The management of a Ceph distributed storage system requires a level of precision that transcends standard storage administration. As a unified platform providing object, block, and file storage, Ceph operates with immense complexity, handling data distribution across OSDs, Monitors, and Managers. Without a robust monitoring architecture, the subtle degradation of performance or the creeping onset of hardware failure can remain undetected until they manifest as catastrophic application-level outages. Achieving true operational excellence necessitates the deployment of a sophisticated observability stack, specifically leveraging Prometheus for time-series metric collection and Grafana for high-fidelity visualization. This architecture allows administrators to move from reactive troubleshooting to proactive health management, detecting anomalies in cluster health, performance, and capacity before they impact the end-user experience.

The Architecture of Ceph Observability

The effectiveness of a monitoring implementation depends entirely on the structural integrity of the data pipeline. In a well-architected Ceph environment, the monitoring stack functions as a continuous loop of collection, storage, and visualization. The foundation of this pipeline is the Ceph Manager (MGR) Prometheus module. This module acts as the primary gateway, exposing critical cluster metrics through specialized Prometheus-compatible endpoints.

The data flow follows a rigorous hierarchy:

1. Metric Generation: The Ceph MGR daemons internally track cluster-wide statistics, such as health status, pool usage, and OSD performance.
2. Metric Exposure: Once the Prometheus module is enabled, these metrics are served over HTTP, typically on port 9283.
3. Metric Collection (Scraping): The Prometheus server is configured with specific scrape targets. It periodically polls the MGR endpoints, pulling the latest snapshots of the cluster state.
4. Time-Series Storage: Prometheus ingests this data, transforming it into a highly compressed, indexed time-series database, allowing for historical trend analysis and complex mathematical queries.
5. Visualization and Alerting: Grafana queries the Prometheus data source to render dashboards. Simultaneously, Prometheus evaluates alert rules against incoming data to trigger notifications when thresholds are breached.

This architecture is designed to be scalable and resilient. By utilizing the native MGR Prometheus module, the need for external exporters like the older ceph_exporter is eliminated for core cluster statistics, reducing the computational overhead on the cluster nodes and simplifying the deployment footprint.

Foundational Requirements and Prerequisites

Before initiating the configuration of the monitoring stack, a baseline of operational readiness must be established. Failure to meet these prerequisites will result in broken data pipelines or incomplete metric visibility.

The following components must be verified and active within the infrastructure:

• Ceph Cluster Version: For modern monitoring capabilities, a Ceph cluster running Quincy or later is strongly recommended. While older versions such as Luminous (12.2) or Mimic (13.2) are compatible with specific dashboards, certain advanced statistics are only reported by Mimic instances and later.
• Ceph MGR Daemons: At least one Ceph Manager daemon must be operational and actively serving the cluster.
• Prometheus Server: A functional Prometheus instance, version 2.45 or later, must be deployed and accessible to the Ceph nodes.
• Grafana Instance: Grafana version 10.0 or later is required to support the latest time-series panels and advanced dashboard features.
• Network Connectivity: Strict firewall rules must allow traffic between the Prometheus server and the Ceph MGR nodes, specifically permitting access to port 9283.
• Node Exporter: For a complete view of the underlying hardware health (CPU, Memory, Disk I/O), Node Exporter must be running on every node within the cluster to provide host-level metrics.

To verify the current state of the cluster and ensure the MGR daemons are healthy and available for metric extraction, the following command should be executed:

bash ceph status

A successful verification will produce an output similar to the following, indicating a healthy cluster with active managers:

```text
cluster:
id: a7f64266-0894-4f1e-a635-d0aeaca0e993
health: HEALTH_OK

services:
mon: 3 daemons, quorum ceph-mon1,ceph-mon2,ceph-mon3
mgr: ceph-mgr1(active), standbys: ceph-mgr2
osd: 12 osds: 12 up, 12 in
```

Activating the Ceph MGR Prometheus Module

The Ceph Manager Prometheus module is the indispensable engine for metric exportation. Enabling this module transforms the MGR from a simple management daemon into a rich data provider.

The activation process involves two primary steps: enabling the module within the Ceph cluster and configuring the network to allow metric scraping.

First, execute the following command on the cluster to enable the module globally:

bash ceph mgr module enable prometheus

Once enabled, the MGR will begin exposing metrics at its endpoint. However, enabling the module is insufficient if the network layer blocks the Prometheus server from reaching the MGR. You must ensure that traffic is permitted through port 9283 on all machines hosting a Ceph MGR daemon.

To ensure high availability and prevent "blind spots" during MGR failovers, it is critical to configure Prometheus to scrape all MGR daemons in the cluster. If Prometheus only targets a single MGR, the metrics stream will be interrupted whenever a failover occurs, leading to gaps in your historical data.

In your prometheus.yml configuration, you should define targets for both the Ceph MGRs and the Node Exporters, using labels to organize them by cluster. An example of a robust configuration for targets is provided below:

```yaml

Configuration for Ceph MGR targets

scrapeconfigs:
- jobname: 'ceph-targets'
static_configs:
- targets: ['mycluster-mgr-1:9283', 'mycluster-mgr-2:9283', 'mycluster-mgr-3:9283']
labels:
cluster: 'mycluster'

# Configuration for Node Exporter targets
- jobname: 'node-exporter-targets'
staticconfigs:
- targets: ['mycluster-node-1:9100', 'mycluster-node-2:9100', 'mycluster-node-3:9100']
labels:
cluster: 'mycluster'
```

By utilizing the cluster label, you can create dynamic Grafana dashboards that switch between different Ceph clusters seamlessly, provided your dashboard queries are designed to filter by this label.

Configuring the Grafana Prometheus Data Source

Once Prometheus is successfully scraping the Ceph metrics, Grafana must be configured to communicate with the Prometheus instance. This is best achieved through Grafana's provisioning system, which allows for "infrastructure as code" management of data sources.

To automate the configuration of the Prometheus data source, you should create a provisioning file. This ensures that every time the Grafana instance is deployed or restarted, the connection to the Ceph metrics is automatically re-established.

Create or edit the following file: /etc/grafana/provisioning/datasources/prometheus.yml

```yaml
apiVersion: 1

datasources:
- name: Prometheus-Ceph
type: prometheus
access: proxy
url: http://prometheus:9090
isDefault: true
jsonData:
exemplarTraceIdDestinations:
- name: trace_id
datasourceUid: tempo
httpMethod: POST
manageAlerts: true
prometheusType: Prometheus
prometheusVersion: "2.45.0"
editable: false
```

The configuration above establishes Prometheus-Ceph as the default data source. Crucially, it also enables exemplar support, which allows for trace correlation. This means that if you are using a tracing tool like Tempo, you can jump directly from a metric spike in Grafana to the specific trace that caused the latency.

Dashboard Deployment and Customization

The visualization layer is where the raw numbers are converted into actionable intelligence. There are two primary approaches to dashboarding: using official community-maintained dashboards or creating custom, highly specialized views.

Importing Official Community Dashboards

The Ceph community and various experts provide pre-configured dashboards that are highly optimized for Ceph environments. These dashboards utilize the latest Grafana time-cluster panels and are compatible with Rook Ceph and standard Ceph deployments.

To import the official Ceph Cluster dashboard via the command line, you can use the following process:

1. Download the official dashboard JSON:
   bash curl -o /tmp/ceph-dashboard.json \ https://raw.githubusercontent.com/ceph/ceph/main/monitoring/ceph-mixin/dashboards_out/ceph-cluster.json

2. Import the dashboard into your Grafana instance via the API:
   ```bash

   Replace and with your actual values

curl -X POST \
-H "Authorization: Bearer " \
-H "Content-Type: application/json" \
-d @/tmp/ceph-dashboard.json \
http://:3000/api/dashboards/db
```

There are several specialized dashboards available for different focus areas:

Implementing Custom Annotations and Panels

For advanced users, creating custom dashboards allows for the integration of specific business logic, such as automated annotations for health changes. An annotation can visually mark a point in time on a graph when a specific event occurred, such as a change in the ceph_health_status.

Below is an example of a JSON fragment for a custom dashboard that includes an annotation rule to track health status changes:

json { "annotations": { "list": [ { "datasource": "Prometheus-Ceph", "enable": true, "expr": "changes(ceph_health_status[5m]) > 0", "name": "Health Status Changes", "tagKeys": "cluster", "titleFormat": "Cluster Health Changed" } ] }, "title": "Ceph Cluster Overview", "description": "Comprehensive overview of Ceph cluster health, performance, and capacity", "uid": "ceph-overview-v1", "version": 1, "panels": [ { "id": 1, "title": "Cluster Health Status", "type": "stat", "description": "Current cluster health: 0=OK, 1=WARN, 2=ERR", "gridPos": {"h": 4, "w": 6, "x": 0, "y": 0}, "targets": [ { "expr": "ceph_health_status", "legendFormat": "Health Status" } ], "fieldConfig": { "defaults": { "mappings": [ {"type": "value", "options": {"0": {"text": "HEALTHY", "color": "green"}}}, {"type": "value", "options": {"1": {"text": "WARN", "color": "orange"}}}, {"type": "value", "options": {"2": {"text": "ERR", "color": "red"}}} ] } } } ] }

In this configuration, the stat panel provides a high-visibility indicator of the cluster's health. The value mapping ensures that a numeric 0 is immediately recognizable as "HEALTHY" in green, while a 1 or 2 triggers a warning or error state in orange or red, respectively.

Strategic Analysis of Monitoring Metrics

Effective monitoring is not about collecting every available metric, but about monitoring the right metrics. An administrator must focus on the metrics that serve as leading indicators of failure.

The following table categorizes the critical metric groups that should be prioritized in any Ceph monitoring strategy:

Monitoring ceph_health_status is the most critical task. By utilizing the changes() function in Prometheus, as seen in the annotation example, administrators can be notified the moment the cluster transitions from HEALTH_OK to HEALTH_WARN. This allows for intervention during the "degraded" phase, before the cluster reaches a HEALTH_ERR state which could lead to data unavailability.

Furthermore, monitoring pool-specific metrics via the Ceph Pools dashboard is essential for multi-tenant environments. If a single pool experiences an unexpected surge in write operations, it could impact the latency of all other pools sharing the same OSDs. Granular visibility into ceph_pool_used allows for proactive rebalancing or capacity expansion.

Conclusion: The Maturity of Observability

Implementing Prometheus and Grafana for Ceph monitoring represents a transition from manual cluster management to automated, data-driven operations. The architecture described—leveraging the MGR Prometheus module, configuring robust Prometheus scrape targets, and deploying specialized Grafana dashboards—creates a defensive perimeter around the storage infrastructure.

The true value of this implementation lies in its ability to provide context. Through the use of labels (e.g., cluster: mycluster), administrators can manage hundreds of disparate Ceph clusters through a single, unified Grafana interface. Furthermore, the integration of node-level metrics via Node Exporter completes the picture, allowing an engineer to correlate a spike in Ceph OSD latency with a simultaneous spike in host-level disk I/O wait or CPU saturation.

As storage environments grow in scale and complexity, the ability to perform deep-drilling into metrics like ceph_pg_degraded or ceph_osd_op_latency becomes the difference between a routine maintenance task and an emergency recovery operation. A mature monitoring stack does not just report that a problem exists; it provides the forensic evidence required to identify the root cause, thereby ensuring the long-term stability and performance of the Ceph distributed storage ecosystem.

Sources

1. OneUptime - Ceph Prometheus Grafana Monitoring
2. Grafana - Ceph Cluster Dashboard
3. Grafana - Ceph Pools Dashboard
4. Grafana - Ceph Cluster Dashboard (Legacy)