
During five months at Red Hat, David Grisonnet enhanced Kubernetes and OpenShift reliability and observability through targeted backend development and system programming. He improved governance and access control in the kube-state-metrics and org repositories by refining code review workflows and team memberships. In openshift/origin, he stabilized upgrade tests by introducing delays to accommodate daemonset restarts, reducing CI flakiness. David tuned memory limits for pod resize tests in kubernetes/kubernetes, mitigating OOM issues, and upgraded dependencies across multiple repositories for improved stability. Leveraging Go, YAML, and DevOps practices, he also advanced metrics instrumentation and documentation, enabling more accurate monitoring and diagnostics.
February 2026 monthly summary focusing on observability and metric accuracy improvements across Kubernetes and CRI-O. Delivered a kubelet_metrics_provider metric to reveal which metrics provider kubelet uses for container stats, enabling precise diagnostics of the metrics provider used by kubelet. In CRI-O, fixed CRI metrics labeling by removing an unused metricName parameter and adding missing labels to network metrics and container_ulimits_soft, improving label cardinality and query reliability. Overall impact: clearer visibility into runtime metrics, faster debugging, and better capacity planning. Technologies/skills demonstrated: Go instrumentation, metrics collection and labeling, code hygiene and review, adherence to observability best practices, and cross-repo collaboration.
February 2026 monthly summary focusing on observability and metric accuracy improvements across Kubernetes and CRI-O. Delivered a kubelet_metrics_provider metric to reveal which metrics provider kubelet uses for container stats, enabling precise diagnostics of the metrics provider used by kubelet. In CRI-O, fixed CRI metrics labeling by removing an unused metricName parameter and adding missing labels to network metrics and container_ulimits_soft, improving label cardinality and query reliability. Overall impact: clearer visibility into runtime metrics, faster debugging, and better capacity planning. Technologies/skills demonstrated: Go instrumentation, metrics collection and labeling, code hygiene and review, adherence to observability best practices, and cross-repo collaboration.
January 2026 monthly summary: Across kubernetes/kubernetes, kube-aggregator, dynamic-resource-allocation, kube-state-metrics, and enhancements, delivered stability, observability, and roadmap progress. Key deliveries include upgrading go-systemd to v22.7.0 across core and peripheral repos; introducing a robust Accurate HTTP Request Duration Metrics API (ObserveSince) to improve kubelet timing reliability; advancing KEP 2371 milestones for container stats; and improving documentation quality for pod metrics resource requests and limits. These changes enhance stability, performance, and compliance while reducing mismeasurement risks and aligning with Kubernetes release goals.
January 2026 monthly summary: Across kubernetes/kubernetes, kube-aggregator, dynamic-resource-allocation, kube-state-metrics, and enhancements, delivered stability, observability, and roadmap progress. Key deliveries include upgrading go-systemd to v22.7.0 across core and peripheral repos; introducing a robust Accurate HTTP Request Duration Metrics API (ObserveSince) to improve kubelet timing reliability; advancing KEP 2371 milestones for container stats; and improving documentation quality for pod metrics resource requests and limits. These changes enhance stability, performance, and compliance while reducing mismeasurement risks and aligning with Kubernetes release goals.
December 2025 monthly summary for kubernetes/kubernetes focusing on stabilizing end-to-end tests by tuning memory limits for Pod Resize tests of Guaranteed QoS pods in OpenShift/CRI-O environments. The work increases headroom to mitigate the container runtime memory footprint during pod creation/restart, reducing intermittent OOM kills and test flakiness. Commit history shows a careful two-step memory limit increase with a follow-up revert to ensure stable configuration; final state leaves the increased memory limits in place.
December 2025 monthly summary for kubernetes/kubernetes focusing on stabilizing end-to-end tests by tuning memory limits for Pod Resize tests of Guaranteed QoS pods in OpenShift/CRI-O environments. The work increases headroom to mitigate the container runtime memory footprint during pod creation/restart, reducing intermittent OOM kills and test flakiness. Commit history shows a careful two-step memory limit increase with a follow-up revert to ensure stable configuration; final state leaves the increased memory limits in place.
Month: 2025-07 — Focused on improving reliability of the OpenShift Origin upgrade path. Key achievement: added a 30-second delay after node upgrades in upgrade tests to allow daemonsets to restart, reducing flakes and improving upgrade verification stability. This change lowers false negatives in CI and accelerates readiness for production upgrades.
Month: 2025-07 — Focused on improving reliability of the OpenShift Origin upgrade path. Key achievement: added a 30-second delay after node upgrades in upgrade tests to allow daemonsets to restart, reducing flakes and improving upgrade verification stability. This change lowers false negatives in CI and accelerates readiness for production upgrades.
April 2025: Governance and access-control enhancements across kubernetes/kube-state-metrics and kubernetes/org. Delivered streamlined code review approvals, clarified ownership, and strengthened security controls through targeted changes to OWNERS and team memberships. Result: faster PR throughput, reduced review bottlenecks, and clearer accountability across critical components.
April 2025: Governance and access-control enhancements across kubernetes/kube-state-metrics and kubernetes/org. Delivered streamlined code review approvals, clarified ownership, and strengthened security controls through targeted changes to OWNERS and team memberships. Result: faster PR throughput, reduced review bottlenecks, and clearer accountability across critical components.

Overview of all repositories you've contributed to across your timeline