Performance testing Kubernetes workloads, with Stephan Schwarz

https://ku.bz/yY-FnmGfH

If you're tasked with performance testing Kubernetes workloads without much guidance, this episode offers clear, experience-based strategies that go beyond theory.

Stephan Schwarz, a DevOps engineer at iits-consulting, walks through his systematic approach to performance testing Kubernetes applications. He covers everything from defining what performance actually means, to the practical methodology of breaking individual pods to understand their limits, and navigating the complexities of Kubernetes-specific components that affect test results.

You will learn:

How to establish baseline performance metrics by systematically testing individual pods, disabling autoscaling features, and documenting each incremental change to understand real application limits

Why shared Kubernetes components skew results and how ingress controllers, service meshes, and monitoring stacks create testing challenges that require careful consideration of the entire request chain

Practical approaches to HPA configuration, including how to account for scaling latency, the time delays inherent in Kubernetes scaling operations, and planning for spare capacity based on your SLA requirements

The role of observability tools like OpenTelemetry in production environments where load testing isn't feasible, and how distributed tracing helps isolate performance bottlenecks across interdependent services

Sponsor

This episode is sponsored by Learnk8s — get started on your Kubernetes journey through comprehensive online, in-person or remote training.

More info

Find all the links and info for this episode here: https://ku.bz/yY-FnmGfH

Interested in sponsoring an episode? Learn more.

via KubeFM https://kube.fm

May 27, 2025 at 06:00AM

The Missing Link: How MCP Servers Supercharge Your AI Coding Assistant

Discover the power of Model Context Protocol (MCP) for AI-assisted software engineering! This video explores how MCP enhances Large Language Models and AI agents by providing crucial context. Learn about two essential MCP servers: Memory and Context7. See how they improve AI's ability to understand project specifics, retain information, and access up-to-date documentation. Witness practical demonstrations using Cursor, and learn how to integrate MCP servers into your workflow. Elevate your AI-assisted coding experience with MCP!

▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Sponsor: Stacklok Toolhive 🔗 https://github.com/stacklok/toolhive ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬

AIForDevelopers, #ModelContextProtocol, #LLMEnhancements

Consider joining the channel: https://www.youtube.com/c/devopstoolkit/join

▬▬▬▬▬▬ 🔗 Additional Info 🔗 ▬▬▬▬▬▬ ➡ Transcript and commands: https://devopstoolkit.live/ai/the-missing-link-how-mcp-servers-supercharge-your-ai-coding-assistant 🔗 Model Context Protocol: https://github.com/modelcontextprotocol 🎬 Outdated AI Responses? Context7 Solves LLMs' Biggest Flaw: https://youtu.be/F0MLnVgk4as

▬▬▬▬▬▬ 💰 Sponsorships 💰 ▬▬▬▬▬▬ If you are interested in sponsoring this channel, please visit https://devopstoolkit.live/sponsor for more information. Alternatively, feel free to contact me over Twitter or LinkedIn (see below).

▬▬▬▬▬▬ 👋 Contact me 👋 ▬▬▬▬▬▬ ➡ BlueSky: https://vfarcic.bsky.social ➡ LinkedIn: https://www.linkedin.com/in/viktorfarcic/

▬▬▬▬▬▬ 🚀 Other Channels 🚀 ▬▬▬▬▬▬ 🎤 Podcast: https://www.devopsparadox.com/ 💬 Live streams: https://www.youtube.com/c/DevOpsParadox

▬▬▬▬▬▬ ⏱ Timecodes ⏱ ▬▬▬▬▬▬ 00:00 MCP Intro 00:50 What is Model Context Protocol (MCP)? 06:53 Memory and Context7 MCP Servers 07:49 Stacklok Toolhive (sponsor) 09:04 Memory and Context7 MCP Servers (cont.)

via YouTube https://www.youtube.com/watch?v=n0dCFY6wMeI

Week Ending May 18, 2025

https://lwkd.info/2025/20250521

Developer News

James Sturtevant and Amim Knabben are stepping down from their roles as techincal leads in SIG Windows and Yuanliang Zhang is notimated as the new Lead

Wenjia Zhang has stepped down as the co-chair of Kubernetes SIG etcd. Siyuan Zhang is nominated to take over Wenjia’s role as the co-chair.

SIG Contributor Experience has updated the help-wanted guidelines to remove the “low barrier to entry” requirement. This improves the distinction between “good first issue” and “help-wanted” and better aligns with other open source projects. The help-wanted issues still require clear tasks, goldilocks priority and must be up-to-date.

Release Schedule

Next Deadline: v1.34 cycle starts May 19

The v1.34 release cycle has officially started this week, with a planned release date of 27th August.

Patch releases v1.33.1, 1.32.5, 1.31.9 and 1.30.13 are available. This is mostly a bugfix release, with a golang update.

Featured PRs

131299: DRA: prevent admin access claims from getting duplicate devices

This PR fixes a bug where ResourceClaims with adminAccess could be allocated the same device multiple times within a single claim; The DRA allocator now checks that each device is used only once per claim, preventing invalid CDI specs and ensuring correct behavior for device sharing with Dynamic Resource Allocation.

131345: scheduler: return UnschedulableAndUnresolvable when node capacity is insufficient

This PR updates the NodeResourcesFit plugin to return UnschedulableAndUnresolvable when a pod’s resource requests exceed a node’s allocatable capacity, even if the node is empty; This avoids unnecessary preemption attempts for nodes that can never satisfy the request, improves scheduling efficiency in large clusters, and provides clearer signals for unschedulable pods.

KEP of the Week

KEP 4247: Per-plugin callback functions for efficient requeueing in the scheduling queue

This KEP introduced the QueueingHint functionality to the Kubernetes scheduler, enabling plugins to provide more precise suggestions for when to requeue Pods. By filtering out low-impact events such as unnecessary Node updates for NodeAffinity the scheduler reduced redundant retries and improved scheduling throughput. The KEP also allowed plugins like the DRA plugin to skip backoff in specific cases, enhancing performance for Pods requiring dynamic resource allocation by avoiding unnecessary delays while waiting for device driver updates.

This KEP is tracked for beta in v1.34.

Other Merges

e2e tests for kuberc added

Scheduler improved the backoff calculation to O(1)

Response body closed after http calls in watch test

Error message improved when a pod with user namespaces is created and the runtime doesn’t support user namespaces

DRA: Reject NodePrepareResources if the cached claim UID doesn’t match resource claim

suggestChangeEmulationVersion to clarify how to test a locked feature for emulation version

kubelet removed the deprecated –cloud-config flag

Non-scheduling related errors to not lengthen the Pod scheduling backoff time

kube-log-runner adds log rotation

Scheduler introduced pInfo.GatingPlugin to filter out events more generally

Subprojects and Dependency Updates

etcd releases v3.6.0 bringing bugfixes and features like robust downgrade support, full migration to the v3store backend, Kubernetes-style feature gates, major memory optimizations and new health check endpoints for improved cluster monitoring.

Shoutouts

Josh Berkus (@jberkus): A big TY to Benjamin Wang (@Benjamin Wang) and Wenjia Zhang (@wenjiaswe) for getting Etcd 3.6 out the door, and to Tim Bannister (@LMKTFY), Ryota Sawada (@Ryota), Mario Fahlandt (@Mario Fahlandt) and Kaslin Fields (@kaslin) for helping promote it!

via Last Week in Kubernetes Development https://lwkd.info/

May 21, 2025 at 04:00PM

Managing 100s of Kubernetes Clusters using Cluster API, with Zain Malik

https://ku.bz/5PLksqVlk

Discover how to manage Kubernetes at scale with declarative infrastructure and automation principles.

Zain Malik shares his experience managing multi-tenant Kubernetes clusters with up to 30,000 pods across clusters capped at 950 nodes. He explains how his team transitioned from Terraform to Cluster API for declarative cluster lifecycle management, contributing upstream to improve AKS support while implementing GitOps workflows.

You will learn:

How to address challenges in large-scale Kubernetes operations, including node pool management inconsistencies and lengthy provisioning times

Why Cluster API provides a powerful foundation for multi-cloud cluster management, and how to extend it with custom operators for production-specific needs

How implementing GitOps principles eliminates manual intervention in critical operations like cluster upgrades

Strategies for handling production incidents and bugs when adopting emerging technologies like Cluster API

Sponsor

This episode is sponsored by Learnk8s — get started on your Kubernetes journey through comprehensive online, in-person or remote training.

More info

Find all the links and info for this episode here: https://ku.bz/5PLksqVlk

Interested in sponsoring an episode? Learn more.

via KubeFM https://kube.fm

May 20, 2025 at 06:00AM

Ep22 - Ask Me Anything About Anything with Scott Rosenberg

There are no restrictions in this AMA session. You can ask anything about DevOps, Cloud, Kubernetes, Platform Engineering, containers, or anything else. We'll have special guests Scott Rosenberg and Ramiro Berrelleza to help us out.

▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Sponsor: Codefresh 🔗 GitOps Argo CD Certifications: https://learning.codefresh.io (use "viktor" for a 50% discount) ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬

▬▬▬▬▬▬ 👋 Contact me 👋 ▬▬▬▬▬▬ ➡ BlueSky: https://vfarcic.bsky.social ➡ LinkedIn: https://www.linkedin.com/in/viktorfarcic/

▬▬▬▬▬▬ 🚀 Other Channels 🚀 ▬▬▬▬▬▬ 🎤 Podcast: https://www.devopsparadox.com/ 💬 Live streams: https://www.youtube.com/c/DevOpsParadox

via YouTube https://www.youtube.com/watch?v=7brdKxUiB9s

Outdated AI Responses? Context7 Solves LLMs' Biggest Flaw

Discover the power of AI-enhanced coding with Context7! This video explores how to overcome outdated LLM information using Context7, an MCP server that provides up-to-date documentation. See how Context7 integrates with AI agents, improving their ability to provide current, reliable information for over 11000 projects. Boost your development workflow and stay ahead with cutting-edge tools and techniques.

▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ Sponsor: Korbit AI 🔗 https://korbit.ai ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬

AIAgents #Context7 #AIDocs

Consider joining the channel: https://www.youtube.com/c/devopstoolkit/join

▬▬▬▬▬▬ 🔗 Additional Info 🔗 ▬▬▬▬▬▬ ➡ Transcript and commands: https://devopstoolkit.live/ai/outdated-ai-responses?-context7-solves-llms-biggest-flaw 🔗 Context7: https://context7.com

▬▬▬▬▬▬ 💰 Sponsorships 💰 ▬▬▬▬▬▬ If you are interested in sponsoring this channel, please visit https://devopstoolkit.live/sponsor for more information. Alternatively, feel free to contact me over Twitter or LinkedIn (see below).

▬▬▬▬▬▬ 👋 Contact me 👋 ▬▬▬▬▬▬ ➡ BlueSky: https://vfarcic.bsky.social ➡ LinkedIn: https://www.linkedin.com/in/viktorfarcic/

▬▬▬▬▬▬ 🚀 Other Channels 🚀 ▬▬▬▬▬▬ 🎤 Podcast: https://www.devopsparadox.com/ 💬 Live streams: https://www.youtube.com/c/DevOpsParadox

▬▬▬▬▬▬ ⏱ Timecodes ⏱ ▬▬▬▬▬▬ 00:00 The Problem with Models (LLMs) 01:07 Korbit AI (sponsor) 02:13 The Problem with Models (LLMs) (cont.) 02:23 Agents Using LLM Alone 04:19 Agents with Context7 MCP 07:04 What Is Context7?

via YouTube https://www.youtube.com/watch?v=DeZ-gw_aop0