extended Berkeley Packet Filter, is a powerful Linux kernel feature that allows us to observe and analyze system events without modifying application code. By leveraging eBPF, we can track client queries, identify potential bottlenecks, and gather detailed performance metrics for PostgreSQL databases.
The process involves using a specialized eBPF agent called Alaz, which operates as a DaemonSet in your Kubernetes cluster. This agent attaches eBPF programs to various kernel hooks to capture PostgreSQL protocol messages and classify them. For example, it captures data sent and received between the client and server, parses it to identify query types, and forwards this information to a user-space application for visualization. This setup helps in monitoring the database’s performance, detecting issues, and planning for resource needs, all while maintaining low overhead and high efficiency.
⚡ TechWave: Cloud News & Analysis
⚡Build your own copilot with Microsoft Azure AI Studio
In the rapidly evolving field of AI technology, Microsoft Azure AI Studio has become a key tool for organizations looking to develop their own AI copilots. This platform allows businesses to create customized AI solutions tailored to their specific needs. For example, Vodafone used AI Studio to enhance their customer service chatbot and develop a new tool called SuperAgent, which helps summarize call center interactions to improve efficiency. Similarly, H&R Block built AI Tax Assist to guide users through the tax filing process with real-time assistance, and Sweco created SwecoGPT to boost employee productivity through automated document handling and advanced search capabilities.
Azure AI Studio supports a range of AI applications by providing tools for building, testing, and deploying AI models. It emphasizes responsible AI practices, ensuring that customer data remains private and secure while offering features like configurable filters and compliance support. Looking ahead, Azure AI Studio is set to expand its capabilities, with future developments aiming to further enhance the potential for custom AI solutions and innovations.
⚡New Cisco-Splunk observability roadmap details emerge
After acquiring Splunk, Cisco has placed Splunk’s former CEO, Gary Steele, in charge of its observability strategy. This move is part of Cisco's plan to replace its existing observability platform, known as Full-Stack Observability (FSO), with Splunk Observability Cloud. The integration will combine Splunk’s Observability Cloud with Cisco’s AppDynamics and ThousandEyes tools to enhance monitoring and performance management across cloud and network environments.
At the recent Cisco Live event, Cisco emphasized that Splunk Observability Cloud, which is more established and feature-rich, will take over from FSO. This change aims to offer a more comprehensive observability solution, integrating AppDynamics’ application performance monitoring capabilities with Splunk’s broader cloud and infrastructure monitoring tools. The merger is expected to streamline and expand observability services, though the full transition will take time and careful planning, with industry analysts noting that it could take up to two years for everything to fully align.
🛠️HackHub: Best Tools for Cloud
Batfish is a network validation tool that helps ensure the security, reliability, and compliance of network configurations. It works by analyzing the configuration files of network devices to create a detailed model of the network's behavior. This model allows Batfish to identify issues such as policy violations, configuration errors, and potential impacts of changes before they are implemented.
Primarily, Batfish is used to verify network configurations before they go live, which addresses a common challenge in network management by preventing errors that could cause problems after deployment. It doesn’t need direct access to the devices; it works with the configuration files and can be enhanced with additional network data like BGP routes or topology information.
dblab is a simple, fast, and lightweight terminal-based UI tool for managing PostgreSQL, MySQL, and SQLite3 databases. It’s written in Go, which allows it to be distributed as a single binary file that works on OSX, Linux, and Windows. It’s designed to be easy to install and use for both local and remote databases.
Mycorrhiza Wiki is a file-system based wiki engine that uses Git to keep track of changes to the content. This means you don't need a separate database, which simplifies setup and maintenance.
🛠️kashalls/external-dns-unifi-webhook
The ExternalDNS Webhook Provider for UniFi is an experimental tool that connects Kubernetes clusters to a UniFi Network controller to automatically manage DNS records. It integrates with ExternalDNS, a Kubernetes add-on for managing DNS records based on Kubernetes resources like ingresses and services, and allows it to communicate with UniFi's network infrastructure.
`encoder-run` is a Kubernetes operator designed to automate the creation and management of vector embeddings for source code. These embeddings are used for various tasks such as search and similarity checks, and the tool handles both the encoding process and the storage of results. It keeps embeddings up-to-date with changes in source code repositories, which helps with code analysis and related tasks.
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