UPEOPulse
Infrastructure metrics only matter when they explain operational impact. UPEOPulse correlates system signals with ERPNext behavior so operators can detect degradation early and act with confidence.
Built for operators who want early warning, not postmortems.
Measure, correlate, and warn early
UPEOPulse collects lightweight system metrics, builds baselines, and correlates them with ERPNext queues and scheduler behavior.
UPEOPulse doesn’t just collect host metrics - it understands ERPNext workload patterns.
- CPU, memory, disk, and load with ERPNext context
- Process-level visibility for workers, scheduler, web
- Correlation with queues and background jobs
- Time-window analysis with baselines
Detect pressure before users complain or queues explode.
- Trend-based alerts, not static thresholds only
- Detect slow memory leaks and creeping load
- Surface abnormal resource-to-throughput ratios
- Highlight unusual patterns vs historical norms
Raw metrics don’t answer “why.” UPEOPulse connects signals across layers.
- Infra metrics ↔ queue backlog correlation
- Scheduler timing ↔ CPU/memory pressure
- Failure bursts ↔ resource saturation
- Clear timelines for incident diagnosis
Alerts are tied to meaning and action, not noise.
- Alerts explain what changed and why it matters
- Routing by environment and ownership
- Links to queue views and runbooks
- Cooldowns to prevent alert storms
Designed to work wherever ERPNext runs.
- Frappe Cloud, AWS, DigitalOcean, GCP, on-prem
- Lightweight agent + secure reporting
- No provider lock-in assumptions
- Consistent signals across environments
Signals that actually predict trouble
These metrics are chosen for early detection and operational relevance.
CPU usage tracked at host level and per critical ERPNext processes (workers, scheduler, web).
High CPU hides behind “system feels slow.” Saturation causes queue lag, request timeouts, and cascading failures.
Alert if CPU > 85% for 5–10 minutes or if worker CPU spikes correlate with queue backlog.
Used memory, swap activity, and OOM kill indicators correlated to ERPNext processes.
Memory leaks and spikes silently kill workers. By the time users complain, damage is already done.
Alert if memory usage > 90% or if swap activity begins unexpectedly.
Available disk, IO wait, and write/read latency on volumes used by ERPNext, Redis, and backups.
Full disks break backups, logs, queues, and databases. IO wait creates system-wide latency.
Alert if free disk < 15% or IO wait exceeds baseline by 2×.
System load compared to CPU core count, with trend analysis.
Load creeping above capacity is an early warning of runaway jobs, stuck workers, or external call blocking.
Correlation between system resource pressure and queue depth, failure rate, and throughput.
Infrastructure metrics alone don’t explain business impact. Correlation shows when infra issues hurt operations.
Resource usage and execution timing around scheduled jobs.
Schedulers often die quietly under pressure. Correlating infra stress with missed runs exposes hidden failures.
Rolling baselines for CPU, memory, disk, and load by hour/day/week.
Static thresholds create noise. Baselines reveal slow drift and unusual behavior.
What infrastructure failure really looks like
These are the patterns operators see in real ERPNext production environments.
Slow degradation over days
Symptom: System feels slower every day; no single spike explains it.
Root cause: Memory leaks, unbounded queues, or growing datasets slowly exhausting resources.
- Baseline drift in memory and load
- Resource-to-throughput ratio worsening
- Queue latency increasing without traffic growth
- Identify leaking processes or job classes
- Tune worker counts and queue separation
- Schedule restarts with evidence-based justification
Sudden saturation during peak usage
Symptom: Timeouts and failures during busy hours.
Root cause: CPU or IO saturation triggered by heavy jobs or external dependencies.
- CPU/IO spikes correlated with queue backlog
- Scheduler overruns during peak windows
- Throughput collapse under stable input
- Reschedule heavy jobs off-peak
- Split queues by runtime class
- Scale resources or workers with evidence
Invisible scheduler failure
Symptom: Scheduled jobs silently stop running.
Root cause: Scheduler process starved or killed under resource pressure.
- Missed scheduler executions
- Resource spikes preceding scheduler silence
- No execution evidence despite enabled config
- Restart scheduler with guardrails
- Protect scheduler with resource reservations
- Alert on execution absence, not config state
Want to see trouble before users do?
We’ll assess your infrastructure signals, queue behavior, and scheduler health - then show how UPEOPulse gives you early, actionable warnings.