System Performance Monitoring Dashboard — Overview & Implementation
A concise walkthrough of a Java application performance dashboard organized across nine strategic sections, plus plugins, configuration, and next steps. (Source: uploaded deck.) :contentReference[oaicite:0]{index=0}
Executive summary
This dashboard provides comprehensive visibility into a Java-based application’s performance and operational health.
Organized across nine strategic categories, it enables quick identification of issues and efficient troubleshooting. :contentReference[oaicite:1]{index=1}
Dashboard organization
The dashboard is logically organized into nine distinct categories, each focused on a specific aspect of system performance and health. These categories help teams jump to the right telemetry quickly:
- Basic System Statistics
- JVM Memory Performance
- I/O Operations
- Garbage Collection Metrics
- Database Connection Pool Performance
- HTTP Request Handling
- Tomcat Server Health
- Logging Activity
- Service Status Indicators
Organizational breakdown per the source slides. :contentReference[oaicite:2]{index=2}
Key features
- Unified view of system & application metrics for fast root-cause analysis.
- Breakdown by JVM, Tomcat, DB pool, HTTP latency and error rates.
- Alerting-ready layout — identify thresholds and anomalies quickly.
- Designed to support both operational teams and engineering stakeholders.
Plugins & configuration
The dashboard integrates with modern observability tooling and Java instrumentation:
- OpenTelemetry Java Agent for tracing and metrics capture.
- JVM Micrometer metrics exported to Prometheus (or other metric sinks).
- Tomcat-specific stats (threads, connectors) and HikariCP pool metrics.
- Custom filters to capture application-specific metrics and dimensions.
Metric categories (high level)
Below is a compact view of the recommended metrics per section. Use this as a checklist when building or validating dashboards.
Basic System
- CPU usage (host & process)
- Memory usage (host)
- Load average
JVM Memory
- Heap usage (young/old)
- Non-heap memory
- Memory pressure trends
I/O Operations
- Disk read/write throughput
- Filesystem latency
- Network I/O
Garbage Collection
- GC pause times
- GC frequency
- Young vs.old collector stats
DB Connection Pool
- Active vs idle connections
- Connection wait times
- Pool exhaustion alerts
HTTP Requests
- Request rate (RPS)
- P95/P99 latency
- 4xx / 5xx error rates
Tomcat Health
- Thread pool usage
- Active sessions
- Connector errors
Logging Activity
- Log rates by level (ERROR/WARN/INFO)
- Top error messages
- Correlation id traces
Service Status
- Health check status
- Dependency availability (DB, caches)
- Release/version indicators
Future initiatives
- Expand Micrometer/JVM metrics and integrate with Prometheus for long-term retention.
- Add custom filters and application-specific metrics for deeper observability.
- Enhance Tomcat and HikariCP statistics capture for DB & thread-level insights.
- Create pre-built alert rules and runbooks for common incident types.
Next steps & recommendations
- Instrument the application with OpenTelemetry Java Agent and Micrometer exporters.
- Validate each metric against known load scenarios (stress, spike, soak).
- Implement dashboards per environment (dev/stage/prod) and set sensible thresholds.
- Automate dashboards and alert rules as code (Grafana/Prometheus/Alertmanager dashboards in Git).
- Run a runbook exercise with simulated incidents to validate operability.