Prometheus官网中PromQL学习笔记。
PromeQL支持的数据类型
- Instant vector - 瞬时向量,相同timestamp的时序集合 (a set of time series containing a single sample for each time series, all sharing the same timestamp)
- Range vector - 范围向量,一个时间范围内不同时序的集合 (a set of time series containing a range of data points over time for each time series)
- Scalar - 标量,即浮点数
- String - 字符串
Instant vector 选择器
通过向{} 里附加一组标签来进一步过滤时间序列。例如jvm_memory_used_bytes{id=~"PS.*"}
= : 选择与提供的字符串完全相同的标签。
!= : 选择与提供的字符串不相同的标签。
=~ : 选择正则表达式与提供的字符串(或子字符串)相匹配的标签。
!~ : 选择正则表达式与提供的字符串(或子字符串)不匹配的标签。
Range Vector 选择器
时间范围通过时间范围选择器 [] 进行定义。jvm_memory_used_bytes{id=~"PS.*"} [5m]
s - seconds
m - minutes
h - hours
d - days
w - weeks
y - years
还支持offset,表示最近过去时间:
jvm_memory_used_bytes{id=~"PS.*"} offset 10m
范围向量的表达式不能直接绘图。
函数和例子
为了得到一堆采样数据,在Windows系统本地安装了wmi_exporter插件,指标通过http://localhost:9182/metrics暴露。
prometheus.yml增加配置:
scrape_configs:
- job_name: 'local-windows'
scrape_interval: 5s
metrics_path: '/metrics'
static_configs:
- targets: ['127.0.0.1:9182']
以网络发送流量做例子(wmi_net_bytes_sent_total{nic="Intel_R__Wireless_AC_9560_160MHz"})。
increase()
increase(v range-vector) 函数获取区间向量中的第一个和最后一个样本并返回其增长量, 它会在单调性发生变化时(如由于采样目标重启引起的计数器复位)自动中断。
rate()
rate(v range-vector) 函数可以直接计算区间向量 v 在时间窗口内平均增长速率,它会在单调性发生变化时(如由于采样目标重启引起的计数器复位)自动中断。该函数的返回结果不带有度量指标,只有标签列表。 当时间区间小于等于scrape频率,则rate函数无法返回。
例如我的scrape频率是5s一次,则
rate( wmi_net_bytes_sent_total{nic="Intel_R__Wireless_AC_9560_160MHz"} [5s] )
不返回数据。
rate() 函数返回值类型只能用计数器,在长期趋势分析或者告警中推荐使用这个函数。
irate()
irate(v range-vector) 函数用于计算区间向量的增长率,但是其反应出的是瞬时增长率。irate 函数是通过区间向量中最后两个两本数据来计算区间向量的增长速率,它会在单调性发生变化时(如由于采样目标重启引起的计数器复位)自动中断。
rate vs irate
irate 只能用于绘制快速变化的计数器,在长期趋势分析或者告警中更推荐使用 rate 函数。 Percona讲述Prometheus这2个函数问题的文章值得看下:Better Prometheus rate() Function with VictoriaMetrics。
源码实现
rate等函数的实现见functions.go
关于rate实现的讨论,有个好TMD长的讨论,以后再分析:
// extrapolatedRate is a utility function for rate/increase/delta.
// It calculates the rate (allowing for counter resets if isCounter is true),
// extrapolates if the first/last sample is close to the boundary, and returns
// the result as either per-second (if isRate is true) or overall.
func extrapolatedRate(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper, isCounter bool, isRate bool) Vector {
ms := args[0].(*parser.MatrixSelector)
vs := ms.VectorSelector.(*parser.VectorSelector)
var (
samples = vals[0].(Matrix)[0]
rangeStart = enh.ts - durationMilliseconds(ms.Range+vs.Offset)
rangeEnd = enh.ts - durationMilliseconds(vs.Offset)
)
// No sense in trying to compute a rate without at least two points. Drop
// this Vector element.
if len(samples.Points) < 2 {
return enh.out
}
var (
counterCorrection float64
lastValue float64
)
for _, sample := range samples.Points {
if isCounter && sample.V < lastValue {
counterCorrection += lastValue
}
lastValue = sample.V
}
resultValue := lastValue - samples.Points[0].V + counterCorrection
// Duration between first/last samples and boundary of range.
durationToStart := float64(samples.Points[0].T-rangeStart) / 1000
durationToEnd := float64(rangeEnd-samples.Points[len(samples.Points)-1].T) / 1000
// 区间长度
sampledInterval := float64(samples.Points[len(samples.Points)-1].T-samples.Points[0].T) / 1000
averageDurationBetweenSamples := sampledInterval / float64(len(samples.Points)-1)
if isCounter && resultValue > 0 && samples.Points[0].V >= 0 {
// Counters cannot be negative. If we have any slope at
// all (i.e. resultValue went up), we can extrapolate
// the zero point of the counter. If the duration to the
// zero point is shorter than the durationToStart, we
// take the zero point as the start of the series,
// thereby avoiding extrapolation to negative counter
// values.
durationToZero := sampledInterval * (samples.Points[0].V / resultValue)
if durationToZero < durationToStart {
durationToStart = durationToZero
}
}
// If the first/last samples are close to the boundaries of the range,
// extrapolate the result. This is as we expect that another sample
// will exist given the spacing between samples we've seen thus far,
// with an allowance for noise.
extrapolationThreshold := averageDurationBetweenSamples * 1.1
extrapolateToInterval := sampledInterval
if durationToStart < extrapolationThreshold {
extrapolateToInterval += durationToStart
} else {
extrapolateToInterval += averageDurationBetweenSamples / 2
}
if durationToEnd < extrapolationThreshold {
extrapolateToInterval += durationToEnd
} else {
extrapolateToInterval += averageDurationBetweenSamples / 2
}
resultValue = resultValue * (extrapolateToInterval / sampledInterval)
// increase 函数不用除以区间时间长度
if isRate {
resultValue = resultValue / ms.Range.Seconds()
}
return append(enh.out, Sample{
Point: Point{V: resultValue},
})
}
// === rate(node parser.ValueTypeMatrix) Vector ===
func funcRate(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
return extrapolatedRate(vals, args, enh, true, true)
}
// === increase(node parser.ValueTypeMatrix) Vector ===
func funcIncrease(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
return extrapolatedRate(vals, args, enh, true, false)
}
// === irate(node parser.ValueTypeMatrix) Vector ===
func funcIrate(vals []parser.Value, args parser.Expressions, enh *EvalNodeHelper) Vector {
return instantValue(vals, enh.out, true)
}
func instantValue(vals []parser.Value, out Vector, isRate bool) Vector {
samples := vals[0].(Matrix)[0]
// No sense in trying to compute a rate without at least two points. Drop
// this Vector element.
if len(samples.Points) < 2 {
return out
}
// 使用最近2个采样计算
lastSample := samples.Points[len(samples.Points)-1]
previousSample := samples.Points[len(samples.Points)-2]
var resultValue float64
if isRate && lastSample.V < previousSample.V {
// Counter reset.
resultValue = lastSample.V
} else {
resultValue = lastSample.V - previousSample.V
}
// 使用最近2个采样计算
sampledInterval := lastSample.T - previousSample.T
if sampledInterval == 0 {
// Avoid dividing by 0.
return out
}
if isRate {
// Convert to per-second.
resultValue /= float64(sampledInterval) / 1000
}
return append(out, Sample{
Point: Point{V: resultValue},
})
}
histogram
过去5分钟的平均响应时间
rate(http_request_duration_seconds_sum[5m])
/
rate(http_request_duration_seconds_count[5m])
apdex分数,假设T=300ms
(
sum(rate(http_request_duration_seconds_bucket{le="0.3"}[5m])) by (job)
+
sum(rate(http_request_duration_seconds_bucket{le="1.2"}[5m])) by (job)
) / 2 / sum(rate(http_request_duration_seconds_count[5m])) by (job)
histogram可以聚合计算。
histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket[5m])) by (le))