The Tool Desk
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What CPU and memory monitoring can—and cannot—tell you
Hetzner Cloud’s provider-level graphs and guest operating-system metrics answer different questions. To understand pressure inside the Linux server running your stream software, collect guest metrics with Node Exporter and scrape them with Prometheus. Node Exporter exposes hardware and kernel metrics over HTTP; Prometheus stores samples over time, and Grafana can display them as dashboards. See the Prometheus Node Exporter guide.
These measurements can show whether the host is under sustained CPU or memory pressure, but they do not tell you by themselves whether YouTube is receiving a healthy stream. Pair resource graphs with the stream process’s service status, logs, and an end-to-end health check or heartbeat appropriate to your setup. No source establishes a CPU or memory percentage that guarantees a healthy YouTube stream: requirements vary with the software, encoding or relay workload, operating system, and server size.
Choose where to run the monitoring stack
| Approach | Setup and placement | Best fit |
|---|---|---|
| Hetzner Prometheus/Grafana app image | Lower initial installation effort; components are preinstalled on the server, but need activation after boot. Metrics and monitoring may share the stream server. | A single-server operator who wants a packaged starting point. See Hetzner’s Prometheus/Grafana app page. |
| Install and configure the exporter and Prometheus independently | More control over configuration and placement. Prometheus can run on a separate monitoring host, scraping the stream host over a restricted network path. | An operator with existing monitoring, multiple servers, or a reason to keep monitoring available if the stream server fails. The Hetzner Community tutorial describes a Debian-based setup; it dates from 2021, so current package details may differ. |
A separate monitor host is not required for one VPS, but it can keep the monitoring interface available during an outage of the stream server. Whichever approach you choose, avoid exposing the exporter endpoint to the public internet.
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Option 1: Start with Hetzner’s packaged app image
- Create a server from the Prometheus/Grafana app image. Choose it in the Hetzner Cloud Console, or provision the image through Hetzner’s API. The app page lists Prometheus, Grafana, Node Exporter, and other components.
- Connect over SSH after the server boots. Follow the activation procedure in the app image’s documentation. Preinstalled does not mean automatically activated: complete the post-boot steps before expecting metrics or dashboards to work.
- Check that the target is being scraped. In Prometheus, confirm that the Node Exporter target is up before relying on its graphs. If components share one server, remember that a failure of that server can also take down its local monitoring stack.
- Open Grafana and build or select useful panels. Plot CPU over time, available memory and swap trends, and supporting context such as filesystem space and network activity. Confirm metric names and labels against the metrics exposed by your installed version.
Option 2: Install Node Exporter and configure Prometheus
This route suits an existing monitoring setup or a separate Prometheus host. Commands and package steps depend on your Linux distribution and on whether the stream runs directly on the host or in a container; consult the current installation instructions for your OS rather than assuming the older Debian-specific tutorial applies unchanged.
- Run Node Exporter on the stream host. Install and start it using the instructions for your distribution. Node Exporter is intended to expose host-system metrics. If you run it in a container without suitable host access, its view may represent the container rather than the complete server; see the Node Exporter project documentation.
- Verify the endpoint locally. On the monitored host, run
curl http://localhost:9100/metrics. A working endpoint returns metric text, including names prefixed withnode_. If it does not, check whether the exporter service is running and listening on the expected address and port. - Add the exporter as a Prometheus target. For a single host where Prometheus can reach the exporter locally, the official guide’s example uses
localhost:9100and a 15-second scrape interval. Treat that interval as an example, not a required setting. For a separate Prometheus host, use the stream server’s private address and permit inbound TCP 9100 only from the Prometheus host. - Confirm scrape health. Check Prometheus target status and resolve any scrape error before building conclusions from dashboard panels. The Hetzner Community tutorial demonstrates checking target health and last error; its specific configuration details may need updating for current distributions.
- Keep the endpoint private. Restrict TCP 9100 with the applicable firewall or private-network controls so only the authorized scraper can reach it. Do not make Node Exporter publicly accessible just to simplify scraping.
Build graphs that show pressure over time
CPU: query a rate, not the raw counter
node_cpu_seconds_total is a cumulative counter of CPU time, not a current CPU percentage. A raw counter will generally increase as the system runs, so chart a rate over a recent time window instead. Prometheus’s guide demonstrates rate(node_cpu_seconds_total{mode="system"}[1m]). Adapt the labels and expression to the CPU meaning your panel is meant to show; this example isolates the system mode and is not, by itself, a whole-machine utilization percentage.
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Memory and swap: look for persistent change
Track available memory over time, along with swap activity where the relevant collectors and metrics are enabled. A continuing decline or growing swap activity deserves investigation, but one isolated sample does not establish that the server is out of resources. Check which process is using memory and whether the streaming workload or another service changed during the same period.
Use other host metrics as context
Filesystem capacity and network activity can help explain a change in CPU or memory, but they do not confirm that YouTube ingest is healthy. Node Exporter provides a broad set of hardware and kernel metrics; verify the names and labels on the installed exporter version before using a query. Compare graph changes with the stream process’s logs and service status around the same time.
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Set alerts from your own baseline
Start by observing a normal stream under the actual server size, operating system, and workload. Then set operator-chosen alerts for conditions such as:
- Prometheus can no longer scrape the monitoring target or Node Exporter is unavailable.
- CPU pressure remains elevated long enough to matter, rather than briefly spiking.
- Available memory stays low or continues falling, or swap activity grows.
- The streaming service stops, restarts unexpectedly, or fails an appropriate stream health check.
Choose durations and thresholds through observation and testing on your server; these are configuration choices, not validated defaults or universal YouTube limits. A brief spike can be normal. Monitoring can make resource trouble visible, but it cannot guarantee uninterrupted delivery.
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How to tell whether the server is running out of resources
- Compare against the baseline: look for sustained loss of CPU or memory headroom, not a percentage considered in isolation.
- Check timing: correlate the change with stream start, a restart, an encoding or relay change, or another workload. Use logs and service status to identify what changed.
- Separate host health from stream health: a healthy exporter target only says Prometheus can scrape metrics. It does not prove the stream reaches YouTube.
- Investigate before resizing: identify the process consuming resources and check for other workloads. If pressure persists under the intended workload, consider whether the server size or deployment needs to change.
- Check monitoring visibility: if a host-local stack disappears at the same time as the stream server, a separate monitoring host may provide a more independent view.
Troubleshoot missing or misleading metrics
Prometheus target is down
Check that Node Exporter is running, that its metrics endpoint responds locally, and that Prometheus is configured with the correct reachable address. For separate hosts, verify routing and the firewall rule for TCP 9100. Permit access only from the scraper.
The dashboard shows no data or an unexpected CPU value
Confirm the target is up, then inspect the installed endpoint’s metric names and labels. CPU time is a counter: use a rate query over a window rather than interpreting the cumulative value as current use. A query filtered to one CPU mode does not represent every kind of CPU time.
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Host graphs look normal but the YouTube stream is unhealthy
Host metrics are not an end-to-end delivery check. Inspect the streaming service’s logs and status, and use a stream health check or heartbeat suitable for the software. System metrics alone cannot establish the health of YouTube ingest.
Container metrics do not represent the whole server
Node Exporter is designed for host metrics. A container deployment without appropriate access to host information may expose a limited view. Adjust the deployment to provide the intended host visibility or run the exporter directly on the host, following the project’s documentation.
Or let it run in the cloud
If your goal is a continuous YouTube stream from uploaded recordings rather than a live camera feed, StreamNeo is a cloud alternative to running and monitoring a stream process on your Hetzner server: upload a recording or build a playlist, add your YouTube stream key once, and go live. Nothing has to stay on at home; each slot streams your upload as made, up to 4K 60fps, at one flat price per slot, and automatically recovers if YouTube drops the stream. The first day is free with no card (one free day per account). Monthly pricing is $9.99 per month. It streams to YouTube only, and UPI is available in India. Start your free StreamNeo day.
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