Observability & Analysis
Every cluster connected to a CodeNOW account comes with a pre-configured set of observability tools for collecting logs, traces, service mesh metrics, and real-time cluster data. These tools serve two primary purposes:
- Application operations — active monitoring and alerting to maintain the health and performance of running applications in production and other environments
- Debugging and performance analysis — diagnosing issues, investigating errors, and resolving performance problems across application components
Dataplane Separation
Under each observability category, all Dataplanes connected to the account are listed separately. This reflects a fundamental architectural principle: all observability data — logs, traces, metrics — is always stored either directly on the Dataplane or in a storage accessible exclusively by that Dataplane. There is strict isolation between clusters, so data from one Dataplane is never mixed with or accessible from another.
Default Tooling
The sub-sections in this chapter describe the default tools that are deployed as part of a CodeNOW Dataplane. However, since each Dataplane can be configured with its own specific tooling, the exact setup may differ from what is described here.
Regardless of the tooling used, every Dataplane is configured to expose the correct dashboard endpoints so that direct links from the Self-Service Portal work as expected — clicking through from a component or environment always opens the relevant dashboard for that Dataplane.
Observability Tools
| Section | Tooling | Purpose |
|---|---|---|
| Logging | Grafana + Loki | Explore and query application and infrastructure logs |
| Tracing | Grafana + Tempo | Distributed tracing and request flow analysis using TraceQL |
| Service Mesh | Kiali + Istio | Service mesh topology, traffic metrics, and configuration validation |
| Cluster Live Monitoring | Grafana + Mimir | Real-time cluster and application metrics dashboards using PromQL |