“What is the cache hit rate?” looks like a simple question. In practice, it first requires four definitions: which cache layer, which object type, which time window, and which request set forms the denominator.
Model the request path
After an NFS request enters Ganesha, it may involve metadata, attribute and directory-entry caches, as well as the backend file system’s page cache. A local counter can easily turn “the backend was not called” into the incorrect claim that “the data cache hit.”
An interpretable metric needs an explicit denominator. An attribute-cache hit rate, for example, should be divided by queries answerable by that cache rather than by all NFS operations. Cross-layer aggregation must also avoid counting one request more than once.
Observation boundaries
It is useful to record three groups together: operations at the entry point, calls from Ganesha to FSAL, and actual backend I/O. They form a causal chain that can be checked. A single metric is useful for alerting; multiple aligned metrics are needed for diagnosis.
Short windows are sensitive to bursts, while lifetime totals hide change. A practical setup keeps monotonically increasing counters and computes rates and ratios over several windows in the collection layer.
Conclusion
More cache metrics are not automatically better. The useful ones correspond to a clear request path, support falsification, and preserve their meaning across configuration and version changes.