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Monitor a storage environment by pairing read and write IOPS, throughput, and latency with clearly defined capacity measures, then drilling from cluster totals down to pools, workloads, hosts, and devices. Add health, recovery, and failure-domain headroom to the same operational view: a healthy cluster average or a single “used” number can conceal a slow device, uneven placement, or insufficient room to recover after a host failure.
Decide what the monitoring must protect
Start with the storage service’s objectives, not a dashboard’s default panels. Identify which client operations matter, which pools, volumes, or tenants need separate visibility, and what level of degradation should trigger an operator response. Map the layers involved: clients or workloads, pools or volumes, storage services, hosts, devices, network, and monitoring pipeline.
Define local service objectives and alert thresholds from application requirements and representative workload baselines. Ceph documentation provides metric examples and dashboard views, but it does not prescribe universal service-level objectives or alert thresholds. Keep the environment’s version, topology, and failure policy in scope when setting them.
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Collect read and write operation rates, bytes per second, and latency at the client or pool level. These measures answer different questions: IOPS show operation rate, throughput shows data movement, and latency shows request delay. Read and write behavior can differ, so keep them separate where the platform allows.
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Ceph’s monitoring documentation gives PromQL examples using the Ceph-specific metrics ceph_osd_op_r, ceph_osd_op_w, ceph_osd_op_r_out_bytes, ceph_osd_op_w_in_bytes, and latency counters. It also demonstrates per-OSD queries. These names and their labels are product- and release-specific; verify their definitions against the Ceph release actually deployed before building queries or alerts.
Read the measures together over time. Higher throughput may simply reflect growing workload demand; latency rising while operation rate stays stable can instead indicate saturation or contention. Where available, inspect distributions or percentiles as well as averages. The cited Ceph sources do not establish universal acceptable latency percentiles or thresholds; derive those from application objectives and validated baselines.
Add workload-specific views where they exist
For Ceph Object Gateway, operation counts, bytes, and latency—including PUT and GET operation metrics—can provide an object-workload view beyond generic cluster averages. The Object Gateway metrics documentation describes sending these metrics to Prometheus for cluster-wide usage views.
For CephFS, treat subvolume IOPS, throughput, and latency as data-I/O signals rather than a full account of filesystem activity. The CephFS metrics documentation says metadata-only actions, such as directory and attribute operations, do not update the cited I/O metrics.
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Track capacity with explicit accounting
Display raw capacity, capacity consumed, and client data stored as distinct measures. A client payload is not equivalent to the physical capacity used to store and protect it: metadata and redundancy affect raw consumption.
In Ceph, ceph_osd_stat_bytes reports OSD capacity; ceph_pool_bytes_used represents raw capacity consumed, including metadata and redundancy; and ceph_pool_stored represents client data before data protection. These are Ceph-specific metric meanings documented in the monitoring overview. Label panels and exports so operators do not mistake one accounting layer for another.
Show current headroom alongside configured warning and danger states. The Ceph Dashboard surfaces used capacity and warning or danger states associated with nearfull and full thresholds; its dashboard documentation describes those views. Make the threshold state legible in text or numbers as well as color.
Forecast from history and stated assumptions
Capacity forecasting requires a consumption history and a planning horizon. State the method and assumptions locally, including expected growth, redundancy, metadata, uneven placement, planned maintenance, and degraded recovery scenarios. The cited sources do not establish a cross-platform forecast formula or a universal reserve percentage, so do not present either as a standard.
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Drill down from cluster to device
Cluster aggregates help identify broad changes, but they cannot tell you whether a problem comes from one OSD, a physical drive, or a service-wide bottleneck. Preserve per-OSD visibility and pair storage counters with host and device observations. Ceph’s monitoring documentation demonstrates label-based per-OSD queries and explains that node-exporter metrics can be combined with Ceph metrics to derive physical-media performance information.
Keep inventory and topology context available beside the time series: node, device, pool, service, and failure-domain labels help locate a symptom and relate it to placement or maintenance. Ceph Dashboard provides an inventory view, while Ceph metrics include per-daemon labels; the exact topology dimensions to retain depend on the environment.
Include failure and recovery in capacity planning
Monitor cluster health, services and daemons up or down, recovery throughput, and capacity threshold state. Steady-state free space alone does not show whether the cluster can absorb a failure and recover safely.
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The Ceph Dashboard’s utilization view includes recovery throughput, alongside capacity-related views. Use recovery behavior as an operational signal: a cluster that is using its available capacity or recovering unusually slowly may have less practical headroom than its healthy-state aggregate suggests.
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Ceph documents a monitoring stack in which ceph_exporter collects daemon performance counters and the manager Prometheus module exposes cluster-level metrics. Prometheus, Alertmanager, Grafana, and scripting are documented options for exploration and customized monitoring; the Ceph Dashboard surfaces selected health, capacity, and utilization views. See the Ceph monitoring overview and dashboard documentation.
Make alerts actionable by including the affected workload, pool, service, node, or device and the relevant recent trend. Potential alert conditions include sustained latency degradation, unexpected IOPS or throughput changes, low capacity headroom, nearfull or full state, unavailable components, and unusual recovery behavior. Set thresholds and alert windows using workload baselines and failure policy; the sources do not provide universal values to copy.
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Choose visibility with scale in mind
Before exporting every available metric and label, decide which dimensions operators need for diagnosis. Ceph Object Gateway documentation cautions that exporting all metrics may be impractical in large systems and describes labeled counters stored in caches. High-cardinality labels can increase monitoring cost and memory pressure.
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When evaluating a monitoring approach, compare its coverage of cluster, pool, tenant, service, host, physical-device, and network layers; scrape and aggregation resolution; retained history; metric semantics; scale and cardinality; alerting and incident-workflow integration; failure-domain visibility; and compatibility with the deployed storage version. These criteria are more useful than choosing a system based on a single dashboard screenshot or headline metric.
Interpret metric windows and release differences
CephFS subvolume IOPS, throughput, and latency are calculated over a sliding window that defaults to 30 seconds and can be configured with subv_metrics_window_interval. That is a CephFS implementation default, not a general monitoring standard.
Metric names, labels, and behavior can vary by Ceph daemon and release. The cited latest Ceph documentation identifies itself as development documentation, so confirm exact semantics against the documentation for the installed release before using examples in production queries or alerts.
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