Rubber Ducky System Monitor: A Beginner’s Guide to Real-Time AlertsKeeping servers and services healthy requires more than occasional checks — you need fast, reliable alerts when something goes wrong. Rubber Ducky System Monitor is a lightweight, developer-friendly monitoring tool designed to give clear, actionable real-time alerts without the complexity of enterprise platforms. This guide walks you through what Rubber Ducky offers, how it works, and how to set it up for effective alerting.
What is Rubber Ducky System Monitor?
Rubber Ducky System Monitor is a minimal, extensible monitoring solution focused on simplicity and quick feedback. It aims to reduce noise and surface only meaningful issues, making it suitable for small teams, hobby projects, or as an embedded component in larger systems.
Key characteristics:
- Lightweight: small resource footprint, easy to deploy on single servers or containers.
- Real-time alerts: near-instant notifications when metrics cross thresholds or checks fail.
- Pluggable checks: supports custom probes for CPU, memory, disk, network, services, HTTP endpoints, and more.
- Flexible notification channels: integrates with e-mail, Slack, Telegram, webhooks, and more.
- Simple UI and API: quick-to-read dashboards and a REST API for automation.
Why choose Rubber Ducky?
If you’re deciding between complex, feature-heavy systems and a simple alerting tool, Rubber Ducky sits in the middle. Use cases where it shines:
- Small infrastructure where running Prometheus + Grafana feels overkill.
- Development environments where quick feedback on deployments is needed.
- Edge devices or containers with tight resource limits.
- Teams that prefer straightforward alert rules and minimal configuration.
Core concepts
Understanding these basic concepts will help you configure alerts properly:
- Checks: Individual probes that collect a metric or verify a condition (e.g., ping, HTTP status, process running).
- Targets: Hosts or services the checks run against.
- Thresholds: Numeric or state-based conditions that trigger alerts (e.g., CPU > 90% for 5 minutes).
- Alerting rules: Combination of checks and thresholds plus suppression or escalation behavior.
- Notification channels: Destinations for alerts (Slack, email, webhook).
- Escalation policies: Rules to escalate unresolved alerts to additional recipients or channels.
- Silences/maintenance windows: Temporarily mute alerts during planned work.
Installation and quick start
Rubber Ducky installs easily on Linux, macOS, and in containers. The example below outlines a typical Docker-based setup, plus a minimal local install.
Docker (quick start)
- Pull the image:
docker pull rubberducky/sysmon:latest - Run with a basic config volume:
docker run -d --name rubberducky -v /path/to/rubberducky.yml:/app/config.yml -p 8080:8080 rubberducky/sysmon:latest - Visit http://localhost:8080 to access the UI.
Local binary (Linux/macOS)
- Download and make executable:
wget https://example.com/rubberducky/sysmon/latest/linux-amd64 -O rubberducky chmod +x rubberducky ./rubberducky --config ./config.yml
Minimal config example (YAML)
server: port: 8080 checks: - name: uptime type: ping target: 192.168.1.10 interval: 30s alert: condition: fail notify: ["slack"] - name: web-health type: http target: https://example.com/health interval: 15s alert: condition: status != 200 notify: ["email"] Creating effective real-time alerts
Real-time alerting is useful only when tuned; otherwise you’ll drown in noise. Follow these practices:
- Set sensible thresholds: avoid single-sample triggers; require a sustained condition (e.g., CPU > 90% for 2m).
- Use multi-condition rules: combine metrics when possible (e.g., high CPU + high load).
- Tier alerts by severity: page on critical, notify on warnings.
- Add context to alerts: include host, service, recent metric samples, and suggested actions.
- Implement deduplication and grouping: collapse repeated alerts for the same issue.
- Silence during deployments: automatically suppress alerts when you expect transient failures.
Example alert rule with suppression:
alert: name: HighCPU condition: cpu.usage > 90% for 2m severity: critical notify: [pagerduty, slack] deduplicate: 5m silence_when: tag=deploying Notification channels and integrations
Rubber Ducky supports common channels out of the box and custom webhooks for anything else.
Built-in:
- Slack (incoming webhooks, rich attachments)
- Email (SMTP)
- Telegram bots
- PagerDuty
- Webhooks (post JSON to arbitrary endpoints)
Integration tips:
- Use Slack threads for follow-ups to keep alerts consolidated.
- Send critical alerts to on-call tools (PagerDuty) and lower-priority to team channels.
- For custom automations, use webhooks to trigger remediation scripts.
Dashboards and context
The UI provides a concise dashboard showing active alerts, recent incidents, and per-check health. Useful features:
- Timeline view of incidents with duration and annotations.
- Per-target metrics graphs (last 1h, 24h, 7d).
- Quick actions: acknowledge, silence, escalate, or run a one-off check.
Add context to checks by tagging targets (env:production, role:db). Tags allow focused views and targeted silences.
Automating remediation
Real-time alerts get more valuable when paired with automated responses:
- Auto-restart a failed process via a webhook trigger.
- Scale up containers when CPU > threshold and scale down when healthy.
- Run health-repair scripts on first failure, then notify if unsuccessful.
Caution: start with conservative automations, and always log actions with links in alerts.
Troubleshooting common issues
- Missing alerts: verify notification credentials, check network access to channels, and ensure rules aren’t silenced.
- False positives: increase evaluation window, add secondary conditions, and validate sensor accuracy.
- High resource usage: lower check frequency, use local agents to aggregate, or limit metric retention.
Security considerations
- Secure API and UI with TLS and strong auth (OIDC or API keys).
- Restrict webhooks’ endpoints and validate payloads.
- Use role-based access for acknowledging/escalating alerts.
- Ensure logs don’t leak secrets or sensitive payloads.
When to migrate to a larger platform
Rubber Ducky is ideal for small-to-medium environments. Consider migrating if you need:
- Long-term, high-cardinality metric storage at scale.
- Complex query languages, advanced correlation, or ML-based anomaly detection.
- Enterprise governance, multi-team tenant isolation, or compliance auditing.
Example: end-to-end setup for a small web app
- Deploy Rubber Ducky in Docker on a monitoring host.
- Add checks:
- HTTP health check for app endpoints (15s interval).
- Process check for worker queues (30s).
- Disk usage check for log volume (5m).
- Configure notifications:
- Critical -> PagerDuty + Slack #oncall
- Warning -> Slack #devops
- Tag services: env=production, app=web, team=backend.
- Create an escalation policy: 0–5m -> on-call, 5–20m -> manager + on-call, >20m -> exec.
- Add a silence during deployments using CI integration that toggles a deploy tag.
Final notes
Rubber Ducky System Monitor emphasizes speed, clarity, and minimal configuration so teams can spend less time tuning monitoring and more time fixing issues. With sensible alerting practices, appropriate integrations, and basic automations, it provides a reliable real-time alerting backbone for small and mid-sized systems.
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