hamlab/internal/metrics/fold.go

package metrics

// One kept history point is the average of the samples behind it. At the default ten-minute
// window that bucket is a single sample and nothing is averaged; at a day it is five minutes
// of them, and the difference matters — a graph of the last day drawn from whichever instant
// landed on each five-minute mark would show spikes that never happened and miss the ones
// that did.
//
// The newest sample of the bucket is the template: its shape, labels, totals and slow
// sections (filesystems, UPS) are the current ones. Only the moving numbers are averaged,
// and only where an entry is present in that sample — a disk plugged in halfway through the
// bucket is averaged over the samples that have it.

func fold(b []Sample) Sample {
	out := b[len(b)-1]
	if len(b) == 1 {
		return out
	}
	n := float64(len(b))
	sum := func(get func(Sample) float64) float64 {
		var t float64
		for _, s := range b {
			t += get(s)
		}
		return t / n
	}
	sumU := func(get func(Sample) uint64) uint64 {
		var t uint64
		for _, s := range b {
			t += get(s)
		}
		return t / uint64(len(b))
	}
	out.CPU = sum(func(s Sample) float64 { return s.CPU })
	out.Load[0] = sum(func(s Sample) float64 { return s.Load[0] })
	out.Load[1] = sum(func(s Sample) float64 { return s.Load[1] })
	out.Load[2] = sum(func(s Sample) float64 { return s.Load[2] })

	out.Cores = append([]float64(nil), out.Cores...)
	for i := range out.Cores {
		var t, c float64
		for _, s := range b {
			if i < len(s.Cores) {
				t, c = t+s.Cores[i], c+1
			}
		}
		if c > 0 {
			out.Cores[i] = t / c
		}
	}

	out.Mem.Used = sumU(func(s Sample) uint64 { return s.Mem.Used })
	out.Mem.Avail = sumU(func(s Sample) uint64 { return s.Mem.Avail })
	out.Mem.Free = sumU(func(s Sample) uint64 { return s.Mem.Free })
	out.Mem.Cached = sumU(func(s Sample) uint64 { return s.Mem.Cached })
	out.Swap.Used = sumU(func(s Sample) uint64 { return s.Swap.Used })

	// the rest are lists whose entries are matched by name across the bucket
	temps := avgBy(b, func(s Sample) map[string][]float64 {
		m := make(map[string][]float64, len(s.Temps))
		for _, x := range s.Temps {
			m[x.Chip+"/"+x.Label] = []float64{x.C}
		}
		return m
	})
	out.Temps = append([]Temp(nil), out.Temps...)
	for i, x := range out.Temps {
		if v, ok := temps[x.Chip+"/"+x.Label]; ok {
			out.Temps[i].C = v[0]
		}
	}

	fans := avgBy(b, func(s Sample) map[string][]float64 {
		m := make(map[string][]float64, len(s.Fans))
		for _, x := range s.Fans {
			m[x.Chip+"/"+x.Label] = []float64{float64(x.RPM)}
		}
		return m
	})
	out.Fans = append([]Fan(nil), out.Fans...)
	for i, x := range out.Fans {
		if v, ok := fans[x.Chip+"/"+x.Label]; ok {
			out.Fans[i].RPM = int(v[0] + .5)
		}
	}

	nets := avgBy(b, func(s Sample) map[string][]float64 {
		m := make(map[string][]float64, len(s.Net))
		for _, x := range s.Net {
			m[x.Iface] = []float64{x.RxBps, x.TxBps}
		}
		return m
	})
	out.Net = append([]Net(nil), out.Net...)
	for i, x := range out.Net {
		if v, ok := nets[x.Iface]; ok {
			out.Net[i].RxBps, out.Net[i].TxBps = v[0], v[1]
		}
	}

	disks := avgBy(b, func(s Sample) map[string][]float64 {
		m := make(map[string][]float64, len(s.Disks))
		for _, x := range s.Disks {
			m[x.Name] = []float64{x.ReadBps, x.WriteBps, x.UtilPct}
		}
		return m
	})
	out.Disks = append([]Disk(nil), out.Disks...)
	for i, x := range out.Disks {
		if v, ok := disks[x.Name]; ok {
			out.Disks[i].ReadBps, out.Disks[i].WriteBps, out.Disks[i].UtilPct = v[0], v[1], v[2]
		}
	}
	return out
}

// avgBy averages each sample's named numbers across the bucket, per name.
func avgBy(b []Sample, of func(Sample) map[string][]float64) map[string][]float64 {
	sums := map[string][]float64{}
	count := map[string]float64{}
	for _, s := range b {
		for k, vs := range of(s) {
			acc := sums[k]
			if acc == nil {
				acc = make([]float64, len(vs))
			}
			for i, v := range vs {
				if i < len(acc) {
					acc[i] += v
				}
			}
			sums[k], count[k] = acc, count[k]+1
		}
	}
	for k, acc := range sums {
		for i := range acc {
			acc[i] /= count[k]
		}
	}
	return sums
}