The Tool Desk
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What to monitor—and what the numbers mean
Hetzner Cloud provider graphs and guest-level monitoring answer different questions. For the resources available to the operating system running your stream, collect metrics from within that guest. Node Exporter exposes hardware and kernel metrics over HTTP, and Prometheus scrapes them for queries and alerts. Grafana can then display current values and trends.
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- CPU:
node_cpu_seconds_totalis a cumulative counter, not a current utilization percentage. Interpret its change over a time window with a rate query. - Memory: Follow available memory over time, and check swap activity if the relevant collectors are enabled. A persistent decline or growing swap use deserves investigation, but there is no source-backed YouTube-specific safe-memory percentage.
- Supporting context: Filesystem space and network activity can help explain changes. Confirm the metric names and labels on the installed Node Exporter version before building queries around them.
- Stream state: A CPU or memory graph cannot confirm that YouTube ingest is healthy. Pair host metrics with the stream service’s status, logs, or a separate heartbeat check.
Resource needs vary with the streaming software, whether the server encodes or relays video, other workloads, operating system, and server size. Establish a baseline while the actual stream runs normally; investigate sustained headroom loss, abrupt changes, and service failures rather than treating a brief spike as proof of trouble.
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| Option | Setup and placement | Best fit |
|---|---|---|
| Hetzner Prometheus/Grafana app image | Lower initial installation effort; components are preinstalled but require activation after boot. If kept on the stream server, monitoring may be unavailable when that server is down. | A single-server operator who wants a packaged starting point. |
| Install and configure components independently | More control over configuration and placement. Prometheus can run on a separate monitoring host, requiring private network or firewall configuration to reach the exporter. | An operator with existing monitoring, multiple servers, or a need to keep monitoring separate from the stream host. |
Hetzner’s app-image page, last changed on 2024-11-06, lists Prometheus, Grafana, Node Exporter, and other components, and describes provisioning through the Console or API: Hetzner Prometheus/Grafana app. The API reference identifies a server metrics endpoint but does not establish that it provides the guest memory metrics needed here; use guest-level collection for that purpose: Hetzner Cloud API.
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Set up monitoring on Hetzner Cloud
Option A: Start with Hetzner’s app image
- When creating a server, choose the Prometheus/Grafana app image in the Hetzner Cloud Console, or provision it through the API. The precise interface labels may change; consult the current app-image page.
- After boot, connect over SSH and follow the app page’s activation procedure. The software is preinstalled, not automatically activated on boot. Do not assume dashboards or collection are working until you have verified the services.
- Open Grafana and Prometheus only through an access method appropriate to your network and security setup. Keep metrics and administrative interfaces from being exposed unnecessarily.
- Confirm Prometheus can scrape Node Exporter and that its target is healthy before relying on Grafana panels.
Option B: Run Node Exporter and configure Prometheus
The Prometheus guide demonstrates running Node Exporter on the monitored Linux host, checking its metrics endpoint, then adding it as a Prometheus scrape target. Its example uses a 15-second scrape interval; that is an example, not a mandatory setting for every stream. Follow the installation instructions for your distribution and the current exporter release rather than relying on an old package command.
- Install and run Node Exporter on the Linux host that runs the stream. Node Exporter is designed to expose host-system metrics; a container deployment without suitable host access may show the container rather than the complete server.
- On the monitored host, verify the local endpoint with
curl http://localhost:9100/metrics. A working response contains metrics with names prefixed bynode_. - In Prometheus’s configuration, add the exporter as a scrape target. For a local, single-host setup, the official guide’s example target is
localhost:9100. Adapt the target to your actual deployment. - Reload or restart Prometheus as required by your installation, then check the target’s health and last error in Prometheus before building dashboards. Hetzner Community’s 2021 tutorial shows this general workflow, but distribution and package details may have changed: Hetzner Community Prometheus metrics stack tutorial.
Keep port 9100 private
Do not expose Node Exporter’s TCP port 9100 to the public Internet. For separate stream and monitoring hosts, point Prometheus at the stream server’s private address and allow inbound TCP 9100 only from the Prometheus host or an appropriately restricted private network. Hetzner’s tutorial discusses allowing the port for scraping; the access rule should be narrow, not an open public endpoint.
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Build useful CPU and memory views
CPU: graph a rate, not the raw counter
The official Prometheus guide demonstrates this expression for recent system-mode CPU time rate: rate(node_cpu_seconds_total{mode="system"}[1m]). It is an example query, not a complete all-mode utilization percentage. For a dashboard that answers a different question—such as total CPU activity—adapt the labels and aggregation to that purpose, and verify the result against the metrics exposed by your installed version. A counter’s raw value keeps accumulating, so plotting it as if it were current CPU use is misleading.
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Compare CPU patterns during a normal stream with patterns around stream startup, interruptions, and other scheduled workloads. A rising sustained rate can indicate pressure, but interpreting it depends on the number of CPU cores and the stream process’s work; do not apply a universal threshold as a YouTube limit.
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Memory: follow available capacity and swap
Put available memory on a time-series panel and look for a sustained decline that coincides with stream activity or another workload. If swap metrics are collected, watch for growing use or activity over time. A single low-looking reading is not enough to diagnose a problem; inspect the stream process and other services, and confirm the relevant metrics and labels on the server.
Check scrape health and correlate with the stream
Use Prometheus target health to distinguish “the server is under pressure” from “Prometheus stopped receiving metrics.” The Hetzner Community tutorial demonstrates checking target health and the last scrape error. When a graph changes around stream start or a failure, line it up with the streaming service’s logs and status. Host metrics alone do not establish whether YouTube ingest is functioning.
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Alert on sustained trouble, not a made-up universal limit
Prometheus and Grafana provide components for monitoring, but the cited setup sources do not prescribe validated alert rules or thresholds for YouTube streaming. Set thresholds by observing and testing the actual server and workload. Where possible, require a duration or trend so a short spike does not trigger the same response as sustained pressure.
- Monitoring target down: Alert if Prometheus cannot scrape the exporter; check the exporter service, network path, firewall, and target address.
- Sustained CPU pressure: Choose a level and duration based on your measured baseline and workload tests; investigate the stream process and competing work.
- Low available memory or growing swap: Set operator-defined conditions based on the server’s normal pattern; identify the processes consuming memory before resizing.
- Stream service failure: Monitor the service or a heartbeat independently. A healthy Node Exporter target does not mean the stream itself is live.
Troubleshoot common monitoring gaps
Prometheus shows the target as down
Check that Node Exporter is running, the Prometheus target address is correct, and TCP 9100 is reachable from the Prometheus host. If monitoring is on another server, verify its private network route and the restricted firewall rule. Read the target’s last error rather than treating an empty graph as zero resource use.
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The endpoint responds, but host metrics look incomplete
Confirm that Node Exporter is observing the host. A container without suitable host access may expose only its own view. Also inspect the actual /metrics output: collectors, metric names, and labels can vary with configuration and version.
CPU appears to climb forever
You may be graphing node_cpu_seconds_total directly. It is cumulative. Use a rate over a recent window and select the modes or aggregation appropriate to the panel’s intended meaning.
The server graphs look normal while the stream is offline
That is possible: host CPU and memory are not end-to-end YouTube health checks. Check the streaming process, its logs, and its service status; add an independent heartbeat or stream-health check if continuity needs to be monitored.
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If Prometheus and Grafana are on the same server as the stream, a host outage can take the monitoring view with it. Consider a separate monitoring host if visibility through a stream-server outage matters; protect the exporter endpoint with a private route or source-restricted firewall rule.
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