app: use gcp storage instead of azure for builds (#81)

* vendor: add dependencies for using GCP storage
* app: use gcp storage instead of azure for builds

1 files changed, 235 insertions, 0 deletions

diff --git a/vendor/go.opencensus.io/stats/view/aggregation_data.go b/vendor/go.opencensus.io/stats/view/aggregation_data.go
new file mode 100644
index 000000000..960b94601
--- /dev/null
+++ b/vendor/go.opencensus.io/stats/view/aggregation_data.go
@@ -0,0 +1,235 @@
+// Copyright 2017, OpenCensus Authors
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+
+package view
+
+import (
+	"math"
+
+	"go.opencensus.io/exemplar"
+)
+
+// AggregationData represents an aggregated value from a collection.
+// They are reported on the view data during exporting.
+// Mosts users won't directly access aggregration data.
+type AggregationData interface {
+	isAggregationData() bool
+	addSample(e *exemplar.Exemplar)
+	clone() AggregationData
+	equal(other AggregationData) bool
+}
+
+const epsilon = 1e-9
+
+// CountData is the aggregated data for the Count aggregation.
+// A count aggregation processes data and counts the recordings.
+//
+// Most users won't directly access count data.
+type CountData struct {
+	Value int64
+}
+
+func (a *CountData) isAggregationData() bool { return true }
+
+func (a *CountData) addSample(_ *exemplar.Exemplar) {
+	a.Value = a.Value + 1
+}
+
+func (a *CountData) clone() AggregationData {
+	return &CountData{Value: a.Value}
+}
+
+func (a *CountData) equal(other AggregationData) bool {
+	a2, ok := other.(*CountData)
+	if !ok {
+		return false
+	}
+
+	return a.Value == a2.Value
+}
+
+// SumData is the aggregated data for the Sum aggregation.
+// A sum aggregation processes data and sums up the recordings.
+//
+// Most users won't directly access sum data.
+type SumData struct {
+	Value float64
+}
+
+func (a *SumData) isAggregationData() bool { return true }
+
+func (a *SumData) addSample(e *exemplar.Exemplar) {
+	a.Value += e.Value
+}
+
+func (a *SumData) clone() AggregationData {
+	return &SumData{Value: a.Value}
+}
+
+func (a *SumData) equal(other AggregationData) bool {
+	a2, ok := other.(*SumData)
+	if !ok {
+		return false
+	}
+	return math.Pow(a.Value-a2.Value, 2) < epsilon
+}
+
+// DistributionData is the aggregated data for the
+// Distribution aggregation.
+//
+// Most users won't directly access distribution data.
+//
+// For a distribution with N bounds, the associated DistributionData will have
+// N+1 buckets.
+type DistributionData struct {
+	Count           int64   // number of data points aggregated
+	Min             float64 // minimum value in the distribution
+	Max             float64 // max value in the distribution
+	Mean            float64 // mean of the distribution
+	SumOfSquaredDev float64 // sum of the squared deviation from the mean
+	CountPerBucket  []int64 // number of occurrences per bucket
+	// ExemplarsPerBucket is slice the same length as CountPerBucket containing
+	// an exemplar for the associated bucket, or nil.
+	ExemplarsPerBucket []*exemplar.Exemplar
+	bounds             []float64 // histogram distribution of the values
+}
+
+func newDistributionData(bounds []float64) *DistributionData {
+	bucketCount := len(bounds) + 1
+	return &DistributionData{
+		CountPerBucket:     make([]int64, bucketCount),
+		ExemplarsPerBucket: make([]*exemplar.Exemplar, bucketCount),
+		bounds:             bounds,
+		Min:                math.MaxFloat64,
+		Max:                math.SmallestNonzeroFloat64,
+	}
+}
+
+// Sum returns the sum of all samples collected.
+func (a *DistributionData) Sum() float64 { return a.Mean * float64(a.Count) }
+
+func (a *DistributionData) variance() float64 {
+	if a.Count <= 1 {
+		return 0
+	}
+	return a.SumOfSquaredDev / float64(a.Count-1)
+}
+
+func (a *DistributionData) isAggregationData() bool { return true }
+
+func (a *DistributionData) addSample(e *exemplar.Exemplar) {
+	f := e.Value
+	if f < a.Min {
+		a.Min = f
+	}
+	if f > a.Max {
+		a.Max = f
+	}
+	a.Count++
+	a.addToBucket(e)
+
+	if a.Count == 1 {
+		a.Mean = f
+		return
+	}
+
+	oldMean := a.Mean
+	a.Mean = a.Mean + (f-a.Mean)/float64(a.Count)
+	a.SumOfSquaredDev = a.SumOfSquaredDev + (f-oldMean)*(f-a.Mean)
+}
+
+func (a *DistributionData) addToBucket(e *exemplar.Exemplar) {
+	var count *int64
+	var ex **exemplar.Exemplar
+	for i, b := range a.bounds {
+		if e.Value < b {
+			count = &a.CountPerBucket[i]
+			ex = &a.ExemplarsPerBucket[i]
+			break
+		}
+	}
+	if count == nil {
+		count = &a.CountPerBucket[len(a.bounds)]
+		ex = &a.ExemplarsPerBucket[len(a.bounds)]
+	}
+	*count++
+	*ex = maybeRetainExemplar(*ex, e)
+}
+
+func maybeRetainExemplar(old, cur *exemplar.Exemplar) *exemplar.Exemplar {
+	if old == nil {
+		return cur
+	}
+
+	// Heuristic to pick the "better" exemplar: first keep the one with a
+	// sampled trace attachment, if neither have a trace attachment, pick the
+	// one with more attachments.
+	_, haveTraceID := cur.Attachments[exemplar.KeyTraceID]
+	if haveTraceID || len(cur.Attachments) >= len(old.Attachments) {
+		return cur
+	}
+	return old
+}
+
+func (a *DistributionData) clone() AggregationData {
+	c := *a
+	c.CountPerBucket = append([]int64(nil), a.CountPerBucket...)
+	c.ExemplarsPerBucket = append([]*exemplar.Exemplar(nil), a.ExemplarsPerBucket...)
+	return &c
+}
+
+func (a *DistributionData) equal(other AggregationData) bool {
+	a2, ok := other.(*DistributionData)
+	if !ok {
+		return false
+	}
+	if a2 == nil {
+		return false
+	}
+	if len(a.CountPerBucket) != len(a2.CountPerBucket) {
+		return false
+	}
+	for i := range a.CountPerBucket {
+		if a.CountPerBucket[i] != a2.CountPerBucket[i] {
+			return false
+		}
+	}
+	return a.Count == a2.Count && a.Min == a2.Min && a.Max == a2.Max && math.Pow(a.Mean-a2.Mean, 2) < epsilon && math.Pow(a.variance()-a2.variance(), 2) < epsilon
+}
+
+// LastValueData returns the last value recorded for LastValue aggregation.
+type LastValueData struct {
+	Value float64
+}
+
+func (l *LastValueData) isAggregationData() bool {
+	return true
+}
+
+func (l *LastValueData) addSample(e *exemplar.Exemplar) {
+	l.Value = e.Value
+}
+
+func (l *LastValueData) clone() AggregationData {
+	return &LastValueData{l.Value}
+}
+
+func (l *LastValueData) equal(other AggregationData) bool {
+	a2, ok := other.(*LastValueData)
+	if !ok {
+		return false
+	}
+	return l.Value == a2.Value
+}