To monitor a Mitsuki API with Prometheus and Grafana, expose Mitsuki’s Prometheus-format endpoint, allow Prometheus to reach it, confirm the target is healthy, then provision a Mitsuki dashboard in Grafana. In the Docker Compose example below, Prometheus scrapes mitsuki:8000/metrics/prometheus; the separate /metrics endpoint returns JSON and is not the Prometheus scrape endpoint.
The configuration and dashboard details here follow the example published October 3, 2026, in David Landup’s Mitsuki observability tutorial. Its image tags use latest, so treat it as an example rather than a compatibility guarantee for every Mitsuki, Prometheus, or Grafana release.
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How the monitoring pieces fit together
Mitsuki exposes metrics, Prometheus periodically scrapes and stores them, and Grafana queries Prometheus to visualize them. Prometheus must be able to reach the application over the network, and Grafana must point to the Prometheus data source holding those metrics.
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Configure Prometheus to scrape Mitsuki
Use the Prometheus text endpoint and the Compose service name as the target when both containers share the Compose network. The tutorial’s example uses a five-second scrape interval; that is an example configuration, not a benchmark or a universal recommendation.
| Setting | Example value | Purpose |
|---|---|---|
job_name |
mitsuki |
Identifies the scrape job. |
metrics_path |
/metrics/prometheus |
Selects Mitsuki’s Prometheus text endpoint; /metrics is a JSON summary instead. |
targets |
mitsuki:8000 |
Uses the service name and port reachable from Prometheus on the Compose network. |
scrape_interval |
5s |
Sets how often the example Prometheus configuration scrapes the target. |
The corresponding Prometheus job is:
scrape_configs:
- job_name: 'mitsuki'
scrape_interval: 5s
metrics_path: '/metrics/prometheus'
static_configs:
- targets: ['mitsuki:8000']Prometheus also needs network permission to read the endpoint. Mitsuki’s metrics.allowed_ips must include the address range used by the Docker network. The tutorial’s example uses 172.16.0.0/12; verify that this matches the network in your own Compose environment rather than copying it blindly.
Confirm the target before opening Grafana
- Start the Mitsuki and Prometheus services on the same Compose network, using the configuration appropriate to the Mitsuki release you run.
- Open Prometheus and check Status → Target health. Find the
mitsukijob and confirm its target isUP. - If the target is down, inspect the scrape error. Check that the path is
/metrics/prometheus, the address ismitsuki:8000, and Mitsuki permits the scraper’s Docker IP range. - Only troubleshoot Grafana panels after the target is healthy. An unavailable target cannot supply the time series those panels query.
Generate and provision the framework dashboard
The tutorial generates the bundled Mitsuki dashboard with mitsuki grafana-dashboard -o /dashboards. Its Compose setup writes the output to a shared volume, then mounts that volume where Grafana’s dashboard provider can read it.
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- Run the dashboard-generation command in the one-shot initialization service:
mitsuki grafana-dashboard -o /dashboards. - Mount the generated dashboard directory or shared volume into Grafana and configure a dashboard provider to load it.
- Make the Grafana service wait until the initialization service has completed successfully. Otherwise, Grafana may read the directory before the dashboard file exists.
- Open the provisioned dashboard in Grafana and check that its panels use the Prometheus data source scraping Mitsuki.
The tutorial’s bundled dashboard is intended to be reusable across Mitsuki applications because its panels query framework-emitted metrics. The example provisions a separate directory for its application-specific dashboard rather than mixing business metrics into the framework dashboard.
If Grafana has multiple Prometheus data sources, set the intended one as the default or adjust the dashboard panels to select it. A dashboard can load correctly yet show no data if it queries the wrong source.
What the example dashboards show
The framework dashboard described in the tutorial covers API behavior, internal work, and resource use. These are the tutorial’s stated panel categories, not independently verified behavior for every Mitsuki release.
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- Request health: total requests, request rate, average response time, error rate, p95 and p99 latency, and status-code distribution.
- Routes: per-route totals, request rate, average latency, and p95 and p99 latency.
- Framework work: component and method call rates, durations, and failures; scheduler task rate, duration, and failures.
- Resources: CPU and memory usage.
The example’s second, application-specific dashboard tracks order creation, database writes, rows returned, full table scans, and expensive aggregations. That split makes the framework dashboard portable while keeping service-specific questions visible to the team that owns the application.
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In the example, an OrderService records custom measurements through InstrumentationProvider. Use the same principle for business events that the framework dashboard cannot answer, such as whether orders are being created or database operations are returning unexpectedly large result sets.
Best Value
Choose metric types to match the value. A counter accumulates events and is commonly analyzed with Prometheus rate(); a gauge represents a value that can go up or down, such as in-progress requests or memory. Prometheus’s Instrumentation guidance also cautions that each distinct label combination creates another time series and consumes resources. It recommends keeping most metric cardinality low: “As a rule of thumb, try to keep the cardinality of your metrics below 10, and for metrics that exceed that, aim to limit them to a handful across your whole system.” — Prometheus Authors.
Avoid labels whose values can grow without bound, such as user IDs or raw request paths. Prometheus advises reducing dimensions or using a general-purpose processing system when cardinality exceeds 100 or could grow to that scale. For an API, request volume, errors, and latency are useful starting measurements; keep labels to a small, stable set that supports the questions operators actually need to answer.
Keep Grafana access private unless it is secured
The tutorial’s Compose example enables anonymous Grafana access with the Admin role and explicitly limits that setup to local use. Do not expose that configuration on a public network: an anonymous administrator can make changes to dashboards and Grafana settings. For a remotely reachable deployment, configure authenticated access and appropriate permissions before exposing the service.
Release and configuration caveats
The tutorial uses prom/prometheus:latest and grafana/grafana:latest, without establishing which pinned releases are compatible. Its endpoint, allowed-IP setting, dashboard command, and panel descriptions should therefore be checked against the exact Mitsuki version deployed. The cited tutorial provides the example configuration; it does not establish that every listed panel or command behaves identically across versions.
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