Perf histograms
The perf histograms build on perf counters infrastructure. Histograms are built for a number of counters and simplify gathering data on which groups of counter values occur most often over time.Perf histograms are currently unsigned 64-bit integer counters, so they’re mostly useful for time and sizes. Data dumped by perf histogram can then be feed into other analysis tools/scripts.
Access
The perf histogram data are accessed via the admin socket. For example:
- ceph daemon osd.0 perf histogram schema
- ceph daemon osd.0 perf histogram dump
Collections
The histograms are grouped into named collections, normally representing a subsystem or an instance of a subsystem. For example, the internal throttle
mechanism reports statistics on how it is throttling, and each instance is named something like:
- op_r_latency_out_bytes_histogram
- op_rw_latency_in_bytes_histogram
- op_rw_latency_out_bytes_histogram
- ...
Schema
The perf histogram schema
command dumps a json description of which values are available, and what their type is. Each named value as a type
bitfield, with the 5-th bit always set and following bits defined.
bit | meaning |
---|---|
1 | floating point value |
2 | unsigned 64-bit integer value |
4 | average (sum + count pair) |
8 | counter (vs gauge) |
In other words, histogram of type “18” is a histogram of unsigned 64-bit integer values (16 + 2).
Here is an example of the schema output:
- {
- "AsyncMessenger::Worker-0": {},
- "AsyncMessenger::Worker-1": {},
- "AsyncMessenger::Worker-2": {},
- "mutex-WBThrottle::lock": {},
- "objecter": {},
- "osd": {
- "op_r_latency_out_bytes_histogram": {
- "type": 18,
- "description": "Histogram of operation latency (including queue time) + da ta read",
- "nick": ""
- },
- "op_w_latency_in_bytes_histogram": {
- "type": 18,
- "description": "Histogram of operation latency (including queue time) + da ta written",
- "nick": ""
- },
- "op_rw_latency_in_bytes_histogram": {
- "type": 18,
- "description": "Histogram of rw operation latency (including queue time) + data written",
- "nick": ""
- },
- "op_rw_latency_out_bytes_histogram": {
- "type": 18,
- "description": "Histogram of rw operation latency (including queue time) + data read",
- "nick": ""
- }
- }
- }
Dump
The actual dump is similar to the schema, except that there are actual value groups. For example:
- "osd": {
- "op_r_latency_out_bytes_histogram": {
- "axes": [
- {
- "name": "Latency (usec)",
- "min": 0,
- "quant_size": 100000,
- "buckets": 32,
- "scale_type": "log2",
- "ranges": [
- {
- "max": -1
- },
- {
- "min": 0,
- "max": 99999
- },
- {
- "min": 100000,
- "max": 199999
- },
- {
- "min": 200000,
- "max": 399999
- },
- {
- "min": 400000,
- "max": 799999
- },
- {
- "min": 800000,
- "max": 1599999
- },
- {
- "min": 1600000,
- "max": 3199999
- },
- {
- "min": 3200000,
- "max": 6399999
- },
- {
- "min": 6400000,
- "max": 12799999
- },
- {
- "min": 12800000,
- "max": 25599999
- },
- {
- "min": 25600000,
- "max": 51199999
- },
- {
- "min": 51200000,
- "max": 102399999
- },
- {
- "min": 102400000,
- "max": 204799999
- },
- {
- "min": 204800000,
- "max": 409599999
- },
- {
- "min": 409600000,
- "max": 819199999
- },
- {
- "min": 819200000,
- "max": 1638399999
- },
- {
- "min": 1638400000,
- "max": 3276799999
- },
- {
- "min": 3276800000,
- "max": 6553599999
- },
- {
- "min": 6553600000,
- "max": 13107199999
- },
- {
- "min": 13107200000,
- "max": 26214399999
- },
- {
- "min": 26214400000,
- "max": 52428799999
- },
- {
- "min": 52428800000,
- "max": 104857599999
- },
- {
- "min": 104857600000,
- "max": 209715199999
- },
- {
- "min": 209715200000,
- "max": 419430399999
- },
- {
- "min": 419430400000,
- "max": 838860799999
- },
- {
- "min": 838860800000,
- "max": 1677721599999
- },
- {
- "min": 1677721600000,
- "max": 3355443199999
- },
- {
- "min": 3355443200000,
- "max": 6710886399999
- },
- {
- "min": 6710886400000,
- "max": 13421772799999
- },
- {
- "min": 13421772800000,
- "max": 26843545599999
- },
- {
- "min": 26843545600000,
- "max": 53687091199999
- },
- },
- {
- "min": 53687091200000
- }
- ]
- },
- {
- "name": "Request size (bytes)",
- "min": 0,
- "quant_size": 512,
- "buckets": 32,
- "scale_type": "log2",
- "ranges": [
- {
- "max": -1
- },
- {
- "min": 0,
- "max": 511
- },
- {
- "min": 512,
- "max": 1023
- },
- {
- "min": 1024,
- "max": 2047
- },
- {
- "min": 2048,
- "max": 4095
- },
- {
- "min": 4096,
- "max": 8191
- },
- {
- "min": 8192,
- "max": 16383
- },
- {
- "min": 16384,
- "max": 32767
- },
- {
- "min": 32768,
- "max": 65535
- },
- {
- "min": 65536,
- "max": 131071
- },
- {
- "min": 131072,
- "max": 262143
- },
- {
- "min": 262144,
- "max": 524287
- },
- {
- "min": 524288,
- "max": 1048575
- },
- {
- "min": 1048576,
- "max": 2097151
- },
- {
- "min": 2097152,
- "max": 4194303
- },
- {
- "min": 4194304,
- "max": 8388607
- },
- {
- "min": 8388608,
- "max": 16777215
- },
- {
- "min": 16777216,
- "max": 33554431
- },
- {
- "min": 33554432,
- "max": 67108863
- },
- {
- "min": 67108864,
- "max": 134217727
- },
- {
- "min": 134217728,
- "max": 268435455
- },
- {
- "min": 268435456,
- "max": 536870911
- },
- {
- "min": 536870912,
- "max": 1073741823
- },
- {
- "min": 1073741824,
- "max": 2147483647
- },
- {
- "min": 2147483648,
- "max": 4294967295
- },
- {
- "min": 4294967296,
- "max": 8589934591
- },
- {
- "min": 8589934592,
- "max": 17179869183
- },
- {
- "min": 17179869184,
- "max": 34359738367
- },
- {
- "min": 34359738368,
- "max": 68719476735
- },
- {
- "min": 68719476736,
- "max": 137438953471
- },
- {
- "min": 137438953472,
- "max": 274877906943
- },
- {
- "min": 274877906944
- }
- ]
- }
- ],
- "values": [
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This represents the 2d histogram, consisting of 9 history entrires and 32 value groups per each history entry.“Ranges” element denote value bounds for each of value groups. “Buckets” denote amount of value groups (“buckets”),“Min” is a minimum accepted valaue, “quant_size” is quantization unit and “scale_type” is either “log2” (logarhitmicscale) or “linear” (linear scale).You can use histogram_dump.py tool (see src/tools/histogram_dump.py) for quick visualisation of existing histogramdata.