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Commands and health-check behavior vary by product, operating system, container runtime, and version. The examples below are labeled for Docker Compose, systemd, Kubernetes, or a specific application; adapt service names, endpoints, and recovery steps to the software you actually run.
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Build a symptom you can reproduce
“Slow” is not a diagnosis. Write down exactly what a user experiences and make the observation repeatable before restarting anything.
Describe the affected operation
- Scope: Is the first page load slow, one action slow, streaming delayed, background jobs stuck, or every request affected?
- Audience: Does it affect every user, one account, one geographic location, or only requests through a particular hostname?
- Timing: When did it begin, and does it follow a release, configuration change, backup, migration, certificate renewal, traffic increase, or host reboot?
- Failure mode: Record time to first byte, total response time, HTTP status, timeout text, disconnects, and whether a retry succeeds.
Choose one request or job that reliably demonstrates the problem. Record the timestamp, URL or operation, client location, authentication state, cache state, response code, and duration. A browser waterfall, an application request ID, or a command-line timing result can provide a useful reference. Repeat the same test after every change; otherwise an improvement may simply reflect a different workload or a warm cache.
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Collect a small evidence bundle first
Before changing configuration, capture the service state, logs covering the incident window, and a direct reachability check. NetBird’s self-hosting troubleshooting guidance describes this sequence and notes that “Almost every self-hosted issue is faster to resolve with service status and logs in hand.” See its self-hosting troubleshooting guide for the product’s current service names and examples.
What to save
- The exact reproduction time, request, response code, and measured duration.
- Application, proxy, database, and operating-system logs from several minutes before and after the event.
- Container or pod restarts, exit codes, health status, deployment events, and recent image or package versions.
- CPU, memory, disk, network, and process observations taken during the slowdown rather than hours later.
- Recent changes: commits, environment variables, certificates, DNS records, firewall rules, backups, and scheduled jobs.
Preserve the original logs before rotating or restarting a service. Redact credentials, cookies, tokens, private keys, and personal data before sharing an incident bundle.
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Check whether the process is running—and what it says
A process can be alive while requests fail, and a container can be restarting while a dashboard briefly shows it as “up.” Check both the supervisor’s state and the application’s own messages.
Docker Compose example
For a Compose deployment, list service state and then follow the relevant service log:
- Run
docker compose psfrom the directory containing the deployed Compose file. - Run
docker compose logs --since=30m <service-name>, replacing<service-name>with the actual service name. - For a live incident, use
docker compose logs -f <service-name>and reproduce the request once. - Record restart counts, exit status, health status, and whether errors coincide with the measured request.
NetBird’s documentation uses docker compose ps and docker compose logs -f netbird-server as product-specific examples. Its service names may differ by release and installation. Configuration, authentication, and migration errors often appear in service logs, so inspect those messages before changing resource limits.
systemd example
For a Linux service managed by systemd, replace the unit name with the one installed on your host:
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- Check state with
systemctl status <unit-name>. - Read the recent journal with
journalctl -u <unit-name> --since "30 minutes ago". - Follow new entries while reproducing the issue with
journalctl -u <unit-name> -f. - Check the service’s documented diagnostic directory and retention policy.
GitHub’s self-hosted runner troubleshooting documentation demonstrates journal inspection and a runner-specific diagnostic log directory. Those commands and paths are for GitHub runners, not a generic application. Ephemeral runner deployments must forward and preserve logs elsewhere because the runner filesystem may disappear when the instance is replaced.
Interpret restarts carefully
A restart is a symptom, not proof that the application needed more memory. Correlate the restart timestamp with an out-of-memory event, failed health probe, host reboot, deployment, or administrator action. A crash loop can be caused by an invalid secret, a missing migration, an unavailable dependency, or a port conflict.
Test the application and reverse-proxy path separately
Users normally reach an app through DNS, TLS termination, a reverse proxy or ingress, and then the application. Test a safe local endpoint and the public hostname independently so you know which boundary adds the delay.
Direct test from the host
If the application listens on a local port, query its documented health or status endpoint from the host or an adjacent container. Use a request that does not mutate data. Capture status and timing, for example with the curl options supported by your environment. Avoid exposing an internal port to the internet merely to make this test.
Proxied test from the user path
Request the public HTTPS hostname through the normal reverse proxy and compare it with the direct result. NetBird documents requesting its API through the public domain to verify proxy-to-API reachability; see its troubleshooting page for the product-specific endpoint.
If the direct request succeeds but the proxied request fails or is slower, inspect, in order:
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- DNS resolution and whether different clients receive different addresses.
- TLS certificate validity, protocol negotiation, and renewal history.
- Proxy route matching, upstream address, port, and connection timeouts.
- Proxy-to-application connectivity from the proxy’s network namespace.
- Authentication middleware, request-size limits, buffering, compression, and WebSocket or streaming settings.
- Load-balancer target health and whether only one backend is failing.
Compare client-to-proxy, proxy-to-application, and application-to-dependency timings where your logs expose them. Repeat each comparison with the same request, cache state, and workload. A single fast direct request does not prove that the public path is healthy for all users.
Use health checks without confusing liveness and readiness
A health endpoint answers a narrow question: can the process respond to this probe? It may not answer whether the app can query its database, reach an external API, or complete a real user operation.
Liveness versus dependency readiness
Microsoft’s ASP.NET Core health-check guidance explains that checks can test dependencies such as databases and external service endpoints. Use separate endpoints or check groups when the framework supports them:
- Liveness: a cheap process check that confirms the application can accept work. It should not fail merely because a temporary dependency is unavailable.
- Readiness: a dependency-aware check used to decide whether the instance should receive traffic.
- Deep diagnostic check: an operator-only test that exercises additional dependencies or a representative operation without being used to trigger automatic restarts.
Orchestrators and load balancers may restart a container, stop a rollout, or remove an instance from service after a failed probe. Verify the exact semantics before changing probe intervals, timeouts, or failure thresholds. A dependency check that is too broad can make a responsive app appear dead; a liveness-only check can leave a broken database hidden.
Product-specific endpoint caveat
OpenProject warns that adding basic authentication to its /health_checks endpoint can break existing Docker Compose and Kubernetes container health services. This is an OpenProject-specific compatibility detail documented at its monitoring page, not a universal rule. Do not protect or rename a probe until you have checked how the deployed orchestrator calls it.
Correlate resource metrics with the exact slow period
Resource graphs are useful only when their timestamps line up with the symptom. Compare the affected interval with a normal baseline, the workload, and the limits imposed on the container or virtual machine.
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Signals to collect
| Layer | Signals | What a correlation can suggest |
|---|---|---|
| Host CPU | Utilization, load average, iowait, and virtual-machine steal time | Compute contention, storage waits, or noisy-neighbor effects |
| Memory | Used memory, working set, swap activity, OOM events, and process growth | Leaks, cache pressure, limits being hit, or reclaim activity |
| Storage | Free capacity, read/write throughput, latency, and filesystem errors | Full volumes, slow disks, blocked writes, or database pressure |
| Network | Throughput, errors, drops, retransmits, and connection counts | Link saturation, packet problems, or connection-pool exhaustion |
| Containers or pods | Per-workload CPU, memory, restarts, throttling, and network use | One workload consuming a disproportionate share |
Kubernetes v1.37 defines container_cpu_usage_seconds_total as cumulative container CPU time measured in core-seconds and container_memory_working_set_bytes as current working-set memory. Its metrics reference also documents node CPU and working-set memory metrics. Use the metric definitions, not just dashboard labels, when calculating rates or comparing pods.
Watchflare’s self-hosted monitoring documentation lists host CPU—including iowait and virtual-machine steal—memory, disk usage and read/write throughput, network bandwidth, temperature, load average, and optional Docker or Podman per-container CPU, memory, and network metrics. That page describes the product’s coverage; it is not an independent performance evaluation.
Do not apply a universal threshold
No single CPU percentage, memory level, disk-usage percentage, or latency value identifies failure for every self-hosted app. A CPU-bound video job, an idle database, and a latency-sensitive API have different normal ranges. Treat a metric as a clue and ask whether it changed at the same time as the request, exceeded the workload’s established baseline, or collided with a configured limit.
Inspect application and dependency bottlenecks
Once the host and network path look healthy, move inward. Application instrumentation can show which route, query, queue, or external call consumed the time; a host dashboard usually cannot.
Request and worker timing
- Break total latency into queue wait, application processing, database time, external calls, and response transmission when the framework exposes those spans.
- Check worker counts, event-loop lag, thread-pool starvation, connection-pool usage, and queued jobs.
- Look for errors that trigger retries. A retry storm can make a healthy dependency appear slow.
- Compare slow requests with ordinary requests using the same route and payload size.
Database and queue behavior
Inspect active connections, lock waits, queue depth, transaction duration, and slow-query facilities provided by your database or application. SurrealDB’s self-hosted monitoring documentation describes metrics and, for Enterprise editions, threshold-based slow-query logging. Availability and configuration depend on the edition, so do not assume that an Enterprise-only feature exists in a community installation.
For any database, verify that a suspected query is actually present in the slow period and that an index, lock, or connection limit explains the delay. Do not add indexes, raise limits, or terminate sessions without checking the application’s supported procedure and data-safety implications.
Streaming, compression, and caching
Open WebUI’s performance troubleshooting guide gives a product-specific example: high-frequency token streaming increases network I/O and server CPU, while response compression and static-asset caching can shift work between the application and proxy. Its profiling discussion estimates that compression middleware used roughly 3–4% of worker CPU time in production deployments. That figure is an Open WebUI example, not a general server benchmark.
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Measure the affected path before changing buffering, compression, or cache headers. Disabling compression may reduce CPU while increasing bandwidth; buffering may improve throughput for ordinary responses but damage interactive streaming; caching can reduce origin work while creating stale-content or invalidation problems. Apply the setting at the layer that owns the behavior and verify both normal and streaming requests afterward.
Choose monitoring that answers your actual question
Persistent monitoring is valuable when you need to see a problem between incidents, but select it by coverage and operating cost rather than by a dashboard screenshot. Keep monitoring independent of the application host when losing that host would otherwise erase the evidence.
| Decision axis | Questions to ask |
|---|---|
| Coverage | Does it monitor the host, containers or pods, processes, endpoints, dependencies, and certificate expiry? |
| Alerts | Can it alert on failed checks, restarts, resource limits, latency, and error rates without flooding you? |
| History | How much metric and log retention is needed to compare weekly jobs, backups, and releases? |
| Overhead | What CPU, memory, disk, and network use does the agent add to a small host? |
| Deployment | Is the agent compatible with the operating system, container runtime, and orchestration model? |
| Independence | Are alerts and stored data available if the monitored machine, disk, or network path fails? |
A general host dashboard can reveal pressure, but it usually cannot identify the request or query that consumed the time. Pair infrastructure metrics with application logs, request timing, and dependency metrics. The Kubernetes metric definitions and the application-specific guidance for NetBird and Open WebUI illustrate why both levels are needed.
Change one variable, then verify
Once you have a plausible cause, make the smallest reversible change that tests it. Write down the original value, the reason for the change, and the exact measurement you expect to move.
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- Change one setting or component. Avoid simultaneously upgrading the app, changing the proxy, and resizing the host.
- Repeat the same request under a comparable workload and cache state.
- Check error rate, latency, resource use, restarts, and dependency behavior—not only the metric you expected to improve.
- Keep the change only if the result is sustained and does not create a new failure mode; otherwise revert it.
If the issue began after a release or configuration change, compare versions and use the project’s supported rollback procedure. Back up persistent data before migrations, repairs, or storage changes. Never treat deleting a volume, rebuilding a database, or purging a queue as a routine troubleshooting step without a tested recovery path.
Use symptom-to-layer checks
| Observed symptom | First comparisons | Next place to look |
|---|---|---|
| Local endpoint is fast; public hostname times out | DNS, TLS, proxy route, upstream connection, and proxy logs | Reverse proxy or load balancer configuration |
| Every request slows during backups | Disk latency, iowait, throughput, and database lock or checkpoint activity | Storage scheduling and database behavior |
| Only streaming responses lag | First-byte time, flush intervals, buffering, compression, and network throughput | Application streaming path and proxy protocol settings |
| Container repeatedly restarts | Probe results, exit code, OOM events, and logs immediately before restart | Probe semantics, memory limits, configuration, or dependency startup |
| One route is slow while others are normal | Route-level timing, query logs, payload size, and external calls | Application code, database query, or a route-specific dependency |
| Only one region or client network fails | DNS answers, packet loss, TLS negotiation, and proxy access logs by source | Network path, DNS, firewall, or edge configuration |
These comparisons narrow the search; they do not prove causation by themselves. Continue until a timestamped metric, log entry, or controlled change explains the observed behavior.
Turn the investigation into a repeatable runbook
Document the checks that are safe for your deployment so the next incident does not begin from memory. A useful runbook contains:
- The service, container, pod, unit, and proxy names for the current deployment.
- Read-only commands for status, logs, health endpoints, and resource inspection.
- Which endpoints are liveness, readiness, and operator-only diagnostics.
- Normal latency, error-rate, resource, and restart baselines for important workloads.
- Where logs and metrics are retained if the host is offline or rebuilt.
- Rollback, backup, migration, and escalation procedures for each major component.
- The date and version for every product-specific instruction, because labels and endpoints change.
Review the runbook after upgrades and test that the documented health checks, log paths, alerts, and backups still work. Reliable troubleshooting is less about finding a magic threshold than about preserving evidence and moving methodically from the user-visible symptom to the component that actually owns it.
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