7dc100714d
This PR adds counter metrics for the CPU system and the Geth process. Currently the only metrics available for these items are gauges. Gauges are fine when the consumer scrapes metrics data at the same interval as Geth produces new values (every 3 seconds), but it is likely that most consumers will not scrape that often. Intervals of 10, 15, or maybe even 30 seconds are probably more common. So the problem is, how does the consumer estimate what the CPU was doing in between scrapes. With a counter, it's easy ... you just subtract two successive values and divide by the time to get a nice, accurate average. But with a gauge, you can't do that. A gauge reading is an instantaneous picture of what was happening at that moment, but it gives you no idea about what was going on between scrapes. Taking an average of values is meaningless.
84 lines
3.0 KiB
Go
84 lines
3.0 KiB
Go
package metrics
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import (
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"time"
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)
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type Logger interface {
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Printf(format string, v ...interface{})
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}
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func Log(r Registry, freq time.Duration, l Logger) {
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LogScaled(r, freq, time.Nanosecond, l)
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}
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// Output each metric in the given registry periodically using the given
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// logger. Print timings in `scale` units (eg time.Millisecond) rather than nanos.
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func LogScaled(r Registry, freq time.Duration, scale time.Duration, l Logger) {
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du := float64(scale)
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duSuffix := scale.String()[1:]
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for range time.Tick(freq) {
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r.Each(func(name string, i interface{}) {
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switch metric := i.(type) {
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case Counter:
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l.Printf("counter %s\n", name)
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l.Printf(" count: %9d\n", metric.Count())
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case CounterFloat64:
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l.Printf("counter %s\n", name)
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l.Printf(" count: %f\n", metric.Count())
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case Gauge:
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l.Printf("gauge %s\n", name)
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l.Printf(" value: %9d\n", metric.Value())
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case GaugeFloat64:
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l.Printf("gauge %s\n", name)
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l.Printf(" value: %f\n", metric.Value())
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case Healthcheck:
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metric.Check()
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l.Printf("healthcheck %s\n", name)
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l.Printf(" error: %v\n", metric.Error())
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case Histogram:
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h := metric.Snapshot()
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ps := h.Percentiles([]float64{0.5, 0.75, 0.95, 0.99, 0.999})
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l.Printf("histogram %s\n", name)
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l.Printf(" count: %9d\n", h.Count())
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l.Printf(" min: %9d\n", h.Min())
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l.Printf(" max: %9d\n", h.Max())
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l.Printf(" mean: %12.2f\n", h.Mean())
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l.Printf(" stddev: %12.2f\n", h.StdDev())
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l.Printf(" median: %12.2f\n", ps[0])
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l.Printf(" 75%%: %12.2f\n", ps[1])
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l.Printf(" 95%%: %12.2f\n", ps[2])
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l.Printf(" 99%%: %12.2f\n", ps[3])
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l.Printf(" 99.9%%: %12.2f\n", ps[4])
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case Meter:
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m := metric.Snapshot()
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l.Printf("meter %s\n", name)
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l.Printf(" count: %9d\n", m.Count())
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l.Printf(" 1-min rate: %12.2f\n", m.Rate1())
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l.Printf(" 5-min rate: %12.2f\n", m.Rate5())
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l.Printf(" 15-min rate: %12.2f\n", m.Rate15())
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l.Printf(" mean rate: %12.2f\n", m.RateMean())
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case Timer:
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t := metric.Snapshot()
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ps := t.Percentiles([]float64{0.5, 0.75, 0.95, 0.99, 0.999})
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l.Printf("timer %s\n", name)
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l.Printf(" count: %9d\n", t.Count())
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l.Printf(" min: %12.2f%s\n", float64(t.Min())/du, duSuffix)
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l.Printf(" max: %12.2f%s\n", float64(t.Max())/du, duSuffix)
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l.Printf(" mean: %12.2f%s\n", t.Mean()/du, duSuffix)
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l.Printf(" stddev: %12.2f%s\n", t.StdDev()/du, duSuffix)
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l.Printf(" median: %12.2f%s\n", ps[0]/du, duSuffix)
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l.Printf(" 75%%: %12.2f%s\n", ps[1]/du, duSuffix)
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l.Printf(" 95%%: %12.2f%s\n", ps[2]/du, duSuffix)
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l.Printf(" 99%%: %12.2f%s\n", ps[3]/du, duSuffix)
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l.Printf(" 99.9%%: %12.2f%s\n", ps[4]/du, duSuffix)
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l.Printf(" 1-min rate: %12.2f\n", t.Rate1())
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l.Printf(" 5-min rate: %12.2f\n", t.Rate5())
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l.Printf(" 15-min rate: %12.2f\n", t.Rate15())
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l.Printf(" mean rate: %12.2f\n", t.RateMean())
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}
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})
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}
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}
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