New observability features in Red Hat OpenShift 4.20 and Red Hat Advanced Cluster Management 2.15

New observability features in Red Hat OpenShift 4.20 and Red Hat Advanced Cluster Management 2.15 Cluster observability operator 1.3 Observability signal correlation for Red Hat OpenShift Incident detection for Red Hat OpenShift Integrate incident detection with OpenShift Lightspeed APM dashboard with Red Hat Distributed Tracing OpenShift monitoring OpenShift logging Streamlined storage with AWS S3 output Enhanced CloudWatch and S3 Integration with Flexible Authentication Loki performance troubleshooting made simple Smarter monitoring with Loki conditional alerting rules OpenTelemetry and Tracing Red Hat Build of OpenTelemetry Tempo Operator New observability features in Red Hat Advanced Cluster Management for Kubernetes Right-sizing for virtualization (Technology Preview) Explore the new features Red Hat OpenShift Container Platform | Product Trial About the authors Roger Florén Jamie Parker Vanessa Martini Eric Evans Simon Herlofsson More like this Introducing OpenShift Service Mesh 3.2 with Istio’s ambient mode How Banco do Brasil uses hyperautomation and platform engineering to drive efficiency Technically Speaking | Taming AI agents with observability You Can’t Automate Collaboration | Code Comments Keep exploring Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share The latest release of the Red Hat OpenShift cluster observability operator 1.3 introduces observability signal correlation, incident detection, application performance monitoring (APM) dashboard, and more. These features aim to revolutionize how organizations monitor, troubleshoot, and maintain containerized environments by reducing complexity and accelerating issue resolution. Advanced observability capabilities in Red Hat OpenShift have evolved significantly, with the 1.3 release introducing the general availability of two features to help organizations monitor, troubleshoot, and maintain their containerized environments: Observability signal correlation Incident detection for Red Hat OpenShift Both observability signal correlation and incident detection are integral components of Red Hat's observability troubleshooting journey initiative. This structured approach is designed to improve the efficiency and effectiveness of identifying and resolving issues within OpenShift clusters through a comprehensive suite of analytics features. All in all, the cluster observability operator (COO) serves as the deployment and management platform for these advanced observability features. The operator provides highly customizable monitoring stacks that complement the default OpenShift monitoring capabilities, offering enterprise-level functionality including long-term data retention, advanced analytics, and multi-tenancy support. These capabilities collectively transform OpenShift observability from reactive monitoring to proactive, intelligent operations management, providing organizations with the tools needed to maintain high-performance, reliable containerized environments at scale. Observability signal correlation for Red Hat OpenShift is now generally available. This feature is powered by Korrel8r , an open source correlation engine founded within Red Hat. This technology enables automatic correlation of multiple observability signals across heterogeneous data stores, including metrics from Prometheus/Thanos, logs from Loki, alerts from Alertmanager, and cluster resources from the Kubernetes API server. The correlations are then rendered in a dedicated troubleshooting panel in the OpenShift web console. The correlation capability operates as a rule-based correlation engine with an extensible rule set that can navigate diverse signal types, data models, naming conventions, and query languages stored across multiple data stores.