Kubernetes Learning Week Series 13
How can we seamlessly transition node services to K8S?
This article describes how the online gaming company Games24x7 seamlessly migrated its Node.js-based services to Kubernetes (K8s) to meet the high scalability and performance demands of its products during peak seasons.
Key Points:
Previously, Games24x7 ran over 250 Node.js instances on AWS EC2 to handle workloads but decided to switch to Kubernetes for better scalability and cost efficiency.
The initial migration plan focused on moving the login page service to Kubernetes, while keeping the existing infrastructure and other applications unchanged.
The team faced challenges when packaging Nginx and Node.js into separate containers within the same pod and when using TargetGroupBinding to integrate with the existing load balancer.
Another challenge was high API latency due to CoreDNS delays, which the team resolved by implementing NodeLocal DNSCache.
The team also encountered an issue where Kubernetes cluster availability zones were inconsistent with those supported by the public load balancer, requiring modifications to the load balancer setup.
Related Interview Questions:
What were the primary motivations for migrating to Kubernetes?
What were the key challenges faced during the Kubernetes migration?
How was high API latency addressed in the Kubernetes environment?
What additional factors should be considered when aligning load balancers with availability zones?
Why I recommend not changing the kubelet root directory arbitrarily?
https://cep.dev/posts/adventure-trying-change-kubelet-rootdir/
This article discusses the potential issues that may arise when changing the kubelet root directory in a Kubernetes cluster, especially when using a CSI (Container Storage Interface) driver. The article advises against making this change, as it may disrupt communication between kubelet and the CSI driver.
Key Points:
Changing the kubelet root directory from its default location /var/lib/kubelet may break the CSI driver and other Kubernetes components.
CSI drivers expect kubelet’s Unix domain socket to be located at /var/lib/kubelet/plugins/, and modifying the root directory violates this assumption.
The author suggests a better approach: instead of changing the root directory, bind-mount a RAID volume to /var/lib/kubelet. This way, you avoid updating all existing DaemonSets that depend on the default location.
AWS follows a similar approach by configuring NVMe instance storage disks with RAID-0 and bind-mounting the kubelet and containerd state directories to a new location.
Related Interview Questions:
What issues can arise from changing the kubelet root directory?
Why is it recommended to bind-mount a RAID volume to /var/lib/kubelet instead of changing the root directory?
How does AWS handle local disk setup for Kubernetes clusters?
OpenAI’s code execution runtime and sandbox infrastructure
https://itnext.io/openais-code-execution-runtime-replicating-sandboxing-infrastructure-a2574e22dc3c
This article discusses how OpenAI’s code execution runtime uses gVisor (a user-space kernel built by Google) to provide a secure and isolated sandbox environment for executing user-submitted code. It explains the benefits of sandboxing, how gVisor achieves isolation, and how to reproduce the underlying infrastructure using Kubernetes.
Key Points:
The code execution feature allows Python code generated or modified by language models to run in a sandboxed environment.
The sandbox provides key benefits such as isolation, constraints, a predictable environment, data protection, and resource limits.
OpenAI’s code interpreter leverages gVisor, which is a user-space kernel implementing most Linux system call interfaces, ensuring secure and isolated execution.
A gVisor instance is initialized to set up a sandboxed environment along with a FastAPI service called user_machine, responsible for handling code execution requests.
The user_machine service communicates with the gVisor instance, sending input, receiving execution output, and managing timeouts and callbacks.
To reproduce this environment, one can create a new GKE cluster, enable gVisor on the node pool, and deploy a custom code execution service within the gVisor runtime.
Potential Questions of Interest:
How does OpenAI’s code execution runtime use gVisor for sandboxing?
What are the key benefits of using a sandbox for code execution?
How can Kubernetes and gVisor be used to set up a similar code execution infrastructure?
GenAI Experiment: Monitoring and Debugging Kubernetes Cluster Health
This article discusses Intuit’s experiment on improving Kubernetes cluster monitoring and debugging using GenAI, including the application of Cluster Golden Signals, k8sgpt, and Retrieval-Augmented Generation (RAG) to enhance the on-call experience and reduce issue detection and resolution time.
Key Points:
Challenges in Observability and Debugging:
Due to rapid growth, scale, and complex cluster queues, monitoring and debugging Kubernetes clusters present significant challenges.
Introduction of Cluster Golden Signals:
Provides a single-pane view of cluster health, improving issue detection.
Usage of k8sgpt:
As an open-source tool, k8sgpt is used to scan Kubernetes clusters and diagnose issues, enabling deeper debugging.
Leveraging Intuit’s GenAI Platform:
GenOS integrates public language models with Intuit-specific context, providing more accurate remediation steps.
Initial Results:
Show improvements in detecting, debugging, and resolving platform issues.
Potential Questions of Interest:
What key challenges does Intuit face in monitoring and debugging Kubernetes clusters?
How does Intuit use Cluster Golden Signals to improve issue detection?
What role do k8sgpt and Intuit’s GenAI platform play in deep debugging and issue resolution?
What are the key findings and lessons learned from Intuit’s GenAI-based tool experiments?
How I Over-Engineered My Home Kubernetes Cluster: Part 1
This article discusses how the author over-engineered their home Kubernetes cluster, including setting up a 2-node K3s cluster, configuring a cloud-based proxy server, and managing Ingress and storage. The author’s goal was to create a reliable and secure setup while exploring Kubernetes.
Key Points:
The author migrated from running services on a single Raspberry Pi to setting up a 2-node Kubernetes cluster using K3s.
A Traefik-based proxy server was deployed in the cloud to allow access to the cluster from anywhere without exposing the home IP address.
Nodes were configured to connect to the proxy server using WireGuard tunnels, and Flannel leveraged WireGuard interfaces for inter-node communication.
Ingress Configuration:
Used ingress-nginx as the Ingress Controller.
Enabled ModSecurity as a Web Application Firewall (WAF).
Configured Proxy Protocol to retain the real client IP.
Implemented structured logging for better parsing and analysis.
Storage Setup:
- Used Longhorn as a distributed storage solution.
Faced challenges managing dependencies like linux-headers and linux-modules-extra.
Potential Questions of Interest:
What were the main reasons for migrating from a single Raspberry Pi to a Kubernetes cluster?
How did the author set up a proxy server to access the cluster and expose services securely?
What key configurations were applied to ingress-nginx for enhanced security and functionality?
What distributed storage solution was used, and what challenges were encountered?
Kubernetes Storage Performance Comparison: Rook Ceph vs. Piraeus Data Storage (LINSTOR)
This article discusses different Kubernetes storage options, focusing on two specific choices: Piraeus Datastore (LINSTOR) and Rook Ceph. It provides a detailed performance comparison between these two solutions and other alternatives, along with implementation details and challenges encountered.
Key Points:
Piraeus Datastore (LINSTOR) is a cloud-native Kubernetes data store that uses Linux LVM technology and is known for its high-speed performance.
Rook Ceph, a CNCF graduated project, provides file, block, and object storage, but may exhibit higher latency and lower performance in some scenarios.
The author’s main requirements were minimal complexity, low overhead, and good performance with 4KB block sizes, which led to choosing Piraeus Datastore (LINSTOR).
The article discusses the implementation details and challenges of Rook Ceph and Piraeus Datastore (LINSTOR), including configuration options, dependencies, and decommissioning procedures.
A comprehensive performance evaluation is provided, comparing IOPS, latency, and bandwidth across Rook Ceph, Piraeus Datastore (LINSTOR), and local storage.
Potential Questions of Interest:
What are the key differences in performance and features between Rook Ceph and Piraeus Datastore (LINSTOR)?
What were the major challenges encountered during the implementation of Rook Ceph and Piraeus Datastore (LINSTOR)?
How did the author determine that Piraeus Datastore (LINSTOR) was the best choice for their specific requirements?
What are the strengths and weaknesses of Rook Ceph and Piraeus Datastore (LINSTOR) based on performance test results?