Add high resolution metrics support to CloudWatch output (#6689)
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@ -45,4 +45,7 @@ also save AWS API cost. If enable this flag, this plugin would parse the require
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[CloudWatch statistic fields](https://docs.aws.amazon.com/sdk-for-go/api/service/cloudwatch/#StatisticSet)
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(count, min, max, and sum) and send them to CloudWatch. You could use `basicstats`
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aggregator to calculate those fields. If not all statistic fields are available,
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all fields would still be sent as raw metrics.
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all fields would still be sent as raw metrics.
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## high_resolution_metrics
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Enable high resolution metrics (1 second precision) instead of standard ones (60 seconds precision)
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@ -25,8 +25,9 @@ type CloudWatch struct {
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Token string `toml:"token"`
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EndpointURL string `toml:"endpoint_url"`
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Namespace string `toml:"namespace"` // CloudWatch Metrics Namespace
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svc *cloudwatch.CloudWatch
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Namespace string `toml:"namespace"` // CloudWatch Metrics Namespace
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HighResolutionMetrics bool `toml:"high_resolution_metrics"`
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svc *cloudwatch.CloudWatch
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WriteStatistics bool `toml:"write_statistics"`
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}
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@ -47,11 +48,12 @@ type cloudwatchField interface {
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}
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type statisticField struct {
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metricName string
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fieldName string
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tags map[string]string
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values map[statisticType]float64
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timestamp time.Time
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metricName string
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fieldName string
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tags map[string]string
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values map[statisticType]float64
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timestamp time.Time
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storageResolution int64
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}
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func (f *statisticField) addValue(sType statisticType, value float64) {
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@ -81,6 +83,7 @@ func (f *statisticField) buildDatum() []*cloudwatch.MetricDatum {
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Sum: aws.Float64(sum),
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SampleCount: aws.Float64(count),
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},
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StorageResolution: aws.Int64(f.storageResolution),
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}
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datums = append(datums, datum)
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@ -126,11 +129,12 @@ func (f *statisticField) hasAllFields() bool {
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}
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type valueField struct {
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metricName string
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fieldName string
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tags map[string]string
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value float64
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timestamp time.Time
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metricName string
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fieldName string
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tags map[string]string
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value float64
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timestamp time.Time
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storageResolution int64
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}
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func (f *valueField) addValue(sType statisticType, value float64) {
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@ -143,10 +147,11 @@ func (f *valueField) buildDatum() []*cloudwatch.MetricDatum {
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return []*cloudwatch.MetricDatum{
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{
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MetricName: aws.String(strings.Join([]string{f.metricName, f.fieldName}, "_")),
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Value: aws.Float64(f.value),
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Dimensions: BuildDimensions(f.tags),
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Timestamp: aws.Time(f.timestamp),
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MetricName: aws.String(strings.Join([]string{f.metricName, f.fieldName}, "_")),
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Value: aws.Float64(f.value),
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Dimensions: BuildDimensions(f.tags),
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Timestamp: aws.Time(f.timestamp),
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StorageResolution: aws.Int64(f.storageResolution),
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},
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}
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}
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@ -186,6 +191,9 @@ var sampleConfig = `
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## You could use basicstats aggregator to calculate those fields. If not all statistic
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## fields are available, all fields would still be sent as raw metrics.
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# write_statistics = false
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## Enable high resolution metrics of 1 second (standard resolution metrics are 60 seconds)
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## high_resolution_metrics = false
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`
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func (c *CloudWatch) SampleConfig() string {
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@ -220,7 +228,7 @@ func (c *CloudWatch) Write(metrics []telegraf.Metric) error {
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var datums []*cloudwatch.MetricDatum
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for _, m := range metrics {
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d := BuildMetricDatum(c.WriteStatistics, m)
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d := BuildMetricDatum(c.WriteStatistics, c.HighResolutionMetrics, m)
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datums = append(datums, d...)
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}
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@ -278,10 +286,14 @@ func PartitionDatums(size int, datums []*cloudwatch.MetricDatum) [][]*cloudwatch
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// Make a MetricDatum from telegraf.Metric. It would check if all required fields of
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// cloudwatch.StatisticSet are available. If so, it would build MetricDatum from statistic values.
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// Otherwise, fields would still been built independently.
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func BuildMetricDatum(buildStatistic bool, point telegraf.Metric) []*cloudwatch.MetricDatum {
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func BuildMetricDatum(buildStatistic bool, highResolutionMetrics bool, point telegraf.Metric) []*cloudwatch.MetricDatum {
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fields := make(map[string]cloudwatchField)
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tags := point.Tags()
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storageResolution := int64(60)
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if highResolutionMetrics {
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storageResolution = 1
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}
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for k, v := range point.Fields() {
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@ -297,11 +309,12 @@ func BuildMetricDatum(buildStatistic bool, point telegraf.Metric) []*cloudwatch.
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// If statistic metric is not enabled or non-statistic type, just take current field as a value field.
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if !buildStatistic || sType == statisticTypeNone {
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fields[k] = &valueField{
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metricName: point.Name(),
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fieldName: k,
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tags: tags,
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timestamp: point.Time(),
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value: val,
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metricName: point.Name(),
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fieldName: k,
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tags: tags,
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timestamp: point.Time(),
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value: val,
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storageResolution: storageResolution,
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}
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continue
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}
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@ -317,6 +330,7 @@ func BuildMetricDatum(buildStatistic bool, point telegraf.Metric) []*cloudwatch.
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values: map[statisticType]float64{
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sType: val,
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},
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storageResolution: storageResolution,
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}
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} else {
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// Add new statistic value to this field
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@ -75,11 +75,11 @@ func TestBuildMetricDatums(t *testing.T) {
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testutil.TestMetric(float64(1.174272e+108)), // largest should be 1.174271e+108
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}
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for _, point := range validMetrics {
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datums := BuildMetricDatum(false, point)
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datums := BuildMetricDatum(false, false, point)
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assert.Equal(1, len(datums), fmt.Sprintf("Valid point should create a Datum {value: %v}", point))
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}
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for _, point := range invalidMetrics {
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datums := BuildMetricDatum(false, point)
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datums := BuildMetricDatum(false, false, point)
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assert.Equal(0, len(datums), fmt.Sprintf("Valid point should not create a Datum {value: %v}", point))
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}
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@ -89,7 +89,7 @@ func TestBuildMetricDatums(t *testing.T) {
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map[string]interface{}{"value_max": float64(10), "value_min": float64(0), "value_sum": float64(100), "value_count": float64(20)},
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time.Date(2009, time.November, 10, 23, 0, 0, 0, time.UTC),
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)
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datums := BuildMetricDatum(true, statisticMetric)
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datums := BuildMetricDatum(true, false, statisticMetric)
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assert.Equal(1, len(datums), fmt.Sprintf("Valid point should create a Datum {value: %v}", statisticMetric))
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multiFieldsMetric, _ := metric.New(
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@ -98,7 +98,7 @@ func TestBuildMetricDatums(t *testing.T) {
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map[string]interface{}{"valueA": float64(10), "valueB": float64(0), "valueC": float64(100), "valueD": float64(20)},
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time.Date(2009, time.November, 10, 23, 0, 0, 0, time.UTC),
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)
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datums = BuildMetricDatum(true, multiFieldsMetric)
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datums = BuildMetricDatum(true, false, multiFieldsMetric)
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assert.Equal(4, len(datums), fmt.Sprintf("Each field should create a Datum {value: %v}", multiFieldsMetric))
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multiStatisticMetric, _ := metric.New(
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@ -112,10 +112,27 @@ func TestBuildMetricDatums(t *testing.T) {
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},
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time.Date(2009, time.November, 10, 23, 0, 0, 0, time.UTC),
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)
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datums = BuildMetricDatum(true, multiStatisticMetric)
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datums = BuildMetricDatum(true, false, multiStatisticMetric)
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assert.Equal(7, len(datums), fmt.Sprintf("Valid point should create a Datum {value: %v}", multiStatisticMetric))
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}
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func TestMetricDatumResolution(t *testing.T) {
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const expectedStandardResolutionValue = int64(60)
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const expectedHighResolutionValue = int64(1)
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assert := assert.New(t)
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metric := testutil.TestMetric(1)
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standardResolutionDatum := BuildMetricDatum(false, false, metric)
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actualStandardResolutionValue := *standardResolutionDatum[0].StorageResolution
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assert.Equal(expectedStandardResolutionValue, actualStandardResolutionValue)
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highResolutionDatum := BuildMetricDatum(false, true, metric)
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actualHighResolutionValue := *highResolutionDatum[0].StorageResolution
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assert.Equal(expectedHighResolutionValue, actualHighResolutionValue)
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}
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func TestBuildMetricDatums_SkipEmptyTags(t *testing.T) {
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input := testutil.MustMetric(
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"cpu",
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@ -129,7 +146,7 @@ func TestBuildMetricDatums_SkipEmptyTags(t *testing.T) {
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time.Unix(0, 0),
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)
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datums := BuildMetricDatum(true, input)
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datums := BuildMetricDatum(true, false, input)
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require.Len(t, datums[0].Dimensions, 1)
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}
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