tracing: Add initial opentracing support

Add initial support for opentracing by using the `jaeger` package.
Since opentracing uses the `context` package, add a `context.Context`
as the first parameter to all the functions that we might want to
trace. Trace "spans" (trace points) are then added by extracting the
trace details from the specified context parameter.

Notes:

- Although the tracer is created in `main()`, the "root span"
  (aka the first trace point) is not added until `beforeSubcommands()`.

  This is by design and is a compromise: by delaying the creation of the
  root span, the spans become much more readable since using the web-based
  JaegerUI, you will see traces like this:

  ```
  kata-runtime: kata-runtime create
  ------------  -------------------
       ^                ^
       |                |
  Trace name        First span name
                    (which clearly shows the CLI command that was run)
  ```

  Creating the span earlier means it is necessary to expand 'n' spans in
  the UI before you get to see the name of the CLI command that was run.
  In adding support, this became very tedious, hence my design decision to
  defer the creation of the root span until after signal handling has been
  setup and after CLI options have been parsed, but still very early in
  the code path.

  - At this stage, the tracing stops at the `virtcontainers` call
  boundary.

- Tracing is "always on" as there doesn't appear to be a way to toggle
  it. However, its resolves to a "nop" unless the tracer can talk to a
  jaeger agent.

Note that this commit required a bit of rework to `beforeSubcommands()`
to reduce the cyclomatic complexity.

Fixes #557.

Signed-off-by: James O. D. Hunt <james.o.hunt@intel.com>

vendor/github.com/codahale/hdrhistogram/LICENSE

@@ -0,0 +1,21 @@
+The MIT License (MIT)
+
+Copyright (c) 2014 Coda Hale
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in
+all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+THE SOFTWARE.

vendor/github.com/codahale/hdrhistogram/hdr.go

@@ -0,0 +1,564 @@
+// Package hdrhistogram provides an implementation of Gil Tene's HDR Histogram
+// data structure. The HDR Histogram allows for fast and accurate analysis of
+// the extreme ranges of data with non-normal distributions, like latency.
+package hdrhistogram
+
+import (
+	"fmt"
+	"math"
+)
+
+// A Bracket is a part of a cumulative distribution.
+type Bracket struct {
+	Quantile       float64
+	Count, ValueAt int64
+}
+
+// A Snapshot is an exported view of a Histogram, useful for serializing them.
+// A Histogram can be constructed from it by passing it to Import.
+type Snapshot struct {
+	LowestTrackableValue  int64
+	HighestTrackableValue int64
+	SignificantFigures    int64
+	Counts                []int64
+}
+
+// A Histogram is a lossy data structure used to record the distribution of
+// non-normally distributed data (like latency) with a high degree of accuracy
+// and a bounded degree of precision.
+type Histogram struct {
+	lowestTrackableValue        int64
+	highestTrackableValue       int64
+	unitMagnitude               int64
+	significantFigures          int64
+	subBucketHalfCountMagnitude int32
+	subBucketHalfCount          int32
+	subBucketMask               int64
+	subBucketCount              int32
+	bucketCount                 int32
+	countsLen                   int32
+	totalCount                  int64
+	counts                      []int64
+}
+
+// New returns a new Histogram instance capable of tracking values in the given
+// range and with the given amount of precision.
+func New(minValue, maxValue int64, sigfigs int) *Histogram {
+	if sigfigs < 1 || 5 < sigfigs {
+		panic(fmt.Errorf("sigfigs must be [1,5] (was %d)", sigfigs))
+	}
+
+	largestValueWithSingleUnitResolution := 2 * math.Pow10(sigfigs)
+	subBucketCountMagnitude := int32(math.Ceil(math.Log2(float64(largestValueWithSingleUnitResolution))))
+
+	subBucketHalfCountMagnitude := subBucketCountMagnitude
+	if subBucketHalfCountMagnitude < 1 {
+		subBucketHalfCountMagnitude = 1
+	}
+	subBucketHalfCountMagnitude--
+
+	unitMagnitude := int32(math.Floor(math.Log2(float64(minValue))))
+	if unitMagnitude < 0 {
+		unitMagnitude = 0
+	}
+
+	subBucketCount := int32(math.Pow(2, float64(subBucketHalfCountMagnitude)+1))
+
+	subBucketHalfCount := subBucketCount / 2
+	subBucketMask := int64(subBucketCount-1) << uint(unitMagnitude)
+
+	// determine exponent range needed to support the trackable value with no
+	// overflow:
+	smallestUntrackableValue := int64(subBucketCount) << uint(unitMagnitude)
+	bucketsNeeded := int32(1)
+	for smallestUntrackableValue < maxValue {
+		smallestUntrackableValue <<= 1
+		bucketsNeeded++
+	}
+
+	bucketCount := bucketsNeeded
+	countsLen := (bucketCount + 1) * (subBucketCount / 2)
+
+	return &Histogram{
+		lowestTrackableValue:        minValue,
+		highestTrackableValue:       maxValue,
+		unitMagnitude:               int64(unitMagnitude),
+		significantFigures:          int64(sigfigs),
+		subBucketHalfCountMagnitude: subBucketHalfCountMagnitude,
+		subBucketHalfCount:          subBucketHalfCount,
+		subBucketMask:               subBucketMask,
+		subBucketCount:              subBucketCount,
+		bucketCount:                 bucketCount,
+		countsLen:                   countsLen,
+		totalCount:                  0,
+		counts:                      make([]int64, countsLen),
+	}
+}
+
+// ByteSize returns an estimate of the amount of memory allocated to the
+// histogram in bytes.
+//
+// N.B.: This does not take into account the overhead for slices, which are
+// small, constant, and specific to the compiler version.
+func (h *Histogram) ByteSize() int {
+	return 6*8 + 5*4 + len(h.counts)*8
+}
+
+// Merge merges the data stored in the given histogram with the receiver,
+// returning the number of recorded values which had to be dropped.
+func (h *Histogram) Merge(from *Histogram) (dropped int64) {
+	i := from.rIterator()
+	for i.next() {
+		v := i.valueFromIdx
+		c := i.countAtIdx
+
+		if h.RecordValues(v, c) != nil {
+			dropped += c
+		}
+	}
+
+	return
+}
+
+// TotalCount returns total number of values recorded.
+func (h *Histogram) TotalCount() int64 {
+	return h.totalCount
+}
+
+// Max returns the approximate maximum recorded value.
+func (h *Histogram) Max() int64 {
+	var max int64
+	i := h.iterator()
+	for i.next() {
+		if i.countAtIdx != 0 {
+			max = i.highestEquivalentValue
+		}
+	}
+	return h.highestEquivalentValue(max)
+}
+
+// Min returns the approximate minimum recorded value.
+func (h *Histogram) Min() int64 {
+	var min int64
+	i := h.iterator()
+	for i.next() {
+		if i.countAtIdx != 0 && min == 0 {
+			min = i.highestEquivalentValue
+			break
+		}
+	}
+	return h.lowestEquivalentValue(min)
+}
+
+// Mean returns the approximate arithmetic mean of the recorded values.
+func (h *Histogram) Mean() float64 {
+	if h.totalCount == 0 {
+		return 0
+	}
+	var total int64
+	i := h.iterator()
+	for i.next() {
+		if i.countAtIdx != 0 {
+			total += i.countAtIdx * h.medianEquivalentValue(i.valueFromIdx)
+		}
+	}
+	return float64(total) / float64(h.totalCount)
+}
+
+// StdDev returns the approximate standard deviation of the recorded values.
+func (h *Histogram) StdDev() float64 {
+	if h.totalCount == 0 {
+		return 0
+	}
+
+	mean := h.Mean()
+	geometricDevTotal := 0.0
+
+	i := h.iterator()
+	for i.next() {
+		if i.countAtIdx != 0 {
+			dev := float64(h.medianEquivalentValue(i.valueFromIdx)) - mean
+			geometricDevTotal += (dev * dev) * float64(i.countAtIdx)
+		}
+	}
+
+	return math.Sqrt(geometricDevTotal / float64(h.totalCount))
+}
+
+// Reset deletes all recorded values and restores the histogram to its original
+// state.
+func (h *Histogram) Reset() {
+	h.totalCount = 0
+	for i := range h.counts {
+		h.counts[i] = 0
+	}
+}
+
+// RecordValue records the given value, returning an error if the value is out
+// of range.
+func (h *Histogram) RecordValue(v int64) error {
+	return h.RecordValues(v, 1)
+}
+
+// RecordCorrectedValue records the given value, correcting for stalls in the
+// recording process. This only works for processes which are recording values
+// at an expected interval (e.g., doing jitter analysis). Processes which are
+// recording ad-hoc values (e.g., latency for incoming requests) can't take
+// advantage of this.
+func (h *Histogram) RecordCorrectedValue(v, expectedInterval int64) error {
+	if err := h.RecordValue(v); err != nil {
+		return err
+	}
+
+	if expectedInterval <= 0 || v <= expectedInterval {
+		return nil
+	}
+
+	missingValue := v - expectedInterval
+	for missingValue >= expectedInterval {
+		if err := h.RecordValue(missingValue); err != nil {
+			return err
+		}
+		missingValue -= expectedInterval
+	}
+
+	return nil
+}
+
+// RecordValues records n occurrences of the given value, returning an error if
+// the value is out of range.
+func (h *Histogram) RecordValues(v, n int64) error {
+	idx := h.countsIndexFor(v)
+	if idx < 0 || int(h.countsLen) <= idx {
+		return fmt.Errorf("value %d is too large to be recorded", v)
+	}
+	h.counts[idx] += n
+	h.totalCount += n
+
+	return nil
+}
+
+// ValueAtQuantile returns the recorded value at the given quantile (0..100).
+func (h *Histogram) ValueAtQuantile(q float64) int64 {
+	if q > 100 {
+		q = 100
+	}
+
+	total := int64(0)
+	countAtPercentile := int64(((q / 100) * float64(h.totalCount)) + 0.5)
+
+	i := h.iterator()
+	for i.next() {
+		total += i.countAtIdx
+		if total >= countAtPercentile {
+			return h.highestEquivalentValue(i.valueFromIdx)
+		}
+	}
+
+	return 0
+}
+
+// CumulativeDistribution returns an ordered list of brackets of the
+// distribution of recorded values.
+func (h *Histogram) CumulativeDistribution() []Bracket {
+	var result []Bracket
+
+	i := h.pIterator(1)
+	for i.next() {
+		result = append(result, Bracket{
+			Quantile: i.percentile,
+			Count:    i.countToIdx,
+			ValueAt:  i.highestEquivalentValue,
+		})
+	}
+
+	return result
+}
+
+// SignificantFigures returns the significant figures used to create the
+// histogram
+func (h *Histogram) SignificantFigures() int64 {
+	return h.significantFigures
+}
+
+// LowestTrackableValue returns the lower bound on values that will be added
+// to the histogram
+func (h *Histogram) LowestTrackableValue() int64 {
+	return h.lowestTrackableValue
+}
+
+// HighestTrackableValue returns the upper bound on values that will be added
+// to the histogram
+func (h *Histogram) HighestTrackableValue() int64 {
+	return h.highestTrackableValue
+}
+
+// Histogram bar for plotting
+type Bar struct {
+	From, To, Count int64
+}
+
+// Pretty print as csv for easy plotting
+func (b Bar) String() string {
+	return fmt.Sprintf("%v, %v, %v\n", b.From, b.To, b.Count)
+}
+
+// Distribution returns an ordered list of bars of the
+// distribution of recorded values, counts can be normalized to a probability
+func (h *Histogram) Distribution() (result []Bar) {
+	i := h.iterator()
+	for i.next() {
+		result = append(result, Bar{
+			Count: i.countAtIdx,
+			From:  h.lowestEquivalentValue(i.valueFromIdx),
+			To:    i.highestEquivalentValue,
+		})
+	}
+
+	return result
+}
+
+// Equals returns true if the two Histograms are equivalent, false if not.
+func (h *Histogram) Equals(other *Histogram) bool {
+	switch {
+	case
+		h.lowestTrackableValue != other.lowestTrackableValue,
+		h.highestTrackableValue != other.highestTrackableValue,
+		h.unitMagnitude != other.unitMagnitude,
+		h.significantFigures != other.significantFigures,
+		h.subBucketHalfCountMagnitude != other.subBucketHalfCountMagnitude,
+		h.subBucketHalfCount != other.subBucketHalfCount,
+		h.subBucketMask != other.subBucketMask,
+		h.subBucketCount != other.subBucketCount,
+		h.bucketCount != other.bucketCount,
+		h.countsLen != other.countsLen,
+		h.totalCount != other.totalCount:
+		return false
+	default:
+		for i, c := range h.counts {
+			if c != other.counts[i] {
+				return false
+			}
+		}
+	}
+	return true
+}
+
+// Export returns a snapshot view of the Histogram. This can be later passed to
+// Import to construct a new Histogram with the same state.
+func (h *Histogram) Export() *Snapshot {
+	return &Snapshot{
+		LowestTrackableValue:  h.lowestTrackableValue,
+		HighestTrackableValue: h.highestTrackableValue,
+		SignificantFigures:    h.significantFigures,
+		Counts:                append([]int64(nil), h.counts...), // copy
+	}
+}
+
+// Import returns a new Histogram populated from the Snapshot data (which the
+// caller must stop accessing).
+func Import(s *Snapshot) *Histogram {
+	h := New(s.LowestTrackableValue, s.HighestTrackableValue, int(s.SignificantFigures))
+	h.counts = s.Counts
+	totalCount := int64(0)
+	for i := int32(0); i < h.countsLen; i++ {
+		countAtIndex := h.counts[i]
+		if countAtIndex > 0 {
+			totalCount += countAtIndex
+		}
+	}
+	h.totalCount = totalCount
+	return h
+}
+
+func (h *Histogram) iterator() *iterator {
+	return &iterator{
+		h:            h,
+		subBucketIdx: -1,
+	}
+}
+
+func (h *Histogram) rIterator() *rIterator {
+	return &rIterator{
+		iterator: iterator{
+			h:            h,
+			subBucketIdx: -1,
+		},
+	}
+}
+
+func (h *Histogram) pIterator(ticksPerHalfDistance int32) *pIterator {
+	return &pIterator{
+		iterator: iterator{
+			h:            h,
+			subBucketIdx: -1,
+		},
+		ticksPerHalfDistance: ticksPerHalfDistance,
+	}
+}
+
+func (h *Histogram) sizeOfEquivalentValueRange(v int64) int64 {
+	bucketIdx := h.getBucketIndex(v)
+	subBucketIdx := h.getSubBucketIdx(v, bucketIdx)
+	adjustedBucket := bucketIdx
+	if subBucketIdx >= h.subBucketCount {
+		adjustedBucket++
+	}
+	return int64(1) << uint(h.unitMagnitude+int64(adjustedBucket))
+}
+
+func (h *Histogram) valueFromIndex(bucketIdx, subBucketIdx int32) int64 {
+	return int64(subBucketIdx) << uint(int64(bucketIdx)+h.unitMagnitude)
+}
+
+func (h *Histogram) lowestEquivalentValue(v int64) int64 {
+	bucketIdx := h.getBucketIndex(v)
+	subBucketIdx := h.getSubBucketIdx(v, bucketIdx)
+	return h.valueFromIndex(bucketIdx, subBucketIdx)
+}
+
+func (h *Histogram) nextNonEquivalentValue(v int64) int64 {
+	return h.lowestEquivalentValue(v) + h.sizeOfEquivalentValueRange(v)
+}
+
+func (h *Histogram) highestEquivalentValue(v int64) int64 {
+	return h.nextNonEquivalentValue(v) - 1
+}
+
+func (h *Histogram) medianEquivalentValue(v int64) int64 {
+	return h.lowestEquivalentValue(v) + (h.sizeOfEquivalentValueRange(v) >> 1)
+}
+
+func (h *Histogram) getCountAtIndex(bucketIdx, subBucketIdx int32) int64 {
+	return h.counts[h.countsIndex(bucketIdx, subBucketIdx)]
+}
+
+func (h *Histogram) countsIndex(bucketIdx, subBucketIdx int32) int32 {
+	bucketBaseIdx := (bucketIdx + 1) << uint(h.subBucketHalfCountMagnitude)
+	offsetInBucket := subBucketIdx - h.subBucketHalfCount
+	return bucketBaseIdx + offsetInBucket
+}
+
+func (h *Histogram) getBucketIndex(v int64) int32 {
+	pow2Ceiling := bitLen(v | h.subBucketMask)
+	return int32(pow2Ceiling - int64(h.unitMagnitude) -
+		int64(h.subBucketHalfCountMagnitude+1))
+}
+
+func (h *Histogram) getSubBucketIdx(v int64, idx int32) int32 {
+	return int32(v >> uint(int64(idx)+int64(h.unitMagnitude)))
+}
+
+func (h *Histogram) countsIndexFor(v int64) int {
+	bucketIdx := h.getBucketIndex(v)
+	subBucketIdx := h.getSubBucketIdx(v, bucketIdx)
+	return int(h.countsIndex(bucketIdx, subBucketIdx))
+}
+
+type iterator struct {
+	h                                    *Histogram
+	bucketIdx, subBucketIdx              int32
+	countAtIdx, countToIdx, valueFromIdx int64
+	highestEquivalentValue               int64
+}
+
+func (i *iterator) next() bool {
+	if i.countToIdx >= i.h.totalCount {
+		return false
+	}
+
+	// increment bucket
+	i.subBucketIdx++
+	if i.subBucketIdx >= i.h.subBucketCount {
+		i.subBucketIdx = i.h.subBucketHalfCount
+		i.bucketIdx++
+	}
+
+	if i.bucketIdx >= i.h.bucketCount {
+		return false
+	}
+
+	i.countAtIdx = i.h.getCountAtIndex(i.bucketIdx, i.subBucketIdx)
+	i.countToIdx += i.countAtIdx
+	i.valueFromIdx = i.h.valueFromIndex(i.bucketIdx, i.subBucketIdx)
+	i.highestEquivalentValue = i.h.highestEquivalentValue(i.valueFromIdx)
+
+	return true
+}
+
+type rIterator struct {
+	iterator
+	countAddedThisStep int64
+}
+
+func (r *rIterator) next() bool {
+	for r.iterator.next() {
+		if r.countAtIdx != 0 {
+			r.countAddedThisStep = r.countAtIdx
+			return true
+		}
+	}
+	return false
+}
+
+type pIterator struct {
+	iterator
+	seenLastValue          bool
+	ticksPerHalfDistance   int32
+	percentileToIteratorTo float64
+	percentile             float64
+}
+
+func (p *pIterator) next() bool {
+	if !(p.countToIdx < p.h.totalCount) {
+		if p.seenLastValue {
+			return false
+		}
+
+		p.seenLastValue = true
+		p.percentile = 100
+
+		return true
+	}
+
+	if p.subBucketIdx == -1 && !p.iterator.next() {
+		return false
+	}
+
+	var done = false
+	for !done {
+		currentPercentile := (100.0 * float64(p.countToIdx)) / float64(p.h.totalCount)
+		if p.countAtIdx != 0 && p.percentileToIteratorTo <= currentPercentile {
+			p.percentile = p.percentileToIteratorTo
+			halfDistance := math.Trunc(math.Pow(2, math.Trunc(math.Log2(100.0/(100.0-p.percentileToIteratorTo)))+1))
+			percentileReportingTicks := float64(p.ticksPerHalfDistance) * halfDistance
+			p.percentileToIteratorTo += 100.0 / percentileReportingTicks
+			return true
+		}
+		done = !p.iterator.next()
+	}
+
+	return true
+}
+
+func bitLen(x int64) (n int64) {
+	for ; x >= 0x8000; x >>= 16 {
+		n += 16
+	}
+	if x >= 0x80 {
+		x >>= 8
+		n += 8
+	}
+	if x >= 0x8 {
+		x >>= 4
+		n += 4
+	}
+	if x >= 0x2 {
+		x >>= 2
+		n += 2
+	}
+	if x >= 0x1 {
+		n++
+	}
+	return
+}

vendor/github.com/codahale/hdrhistogram/window.go

@@ -0,0 +1,45 @@
+package hdrhistogram
+
+// A WindowedHistogram combines histograms to provide windowed statistics.
+type WindowedHistogram struct {
+	idx int
+	h   []Histogram
+	m   *Histogram
+
+	Current *Histogram
+}
+
+// NewWindowed creates a new WindowedHistogram with N underlying histograms with
+// the given parameters.
+func NewWindowed(n int, minValue, maxValue int64, sigfigs int) *WindowedHistogram {
+	w := WindowedHistogram{
+		idx: -1,
+		h:   make([]Histogram, n),
+		m:   New(minValue, maxValue, sigfigs),
+	}
+
+	for i := range w.h {
+		w.h[i] = *New(minValue, maxValue, sigfigs)
+	}
+	w.Rotate()
+
+	return &w
+}
+
+// Merge returns a histogram which includes the recorded values from all the
+// sections of the window.
+func (w *WindowedHistogram) Merge() *Histogram {
+	w.m.Reset()
+	for _, h := range w.h {
+		w.m.Merge(&h)
+	}
+	return w.m
+}
+
+// Rotate resets the oldest histogram and rotates it to be used as the current
+// histogram.
+func (w *WindowedHistogram) Rotate() {
+	w.idx++
+	w.Current = &w.h[w.idx%len(w.h)]
+	w.Current.Reset()
+}