Grafana insights

Server Unresponsiveness from High Load or Network Issuescritical

Nomad server becomes unresponsive with CLI commands hanging, Web UI failing to load, or API requests not processing, typically due to CPU/memory exhaustion or network connectivity problems.

Aggressive Dashboard Auto-Refresh Overloading Data Sourceswarning

Low dashboard refresh intervals (e.g., every 30 seconds or less) create unnecessary query load on data sources, degrading performance and potentially causing timeouts or errors, especially when querying long time ranges or high-cardinality data.

Critical node detection indicating potential data unavailabilitycritical

Nodes become critical when their failure would cause replica unavailability. Detecting critical nodes before termination prevents data loss and service disruption. This is indicated by the /_status/critical_nodes endpoint showing non-empty criticalNodes array.

Anthropic API Latency Spike Detectionwarning

High Anthropic API latency (>500ms) signals backend strain or network issues. Early detection prevents cascading failures in AI-powered applications.

Grafana dashboards exhibit slow performance (high read latency, sluggish UI) even when infrastructure metrics (CPU, memory) appear healthy. This occurs when background compaction tasks, query overload, or excessive time-series rendering saturate resources invisibly.

Cassandra Compaction Backlog Hidden Latencywarning

Rising pending compaction tasks silently degrade read performance by forcing queries to scan more SSTables. This storage debt accumulates invisibly until read latency spikes occur.

JVM Heap Exhaustion Pre-Failure Signalcritical

Heap usage climbing to 80-90% without GC relief indicates impending OutOfMemoryError. Cassandra cannot process requests during heap thrashing, causing cascading application timeouts.

JMX Metrics Collection Gap Blinding Cassandra-Backed Grafana Monitoringcritical

Grafana instances using Cassandra as a backend can experience silent performance degradation when JMX metrics (compaction tasks, heap usage, GC pauses) are not collected. Without JMX visibility, operators miss early warnings of heap exhaustion, compaction backlog, or GC thrashing that manifest as sudden dashboard failures.

Change Failure Rate Increase from Inadequate Pre-Production Testingcritical

Deployments to production fail or require rollback at a higher rate than expected, indicating gaps in pre-production testing coverage, environment parity issues, or insufficient deployment validation.

Reactive Monitoring Without Predictive Capacity Insightsinfo

Traditional BIG-IP monitoring focuses on current state without historical trending, preventing teams from proactively addressing resource exhaustion (bandwidth, connections, CPU) before performance degradation or outages occur.

Time to Restore Service Degradation from Missing Incident Correlationcritical

Mean Time to Recovery (MTTR) increases because deployment-related incidents aren't automatically correlated with the causing deployment, delaying identification of the problematic change and slowing rollback decisions.

Physical cluster replication lag escalationwarning

Growing replication lag in CockroachDB PCR indicates the standby cluster cannot keep pace with primary writes. Unchecked lag degrades failover readiness and increases potential data loss window.

Physical Cluster Replication Lag Silently Growing Before Failovercritical

In CockroachDB physical cluster replication (PCR) setups monitored via Grafana, replication lag (replicated_time vs. actual time) can grow unnoticed if only DB Console is used. During failover, this lag translates to data loss or extended RTO, as the standby cluster is further behind than expected.