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ceph/doc/dev/perf_histograms.rst
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Signed-off-by: Kefu Chai <kchai@redhat.com>
2017-07-07 11:50:33 +08:00

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=================
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" (logarhitmic
scale) or "linear" (linear scale).
You can use histogram_dump.py tool (see src/tools/histogram_dump.py) for quick visualisation of existing histogram
data.
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