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Host

Host dashboard during an active session — fourteen KPI cards across two rows (Heap used, Process CPU, System CPU, Load avg 1m, Threads live, Uptime, Classes loaded, Open file descriptors, Disk free, GC overhead, Total GC pause, GC count lifetime, Process CPU time, Buffer pools) and nine charts (Heap usage over time, CPU usage over time, Threads over time, GC activity over time, Threads by state, Heap pool usage, Heap retention after GC, GC concurrent phase time, Buffer pool used vs capacity)

Host tab during an active chat session — fed by the parallel SystemMetricsCollector pipeline which samples MeterRegistry on a fixed cadence and retains rolling history independently of the trace ring buffer. Even quiet servers show live heap, GC, and thread movement.

Purpose — operational JVM and OS health. Independent of any trace activity — even a quiet server will show heap, GC, and CPU curves.

When to look here

  • "Is there a memory leak?" — Heap usage over time (climbing without GC drops) + Heap retention after GC (% retained climbing).
  • "Why is the app slow?" — Process CPU + System CPU + Threads by state.
  • "Did the JVM hit a long pause?" — GC activity over time + Total GC pause KPI.
  • "How long has this instance been up?" — Uptime KPI.
  • "Am I running out of file descriptors?" — Open file descriptors KPI.
  • "Is disk filling up?" — Disk free KPI.
  • "Are buffer pools healthy (Direct / Mapped)?" — Buffer pool used vs capacity.

Data source

SystemMetricsCollector samples Spring Boot Actuator MeterRegistry on a scheduled cadence; SystemMetricsRingBuffer stores the snapshots; SystemMetricsTimeSeries derives the charts. Does not flow through the trace pipeline, so noisy chat activity cannot crowd out system metric history.

Controls

Host reads the Observability global refresh interval but ignores the time window — values are always live, with rolling history retained by the dedicated SystemMetricsRingBuffer. No tab-specific controls.

KPI cards (fourteen)

Card Shows Source
Heap used Heap usage percent of max, plus absolute MB jvm.memory.used (heap) / jvm.memory.max (heap)
Process CPU Current process CPU percentage process.cpu.usage
System CPU Current host CPU percentage system.cpu.usage
Load avg (1m) 1-minute load average system.load.average.1m
Threads (live) Currently alive JVM threads jvm.threads.live
Uptime Process uptime, human-formatted process.uptime
Classes loaded Currently loaded class count (also surfaces unloaded total) jvm.classes.loaded / jvm.classes.unloaded
Open file descriptors Current open FDs vs max process.files.open / process.files.max
Disk free Disk free / disk total disk.free / disk.total
GC overhead Percentage of wall time spent in GC Derived from jvm.gc.pause sum vs uptime
Total GC pause Cumulative GC pause duration jvm.gc.pause sum
GC count (lifetime) Cumulative GC count across generations jvm.gc.pause count
Process CPU time (cum.) Cumulative CPU time consumed by the process process.cpu.time
Buffer pools Summary of direct / mapped buffer pool usage jvm.buffer.memory.used and jvm.buffer.count

Charts (nine)

Chart Type Reading
Heap usage over time Rolling line (MB) Climbing without GC drops → leak suspicion
CPU usage over time Multi-line (process + system) Process tracks workload; system spikes hint at neighbour processes
Threads over time Rolling line Spikes correlate with concurrent workload bursts
GC activity over time Bar (count) + line (pause) Sustained pause time growing → tune GC or reduce allocation
Threads by state Donut (RUNNABLE / WAITING / BLOCKED / …) High BLOCKED → contention; high WAITING → idle pools
Heap pool usage Horizontal bar (Eden / Old Gen / Survivor / …) Old Gen growing without recovery → suspect long-lived leak
Heap retention after GC (%) Horizontal bar High retention after a Full GC → strong leak signal
GC concurrent phase time Horizontal bar (G1 background phases) Long concurrent phases → fragmentation pressure
Buffer pool used vs capacity Horizontal stacked bar Direct buffers high → NIO-heavy workloads (e.g. HTTP transports)

Cross-references

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