徹底理解Prometheus查詢(xún)語(yǔ)法
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<metric name>{<label name>=<label value>, ...}
^
│ . . . . . . . . . . node_cpu{cpu="cpu0",mode="idle"}
│ . . . . . . . . . . node_cpu{cpu="cpu0",mode="system"}
│ . . . . . . . . . . node_load1{}
v
<------- 時(shí)間 ---------->
指標(biāo)（metric）：metric name和描述當(dāng)前樣本特征的labelsets；
時(shí)間戳（timestamp）：一個(gè)精確到毫秒的時(shí)間戳；
值（value）：表示該時(shí)間的樣本的值。
<--------------- metric ---------------------><-timestamp -><-value->
http_request_total{status="200", method="GET"}@1434417560938 => 94355
http_request_total{status="200", method="GET"}@1434417561287 => 94334
http_request_total{status="404", method="GET"}@1434417560938 => 38473
http_request_total{status="404", method="GET"}@1434417561287 => 38544
http_request_total{status="200", method="POST"}@1434417560938 => 4748
http_request_total{status="200", method="POST"}@1434417561287 => 4785
通過(guò)使用label=value可以選擇那些標(biāo)簽滿足表達(dá)式定義的時(shí)間序列；
反之使用label!=value則可以根據(jù)標(biāo)簽匹配排除時(shí)間序列。
使用label=~regx表示選擇符合正則表達(dá)式定義的時(shí)間序列；
反之使用label=!~regx進(jìn)行反向選擇。
http_requests_total{environment=~"prodect|test|development",method!="GET"}
s - 秒
m - 分鐘
h - 小時(shí)
d - 天
w - 周
y - 年
http_request_total{} # 瞬時(shí)向量表達(dá)式，選擇當(dāng)前最新的數(shù)據(jù)
http_request_total{}[5m] # 區(qū)間向量表達(dá)式，選擇以當(dāng)前時(shí)間為基準(zhǔn)過(guò)去5分鐘內(nèi)的所有數(shù)據(jù)
http_request_total{} offset 5m
http_request_total{}[1d] offset 1d
http_requests_total{api="/bet“} + http_requests_total{api="/login“} 計(jì)算投注和登錄請(qǐng)求的和
+（加法）
-（減法）
*（乘法）
/（除法）
%（求余）
^（冪運(yùn)算）
== （相等）
!= （不相等）
> （大于）
< （小于）
>= （大于等于）
<= （小于）
http_requests_total > bool 1000
http_requests_total{} 170
http_requests_total{environment="product“} 30
http_requests_total{environment="test“} 60
http_requests_total{environment="developement“} 80
sum：求和
min：最小值
max：最大值
avg：平均值
stddev：標(biāo)準(zhǔn)差
stdvar：方差
count：元素個(gè)數(shù)
count_values：等于某值的元素個(gè)數(shù)
bottomk：最小的k個(gè)元素
topk：最大的k個(gè)元素
quantile：分位數(shù)
instant-vector abs(instant-vector)：絕對(duì)值
instant-vector sqrt(instant-vector)：平方根
instant-vector exp(instant-vector)：指數(shù)計(jì)算
instant-vector ln(instant-vector )：自然對(duì)數(shù)
instant-vector ceil(instant-vector)：向上取整
instant-vector floor(instant-vector)：向下取整
instant-vector round(instant-vector)：四舍五入取整
instant-vector delta(range-vector)：計(jì)算區(qū)間向量里最大最小的差值
instant-vector increase(range-vector)：計(jì)算區(qū)間向量里最后一個(gè)值和第一個(gè)值的差值
instant-vector rate(range-vector)：計(jì)算區(qū)間向量里的平均增長(zhǎng)率
avg_over_time(range-vector)：指定間隔內(nèi)所有點(diǎn)的平均值。
min_over_time(range-vector)：指定間隔中所有點(diǎn)的最小值。
max_over_time(range-vector)：指定間隔內(nèi)所有點(diǎn)的最大值。
sum_over_time(range-vector)：指定時(shí)間間隔內(nèi)所有值的總和。
rate(http_requests_total[5m])
topk(10, http_requests_total)
node_memory_MemFree
delta(cpu_temp_celsius{host="zeus"}[2h])
# HELP prometheus_tsdb_wal_fsync_duration_seconds Duration of WAL fsync.
# TYPE prometheus_tsdb_wal_fsync_duration_seconds summary
prometheus_tsdb_wal_fsync_duration_seconds{quantile="0.5"} 0.012352463
prometheus_tsdb_wal_fsync_duration_seconds{quantile="0.9"} 0.014458005
prometheus_tsdb_wal_fsync_duration_seconds{quantile="0.99"} 0.017316173
prometheus_tsdb_wal_fsync_duration_seconds_sum 2.888716127000002
prometheus_tsdb_wal_fsync_duration_seconds_count 216
# HELP prometheus_tsdb_compaction_chunk_range Final time range of chunks on their first compaction
# TYPE prometheus_tsdb_compaction_chunk_range histogram
prometheus_tsdb_compaction_chunk_range_bucket{le="100"} 0
prometheus_tsdb_compaction_chunk_range_bucket{le="400"} 0
prometheus_tsdb_compaction_chunk_range_bucket{le="1600"} 0
prometheus_tsdb_compaction_chunk_range_bucket{le="6400"} 0
prometheus_tsdb_compaction_chunk_range_bucket{le="25600"} 0
prometheus_tsdb_compaction_chunk_range_bucket{le="102400"} 0
prometheus_tsdb_compaction_chunk_range_bucket{le="409600"} 0
prometheus_tsdb_compaction_chunk_range_bucket{le="1.6384e+06"} 260
prometheus_tsdb_compaction_chunk_range_bucket{le="6.5536e+06"} 780
prometheus_tsdb_compaction_chunk_range_bucket{le="2.62144e+07"} 780
prometheus_tsdb_compaction_chunk_range_bucket{le="+Inf"} 780
prometheus_tsdb_compaction_chunk_range_sum 1.1540798e+09
prometheus_tsdb_compaction_chunk_range_count 780
# 查詢(xún) prometheus_tsdb_compaction_chunk_range 95 百分位
histogram_quantile(0.95, prometheus_tsdb_compaction_chunk_range_bucket)
GET /api/v1/query
query=：PromQL表達(dá)式。
time=：指定時(shí)間戳。可選參數(shù)，默認(rèn)情況下使用Prometheus當(dāng)前系統(tǒng)時(shí)間。
$ curl 'http://192.168.40.161:9090/api/v1/query?query=node_cpu_seconds_total{mode="idle"}&time=1587690566.034'
{
"status": "success",
"data": {
"resultType": "vector",
"result": [
{
"metric": {
"__name__": "node_cpu_seconds_total",
"cpu": "0",
"instance": "192.168.40.161:9100",
"job": "linux",
"mode": "idle"
},
"value": [
1587690566.034,
"24745.92"
]
},
{
"metric": {
"__name__": "node_cpu_seconds_total",
"cpu": "1",
"instance": "192.168.40.161:9100",
"job": "linux",
"mode": "idle"
},
"value": [
1587690566.034,
"24846.7"
]
}
]
}
}
{
"resultType": "matrix" | "vector",
"result": <value>
}
[
{
"metric": { "<label_name>": "<label_value>", ... },
"value": [ <unix_time>, "<sample_value>" ]
},
...
]
[
{
"metric": { "<label_name>": "<label_value>", ... },
"values": [ [ <unix_time>, "<sample_value>" ], ... ]
},
...
]
$ curl 'http://192.168.40.161:9090/api/v1/query?query=node_cpu_seconds_total{mode="idle"}[5m]&time=1587690566.034'
{
"status": "success",
"data": {
"resultType": "matrix",
"result": [
{
"metric": {
"__name__": "node_cpu_seconds_total",
"cpu": "0",
"instance": "192.168.40.161:9100",
"job": "linux",
"mode": "idle"
},
"values": [
[
1587690266.034,
"24499.47"
],
[
1587690326.034,
"24548.82"
],
[
1587690386.034,
"24598.17"
],
[
1587690446.034,
"24648.21"
],
[
1587690506.034,
"24697.37"
],
[
1587690566.034,
"24745.92"
]
]
},
{
"metric": {
"__name__": "node_cpu_seconds_total",
"cpu": "1",
"instance": "192.168.40.161:9100",
"job": "linux",
"mode": "idle"
},
"values": [
[
1587690266.034,
"24600.32"
],
[
1587690326.034,
"24649.97"
],
[
1587690386.034,
"24698.96"
],
[
1587690446.034,
"24747.99"
],
[
1587690506.034,
"24797.52"
],
[
1587690566.034,
"24846.7"
]
]
}
]
}
}
GET /api/v1/query_range
query=：PromQL表達(dá)式。
start=：起始時(shí)間。
end=：結(jié)束時(shí)間。
step=：查詢(xún)步長(zhǎng)。
{
"resultType": "matrix",
"result": <value>
}
$ curl 'http://192.168.40.161:9090/api/v1/query_range?query=node_cpu_seconds_total{mode="idle"}&start=1587693926.035&end=1587694166.034&step=2m'
{
"status": "success",
"data": {
"resultType": "matrix",
"result": [
{
"metric": {
"__name__": "node_cpu_seconds_total",
"cpu": "0",
"instance": "192.168.40.161:9100",
"job": "linux",
"mode": "idle"
},
"values": [
[
1587693926.035,
"27510.45"
],
[
1587694046.035,
"27607.39"
]
]
},
{
"metric": {
"__name__": "node_cpu_seconds_total",
"cpu": "1",
"instance": "192.168.40.161:9100",
"job": "linux",
"mode": "idle"
},
"values": [
[
1587693926.035,
"27645.27"
],
[
1587694046.035,
"27743.97"
]
]
}
]
}
}
原文鏈接：https://blog.csdn.net/zhouwenjun0820/article/details/105823389 轉(zhuǎn)自：分布式實(shí)驗(yàn)室
