Observability Beyond Kubernetes: eBPF Magic
Alex chats with Kris Buytaert, Co-founder of DevOpsDays, O11y, Inuits and pivotal instigator of the movement about why he loves using Coroot.
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EP #3: Cloud, Kubernetes, and the Evolution of DevOps - The Open Source Observability Podcast
Kris Buytaert is the Co-founder of Inuits, O11y, and ‘DevOps Days,’ an internationally-attended series of DevOps events. He is a passionate advocate of Free and Open Source Software, and is accredited by the community as being a founding instigator of the DevOps movement. In this episode we trace the history of the DevOps movement from its intersection with open source and Agile, through the evolution of Cloud technologies and tools such Docker and Kubernetes, to present day best practices for CI/CD, monitoring, and observability.
Observability vs. Monitoring
Co-founder of DevOps Days, O11y, Inuits, and pivotal instigator of the DevOps movement Kris Buytaert explains why “observability best practices” starts with functioning monitoring and common mistakes to avoid.
DevOps AI Tools: Root Cause Analysis + eBPF + Clickhouse
Watch Coroot’s Root Cause Analysis AI pinpoint the exact cause of an incident and suggest fixes in seconds.
Improve Your Observability With This CPU Metric
🐧🐝 Learn what classic CPU metrics are (Load average, Node usage, and Container CPU usage) and why Delay Accounting can provide better, kernel-level insights into your system: https://t.ly/HQrWx
#DevOps #Kubernetes #AI #tech #observability #Linux #eBPF #Sysadmin #Cloud #Monitoring
Faster, Simpler Root Cause Analysis with AI
Incidents can quickly become costly, and digging through overwhelming amounts of telemetry can take hours. AI-Powered Root Cause Analysis automatically identifies the root cause of an incident and suggests fixes in seconds, so your team can get back to development (or if they’re on call at 3am, back to sleep.)
Underrated Linux Kernel Feature: Delay Accounting
The traditional metrics you’re probably using to determine CPU time shortages lack the precision of kernel-level insights.
How to Reduce Your Cloud Costs with Coroot
Cloud costs often grow quietly until they suddenly command everyone’s attention. Gartner estimates that companies overspend on cloud services by up to 70 percent, mostly because they lack clear visibility into where the money is actually being spent. Cloud invoices speak the language of infrastructure: nodes, instance types, regions, volumes, and egress. Engineering teams speak the language of services, deployments, and code.
Memory stall: the agony before OOM
When we set a memory limit for a container, the expectation is simple: if the app leaks memory, the OOM killer steps in, the container dies, Kubernetes restarts it, done. But reality is messier. As a container gets close to its memory limit, allocations don’t just fail instantly. They get slower. The kernel tries to reclaim memory inside the cgroup, and that takes time. Instead of being killed right away, your app just crawls.
Instrumenting the Node.js event loop with eBPF
Recently, I was testing Coroot’s AI Root Cause Analysis on failure scenarios from the OpenTelemetry demo. One of them, loadgeneratorFloodHomepage, simulates a flood of excessive requests. As expected, it caused a latency degradation across the stack. Coroot’s RCA highlighted how the latency cascaded through all dependent services. At the same time, we noticed a moderate increase in CPU usage for the frontend service and the node itself.