Class: Google::Monitoring::V3::Aggregation

Inherits:

Object

Object
Google::Monitoring::V3::Aggregation

show all

Defined in:: lib/google/cloud/monitoring/v3/doc/google/monitoring/v3/common.rb

Overview

Describes how to combine multiple time series to provide different views of the data. Aggregation consists of an alignment step on individual time series (+alignment_period+ and +per_series_aligner+) followed by an optional reduction step of the data across the aligned time series (+cross_series_reducer+ and +group_by_fields+). For more details, see Aggregation.

Defined Under Namespace

Modules: Aligner, Reducer

Instance Attribute Summary collapse

#alignment_period ⇒ Google::Protobuf::Duration
The alignment period for per-time series alignment.
#cross_series_reducer ⇒ Google::Monitoring::V3::Aggregation::Reducer
The approach to be used to combine time series.
#group_by_fields ⇒ Array<String>
The set of fields to preserve when +crossSeriesReducer+ is specified.
#per_series_aligner ⇒ Google::Monitoring::V3::Aggregation::Aligner
The approach to be used to align individual time series.

Instance Attribute Details

#alignment_period ⇒ `Google::Protobuf::Duration`

Returns The alignment period for per-time series alignment. If present, +alignmentPeriod+ must be at least 60 seconds. After per-time series alignment, each time series will contain data points only on the period boundaries. If +perSeriesAligner+ is not specified or equals +ALIGN_NONE+, then this field is ignored. If +perSeriesAligner+ is specified and does not equal +ALIGN_NONE+, then this field must be defined; otherwise an error is returned.

Returns:

(Google::Protobuf::Duration) —
The alignment period for per-time series alignment. If present, +alignmentPeriod+ must be at least 60 seconds. After per-time series alignment, each time series will contain data points only on the period boundaries. If +perSeriesAligner+ is not specified or equals +ALIGN_NONE+, then this field is ignored. If +perSeriesAligner+ is specified and does not equal +ALIGN_NONE+, then this field must be defined; otherwise an error is returned.

# File 'lib/google/cloud/monitoring/v3/doc/google/monitoring/v3/common.rb', line 109

class Aggregation
  # The Aligner describes how to bring the data points in a single
  # time series into temporal alignment.
  module Aligner
    # No alignment. Raw data is returned. Not valid if cross-time
    # series reduction is requested. The value type of the result is
    # the same as the value type of the input.
    ALIGN_NONE = 0

    # Align and convert to delta metric type. This alignment is valid
    # for cumulative metrics and delta metrics. Aligning an existing
    # delta metric to a delta metric requires that the alignment
    # period be increased. The value type of the result is the same
    # as the value type of the input.
    #
    # One can think of this aligner as a rate but without time units; that
    # is, the output is conceptually (second_point - first_point).
    ALIGN_DELTA = 1

    # Align and convert to a rate. This alignment is valid for
    # cumulative metrics and delta metrics with numeric values. The output is a
    # gauge metric with value type
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    #
    # One can think of this aligner as conceptually providing the slope of
    # the line that passes through the value at the start and end of the
    # window. In other words, this is conceptually ((y1 - y0)/(t1 - t0)),
    # and the output unit is one that has a "/time" dimension.
    #
    # If, by rate, you are looking for percentage change, see the
    # +ALIGN_PERCENT_CHANGE+ aligner option.
    ALIGN_RATE = 2

    # Align by interpolating between adjacent points around the
    # period boundary. This alignment is valid for gauge
    # metrics with numeric values. The value type of the result is the same
    # as the value type of the input.
    ALIGN_INTERPOLATE = 3

    # Align by shifting the oldest data point before the period
    # boundary to the boundary. This alignment is valid for gauge
    # metrics. The value type of the result is the same as the
    # value type of the input.
    ALIGN_NEXT_OLDER = 4

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the minimum of all data points in the
    # period. This alignment is valid for gauge and delta metrics with numeric
    # values. The value type of the result is the same as the value
    # type of the input.
    ALIGN_MIN = 10

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the maximum of all data points in the
    # period. This alignment is valid for gauge and delta metrics with numeric
    # values. The value type of the result is the same as the value
    # type of the input.
    ALIGN_MAX = 11

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the average or arithmetic mean of all
    # data points in the period. This alignment is valid for gauge and delta
    # metrics with numeric values. The value type of the output is
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    ALIGN_MEAN = 12

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the count of all data points in the
    # period. This alignment is valid for gauge and delta metrics with numeric
    # or Boolean values. The value type of the output is
    # {Google::Api::MetricDescriptor::ValueType::INT64 INT64}.
    ALIGN_COUNT = 13

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the sum of all data points in the
    # period. This alignment is valid for gauge and delta metrics with numeric
    # and distribution values. The value type of the output is the
    # same as the value type of the input.
    ALIGN_SUM = 14

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the standard deviation of all data
    # points in the period. This alignment is valid for gauge and delta metrics
    # with numeric values. The value type of the output is
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    ALIGN_STDDEV = 15

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the count of True-valued data points in the
    # period. This alignment is valid for gauge metrics with
    # Boolean values. The value type of the output is
    # {Google::Api::MetricDescriptor::ValueType::INT64 INT64}.
    ALIGN_COUNT_TRUE = 16

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the count of False-valued data points in the
    # period. This alignment is valid for gauge metrics with
    # Boolean values. The value type of the output is
    # {Google::Api::MetricDescriptor::ValueType::INT64 INT64}.
    ALIGN_COUNT_FALSE = 24

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the fraction of True-valued data points in the
    # period. This alignment is valid for gauge metrics with Boolean values.
    # The output value is in the range [0, 1] and has value type
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    ALIGN_FRACTION_TRUE = 17

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the 99th percentile of all data
    # points in the period. This alignment is valid for gauge and delta metrics
    # with distribution values. The output is a gauge metric with value type
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    ALIGN_PERCENTILE_99 = 18

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the 95th percentile of all data
    # points in the period. This alignment is valid for gauge and delta metrics
    # with distribution values. The output is a gauge metric with value type
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    ALIGN_PERCENTILE_95 = 19

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the 50th percentile of all data
    # points in the period. This alignment is valid for gauge and delta metrics
    # with distribution values. The output is a gauge metric with value type
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    ALIGN_PERCENTILE_50 = 20

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the 5th percentile of all data
    # points in the period. This alignment is valid for gauge and delta metrics
    # with distribution values. The output is a gauge metric with value type
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    ALIGN_PERCENTILE_05 = 21

    # Align and convert to a percentage change. This alignment is valid for
    # gauge and delta metrics with numeric values. This alignment conceptually
    # computes the equivalent of "((current - previous)/previous)*100"
    # where previous value is determined based on the alignmentPeriod.
    # In the event that previous is 0 the calculated value is infinity with the
    # exception that if both (current - previous) and previous are 0 the
    # calculated value is 0.
    # A 10 minute moving mean is computed at each point of the time window
    # prior to the above calculation to smooth the metric and prevent false
    # positives from very short lived spikes.
    # Only applicable for data that is >= 0. Any values < 0 are treated as
    # no data. While delta metrics are accepted by this alignment special care
    # should be taken that the values for the metric will always be positive.
    # The output is a gauge metric with value type
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    ALIGN_PERCENT_CHANGE = 23
  end

  # A Reducer describes how to aggregate data points from multiple
  # time series into a single time series.
  module Reducer
    # No cross-time series reduction. The output of the aligner is
    # returned.
    REDUCE_NONE = 0

    # Reduce by computing the mean across time series for each
    # alignment period. This reducer is valid for delta and
    # gauge metrics with numeric or distribution values. The value type of the
    # output is {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    REDUCE_MEAN = 1

    # Reduce by computing the minimum across time series for each
    # alignment period. This reducer is valid for delta and
    # gauge metrics with numeric values. The value type of the output
    # is the same as the value type of the input.
    REDUCE_MIN = 2

    # Reduce by computing the maximum across time series for each
    # alignment period. This reducer is valid for delta and
    # gauge metrics with numeric values. The value type of the output
    # is the same as the value type of the input.
    REDUCE_MAX = 3

    # Reduce by computing the sum across time series for each
    # alignment period. This reducer is valid for delta and
    # gauge metrics with numeric and distribution values. The value type of
    # the output is the same as the value type of the input.
    REDUCE_SUM = 4

    # Reduce by computing the standard deviation across time series
    # for each alignment period. This reducer is valid for delta
    # and gauge metrics with numeric or distribution values. The value type of
    # the output is {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    REDUCE_STDDEV = 5

    # Reduce by computing the count of data points across time series
    # for each alignment period. This reducer is valid for delta
    # and gauge metrics of numeric, Boolean, distribution, and string value
    # type. The value type of the output is
    # {Google::Api::MetricDescriptor::ValueType::INT64 INT64}.
    REDUCE_COUNT = 6

    # Reduce by computing the count of True-valued data points across time
    # series for each alignment period. This reducer is valid for delta
    # and gauge metrics of Boolean value type. The value type of
    # the output is {Google::Api::MetricDescriptor::ValueType::INT64 INT64}.
    REDUCE_COUNT_TRUE = 7

    # Reduce by computing the count of False-valued data points across time
    # series for each alignment period. This reducer is valid for delta
    # and gauge metrics of Boolean value type. The value type of
    # the output is {Google::Api::MetricDescriptor::ValueType::INT64 INT64}.
    REDUCE_COUNT_FALSE = 15

    # Reduce by computing the fraction of True-valued data points across time
    # series for each alignment period. This reducer is valid for delta
    # and gauge metrics of Boolean value type. The output value is in the
    # range [0, 1] and has value type
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    REDUCE_FRACTION_TRUE = 8

    # Reduce by computing 99th percentile of data points across time series
    # for each alignment period. This reducer is valid for gauge and delta
    # metrics of numeric and distribution type. The value of the output is
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}
    REDUCE_PERCENTILE_99 = 9

    # Reduce by computing 95th percentile of data points across time series
    # for each alignment period. This reducer is valid for gauge and delta
    # metrics of numeric and distribution type. The value of the output is
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}
    REDUCE_PERCENTILE_95 = 10

    # Reduce by computing 50th percentile of data points across time series
    # for each alignment period. This reducer is valid for gauge and delta
    # metrics of numeric and distribution type. The value of the output is
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}
    REDUCE_PERCENTILE_50 = 11

    # Reduce by computing 5th percentile of data points across time series
    # for each alignment period. This reducer is valid for gauge and delta
    # metrics of numeric and distribution type. The value of the output is
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}
    REDUCE_PERCENTILE_05 = 12
  end
end

#cross_series_reducer ⇒ `Google::Monitoring::V3::Aggregation::Reducer`

The approach to be used to combine time series. Not all reducer functions may be applied to all time series, depending on the metric type and the value type of the original time series. Reduction may change the metric type of value type of the time series.

Time series data must be aligned in order to perform cross-time series reduction. If +crossSeriesReducer+ is specified, then +perSeriesAligner+ must be specified and not equal +ALIGN_NONE+ and +alignmentPeriod+ must be specified; otherwise, an error is returned.

Returns:

(Google::Monitoring::V3::Aggregation::Reducer) —
The approach to be used to combine time series. Not all reducer functions may be applied to all time series, depending on the metric type and the value type of the original time series. Reduction may change the metric type of value type of the time series.

Time series data must be aligned in order to perform cross-time series reduction. If +crossSeriesReducer+ is specified, then +perSeriesAligner+ must be specified and not equal +ALIGN_NONE+ and +alignmentPeriod+ must be specified; otherwise, an error is returned.

# File 'lib/google/cloud/monitoring/v3/doc/google/monitoring/v3/common.rb', line 109

class Aggregation
  # The Aligner describes how to bring the data points in a single
  # time series into temporal alignment.
  module Aligner
    # No alignment. Raw data is returned. Not valid if cross-time
    # series reduction is requested. The value type of the result is
    # the same as the value type of the input.
    ALIGN_NONE = 0

    # Align and convert to delta metric type. This alignment is valid
    # for cumulative metrics and delta metrics. Aligning an existing
    # delta metric to a delta metric requires that the alignment
    # period be increased. The value type of the result is the same
    # as the value type of the input.
    #
    # One can think of this aligner as a rate but without time units; that
    # is, the output is conceptually (second_point - first_point).
    ALIGN_DELTA = 1

    # Align and convert to a rate. This alignment is valid for
    # cumulative metrics and delta metrics with numeric values. The output is a
    # gauge metric with value type
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    #
    # One can think of this aligner as conceptually providing the slope of
    # the line that passes through the value at the start and end of the
    # window. In other words, this is conceptually ((y1 - y0)/(t1 - t0)),
    # and the output unit is one that has a "/time" dimension.
    #
    # If, by rate, you are looking for percentage change, see the
    # +ALIGN_PERCENT_CHANGE+ aligner option.
    ALIGN_RATE = 2

    # Align by interpolating between adjacent points around the
    # period boundary. This alignment is valid for gauge
    # metrics with numeric values. The value type of the result is the same
    # as the value type of the input.
    ALIGN_INTERPOLATE = 3

    # Align by shifting the oldest data point before the period
    # boundary to the boundary. This alignment is valid for gauge
    # metrics. The value type of the result is the same as the
    # value type of the input.
    ALIGN_NEXT_OLDER = 4

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the minimum of all data points in the
    # period. This alignment is valid for gauge and delta metrics with numeric
    # values. The value type of the result is the same as the value
    # type of the input.
    ALIGN_MIN = 10

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the maximum of all data points in the
    # period. This alignment is valid for gauge and delta metrics with numeric
    # values. The value type of the result is the same as the value
    # type of the input.
    ALIGN_MAX = 11

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the average or arithmetic mean of all
    # data points in the period. This alignment is valid for gauge and delta
    # metrics with numeric values. The value type of the output is
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    ALIGN_MEAN = 12

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the count of all data points in the
    # period. This alignment is valid for gauge and delta metrics with numeric
    # or Boolean values. The value type of the output is
    # {Google::Api::MetricDescriptor::ValueType::INT64 INT64}.
    ALIGN_COUNT = 13

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the sum of all data points in the
    # period. This alignment is valid for gauge and delta metrics with numeric
    # and distribution values. The value type of the output is the
    # same as the value type of the input.
    ALIGN_SUM = 14

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the standard deviation of all data
    # points in the period. This alignment is valid for gauge and delta metrics
    # with numeric values. The value type of the output is
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    ALIGN_STDDEV = 15

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the count of True-valued data points in the
    # period. This alignment is valid for gauge metrics with
    # Boolean values. The value type of the output is
    # {Google::Api::MetricDescriptor::ValueType::INT64 INT64}.
    ALIGN_COUNT_TRUE = 16

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the count of False-valued data points in the
    # period. This alignment is valid for gauge metrics with
    # Boolean values. The value type of the output is
    # {Google::Api::MetricDescriptor::ValueType::INT64 INT64}.
    ALIGN_COUNT_FALSE = 24

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the fraction of True-valued data points in the
    # period. This alignment is valid for gauge metrics with Boolean values.
    # The output value is in the range [0, 1] and has value type
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    ALIGN_FRACTION_TRUE = 17

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the 99th percentile of all data
    # points in the period. This alignment is valid for gauge and delta metrics
    # with distribution values. The output is a gauge metric with value type
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    ALIGN_PERCENTILE_99 = 18

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the 95th percentile of all data
    # points in the period. This alignment is valid for gauge and delta metrics
    # with distribution values. The output is a gauge metric with value type
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    ALIGN_PERCENTILE_95 = 19

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the 50th percentile of all data
    # points in the period. This alignment is valid for gauge and delta metrics
    # with distribution values. The output is a gauge metric with value type
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    ALIGN_PERCENTILE_50 = 20

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the 5th percentile of all data
    # points in the period. This alignment is valid for gauge and delta metrics
    # with distribution values. The output is a gauge metric with value type
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    ALIGN_PERCENTILE_05 = 21

    # Align and convert to a percentage change. This alignment is valid for
    # gauge and delta metrics with numeric values. This alignment conceptually
    # computes the equivalent of "((current - previous)/previous)*100"
    # where previous value is determined based on the alignmentPeriod.
    # In the event that previous is 0 the calculated value is infinity with the
    # exception that if both (current - previous) and previous are 0 the
    # calculated value is 0.
    # A 10 minute moving mean is computed at each point of the time window
    # prior to the above calculation to smooth the metric and prevent false
    # positives from very short lived spikes.
    # Only applicable for data that is >= 0. Any values < 0 are treated as
    # no data. While delta metrics are accepted by this alignment special care
    # should be taken that the values for the metric will always be positive.
    # The output is a gauge metric with value type
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    ALIGN_PERCENT_CHANGE = 23
  end

  # A Reducer describes how to aggregate data points from multiple
  # time series into a single time series.
  module Reducer
    # No cross-time series reduction. The output of the aligner is
    # returned.
    REDUCE_NONE = 0

    # Reduce by computing the mean across time series for each
    # alignment period. This reducer is valid for delta and
    # gauge metrics with numeric or distribution values. The value type of the
    # output is {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    REDUCE_MEAN = 1

    # Reduce by computing the minimum across time series for each
    # alignment period. This reducer is valid for delta and
    # gauge metrics with numeric values. The value type of the output
    # is the same as the value type of the input.
    REDUCE_MIN = 2

    # Reduce by computing the maximum across time series for each
    # alignment period. This reducer is valid for delta and
    # gauge metrics with numeric values. The value type of the output
    # is the same as the value type of the input.
    REDUCE_MAX = 3

    # Reduce by computing the sum across time series for each
    # alignment period. This reducer is valid for delta and
    # gauge metrics with numeric and distribution values. The value type of
    # the output is the same as the value type of the input.
    REDUCE_SUM = 4

    # Reduce by computing the standard deviation across time series
    # for each alignment period. This reducer is valid for delta
    # and gauge metrics with numeric or distribution values. The value type of
    # the output is {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    REDUCE_STDDEV = 5

    # Reduce by computing the count of data points across time series
    # for each alignment period. This reducer is valid for delta
    # and gauge metrics of numeric, Boolean, distribution, and string value
    # type. The value type of the output is
    # {Google::Api::MetricDescriptor::ValueType::INT64 INT64}.
    REDUCE_COUNT = 6

    # Reduce by computing the count of True-valued data points across time
    # series for each alignment period. This reducer is valid for delta
    # and gauge metrics of Boolean value type. The value type of
    # the output is {Google::Api::MetricDescriptor::ValueType::INT64 INT64}.
    REDUCE_COUNT_TRUE = 7

    # Reduce by computing the count of False-valued data points across time
    # series for each alignment period. This reducer is valid for delta
    # and gauge metrics of Boolean value type. The value type of
    # the output is {Google::Api::MetricDescriptor::ValueType::INT64 INT64}.
    REDUCE_COUNT_FALSE = 15

    # Reduce by computing the fraction of True-valued data points across time
    # series for each alignment period. This reducer is valid for delta
    # and gauge metrics of Boolean value type. The output value is in the
    # range [0, 1] and has value type
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    REDUCE_FRACTION_TRUE = 8

    # Reduce by computing 99th percentile of data points across time series
    # for each alignment period. This reducer is valid for gauge and delta
    # metrics of numeric and distribution type. The value of the output is
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}
    REDUCE_PERCENTILE_99 = 9

    # Reduce by computing 95th percentile of data points across time series
    # for each alignment period. This reducer is valid for gauge and delta
    # metrics of numeric and distribution type. The value of the output is
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}
    REDUCE_PERCENTILE_95 = 10

    # Reduce by computing 50th percentile of data points across time series
    # for each alignment period. This reducer is valid for gauge and delta
    # metrics of numeric and distribution type. The value of the output is
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}
    REDUCE_PERCENTILE_50 = 11

    # Reduce by computing 5th percentile of data points across time series
    # for each alignment period. This reducer is valid for gauge and delta
    # metrics of numeric and distribution type. The value of the output is
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}
    REDUCE_PERCENTILE_05 = 12
  end
end

#group_by_fields ⇒ `Array<String>`

Returns The set of fields to preserve when +crossSeriesReducer+ is specified. The +groupByFields+ determine how the time series are partitioned into subsets prior to applying the aggregation function. Each subset contains time series that have the same value for each of the grouping fields. Each individual time series is a member of exactly one subset. The +crossSeriesReducer+ is applied to each subset of time series. It is not possible to reduce across different resource types, so this field implicitly contains +resource.type+. Fields not specified in +groupByFields+ are aggregated away. If +groupByFields+ is not specified and all the time series have the same resource type, then the time series are aggregated into a single output time series. If +crossSeriesReducer+ is not defined, this field is ignored.

Returns:

(Array<String>) —
The set of fields to preserve when +crossSeriesReducer+ is specified. The +groupByFields+ determine how the time series are partitioned into subsets prior to applying the aggregation function. Each subset contains time series that have the same value for each of the grouping fields. Each individual time series is a member of exactly one subset. The +crossSeriesReducer+ is applied to each subset of time series. It is not possible to reduce across different resource types, so this field implicitly contains +resource.type+. Fields not specified in +groupByFields+ are aggregated away. If +groupByFields+ is not specified and all the time series have the same resource type, then the time series are aggregated into a single output time series. If +crossSeriesReducer+ is not defined, this field is ignored.

# File 'lib/google/cloud/monitoring/v3/doc/google/monitoring/v3/common.rb', line 109

class Aggregation
  # The Aligner describes how to bring the data points in a single
  # time series into temporal alignment.
  module Aligner
    # No alignment. Raw data is returned. Not valid if cross-time
    # series reduction is requested. The value type of the result is
    # the same as the value type of the input.
    ALIGN_NONE = 0

    # Align and convert to delta metric type. This alignment is valid
    # for cumulative metrics and delta metrics. Aligning an existing
    # delta metric to a delta metric requires that the alignment
    # period be increased. The value type of the result is the same
    # as the value type of the input.
    #
    # One can think of this aligner as a rate but without time units; that
    # is, the output is conceptually (second_point - first_point).
    ALIGN_DELTA = 1

    # Align and convert to a rate. This alignment is valid for
    # cumulative metrics and delta metrics with numeric values. The output is a
    # gauge metric with value type
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    #
    # One can think of this aligner as conceptually providing the slope of
    # the line that passes through the value at the start and end of the
    # window. In other words, this is conceptually ((y1 - y0)/(t1 - t0)),
    # and the output unit is one that has a "/time" dimension.
    #
    # If, by rate, you are looking for percentage change, see the
    # +ALIGN_PERCENT_CHANGE+ aligner option.
    ALIGN_RATE = 2

    # Align by interpolating between adjacent points around the
    # period boundary. This alignment is valid for gauge
    # metrics with numeric values. The value type of the result is the same
    # as the value type of the input.
    ALIGN_INTERPOLATE = 3

    # Align by shifting the oldest data point before the period
    # boundary to the boundary. This alignment is valid for gauge
    # metrics. The value type of the result is the same as the
    # value type of the input.
    ALIGN_NEXT_OLDER = 4

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the minimum of all data points in the
    # period. This alignment is valid for gauge and delta metrics with numeric
    # values. The value type of the result is the same as the value
    # type of the input.
    ALIGN_MIN = 10

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the maximum of all data points in the
    # period. This alignment is valid for gauge and delta metrics with numeric
    # values. The value type of the result is the same as the value
    # type of the input.
    ALIGN_MAX = 11

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the average or arithmetic mean of all
    # data points in the period. This alignment is valid for gauge and delta
    # metrics with numeric values. The value type of the output is
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    ALIGN_MEAN = 12

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the count of all data points in the
    # period. This alignment is valid for gauge and delta metrics with numeric
    # or Boolean values. The value type of the output is
    # {Google::Api::MetricDescriptor::ValueType::INT64 INT64}.
    ALIGN_COUNT = 13

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the sum of all data points in the
    # period. This alignment is valid for gauge and delta metrics with numeric
    # and distribution values. The value type of the output is the
    # same as the value type of the input.
    ALIGN_SUM = 14

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the standard deviation of all data
    # points in the period. This alignment is valid for gauge and delta metrics
    # with numeric values. The value type of the output is
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    ALIGN_STDDEV = 15

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the count of True-valued data points in the
    # period. This alignment is valid for gauge metrics with
    # Boolean values. The value type of the output is
    # {Google::Api::MetricDescriptor::ValueType::INT64 INT64}.
    ALIGN_COUNT_TRUE = 16

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the count of False-valued data points in the
    # period. This alignment is valid for gauge metrics with
    # Boolean values. The value type of the output is
    # {Google::Api::MetricDescriptor::ValueType::INT64 INT64}.
    ALIGN_COUNT_FALSE = 24

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the fraction of True-valued data points in the
    # period. This alignment is valid for gauge metrics with Boolean values.
    # The output value is in the range [0, 1] and has value type
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    ALIGN_FRACTION_TRUE = 17

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the 99th percentile of all data
    # points in the period. This alignment is valid for gauge and delta metrics
    # with distribution values. The output is a gauge metric with value type
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    ALIGN_PERCENTILE_99 = 18

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the 95th percentile of all data
    # points in the period. This alignment is valid for gauge and delta metrics
    # with distribution values. The output is a gauge metric with value type
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    ALIGN_PERCENTILE_95 = 19

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the 50th percentile of all data
    # points in the period. This alignment is valid for gauge and delta metrics
    # with distribution values. The output is a gauge metric with value type
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    ALIGN_PERCENTILE_50 = 20

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the 5th percentile of all data
    # points in the period. This alignment is valid for gauge and delta metrics
    # with distribution values. The output is a gauge metric with value type
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    ALIGN_PERCENTILE_05 = 21

    # Align and convert to a percentage change. This alignment is valid for
    # gauge and delta metrics with numeric values. This alignment conceptually
    # computes the equivalent of "((current - previous)/previous)*100"
    # where previous value is determined based on the alignmentPeriod.
    # In the event that previous is 0 the calculated value is infinity with the
    # exception that if both (current - previous) and previous are 0 the
    # calculated value is 0.
    # A 10 minute moving mean is computed at each point of the time window
    # prior to the above calculation to smooth the metric and prevent false
    # positives from very short lived spikes.
    # Only applicable for data that is >= 0. Any values < 0 are treated as
    # no data. While delta metrics are accepted by this alignment special care
    # should be taken that the values for the metric will always be positive.
    # The output is a gauge metric with value type
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    ALIGN_PERCENT_CHANGE = 23
  end

  # A Reducer describes how to aggregate data points from multiple
  # time series into a single time series.
  module Reducer
    # No cross-time series reduction. The output of the aligner is
    # returned.
    REDUCE_NONE = 0

    # Reduce by computing the mean across time series for each
    # alignment period. This reducer is valid for delta and
    # gauge metrics with numeric or distribution values. The value type of the
    # output is {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    REDUCE_MEAN = 1

    # Reduce by computing the minimum across time series for each
    # alignment period. This reducer is valid for delta and
    # gauge metrics with numeric values. The value type of the output
    # is the same as the value type of the input.
    REDUCE_MIN = 2

    # Reduce by computing the maximum across time series for each
    # alignment period. This reducer is valid for delta and
    # gauge metrics with numeric values. The value type of the output
    # is the same as the value type of the input.
    REDUCE_MAX = 3

    # Reduce by computing the sum across time series for each
    # alignment period. This reducer is valid for delta and
    # gauge metrics with numeric and distribution values. The value type of
    # the output is the same as the value type of the input.
    REDUCE_SUM = 4

    # Reduce by computing the standard deviation across time series
    # for each alignment period. This reducer is valid for delta
    # and gauge metrics with numeric or distribution values. The value type of
    # the output is {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    REDUCE_STDDEV = 5

    # Reduce by computing the count of data points across time series
    # for each alignment period. This reducer is valid for delta
    # and gauge metrics of numeric, Boolean, distribution, and string value
    # type. The value type of the output is
    # {Google::Api::MetricDescriptor::ValueType::INT64 INT64}.
    REDUCE_COUNT = 6

    # Reduce by computing the count of True-valued data points across time
    # series for each alignment period. This reducer is valid for delta
    # and gauge metrics of Boolean value type. The value type of
    # the output is {Google::Api::MetricDescriptor::ValueType::INT64 INT64}.
    REDUCE_COUNT_TRUE = 7

    # Reduce by computing the count of False-valued data points across time
    # series for each alignment period. This reducer is valid for delta
    # and gauge metrics of Boolean value type. The value type of
    # the output is {Google::Api::MetricDescriptor::ValueType::INT64 INT64}.
    REDUCE_COUNT_FALSE = 15

    # Reduce by computing the fraction of True-valued data points across time
    # series for each alignment period. This reducer is valid for delta
    # and gauge metrics of Boolean value type. The output value is in the
    # range [0, 1] and has value type
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    REDUCE_FRACTION_TRUE = 8

    # Reduce by computing 99th percentile of data points across time series
    # for each alignment period. This reducer is valid for gauge and delta
    # metrics of numeric and distribution type. The value of the output is
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}
    REDUCE_PERCENTILE_99 = 9

    # Reduce by computing 95th percentile of data points across time series
    # for each alignment period. This reducer is valid for gauge and delta
    # metrics of numeric and distribution type. The value of the output is
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}
    REDUCE_PERCENTILE_95 = 10

    # Reduce by computing 50th percentile of data points across time series
    # for each alignment period. This reducer is valid for gauge and delta
    # metrics of numeric and distribution type. The value of the output is
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}
    REDUCE_PERCENTILE_50 = 11

    # Reduce by computing 5th percentile of data points across time series
    # for each alignment period. This reducer is valid for gauge and delta
    # metrics of numeric and distribution type. The value of the output is
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}
    REDUCE_PERCENTILE_05 = 12
  end
end

#per_series_aligner ⇒ `Google::Monitoring::V3::Aggregation::Aligner`

The approach to be used to align individual time series. Not all alignment functions may be applied to all time series, depending on the metric type and value type of the original time series. Alignment may change the metric type or the value type of the time series.

Returns:

(Google::Monitoring::V3::Aggregation::Aligner) —
The approach to be used to align individual time series. Not all alignment functions may be applied to all time series, depending on the metric type and value type of the original time series. Alignment may change the metric type or the value type of the time series.

Time series data must be aligned in order to perform cross-time series reduction. If +crossSeriesReducer+ is specified, then +perSeriesAligner+ must be specified and not equal +ALIGN_NONE+ and +alignmentPeriod+ must be specified; otherwise, an error is returned.

# File 'lib/google/cloud/monitoring/v3/doc/google/monitoring/v3/common.rb', line 109

class Aggregation
  # The Aligner describes how to bring the data points in a single
  # time series into temporal alignment.
  module Aligner
    # No alignment. Raw data is returned. Not valid if cross-time
    # series reduction is requested. The value type of the result is
    # the same as the value type of the input.
    ALIGN_NONE = 0

    # Align and convert to delta metric type. This alignment is valid
    # for cumulative metrics and delta metrics. Aligning an existing
    # delta metric to a delta metric requires that the alignment
    # period be increased. The value type of the result is the same
    # as the value type of the input.
    #
    # One can think of this aligner as a rate but without time units; that
    # is, the output is conceptually (second_point - first_point).
    ALIGN_DELTA = 1

    # Align and convert to a rate. This alignment is valid for
    # cumulative metrics and delta metrics with numeric values. The output is a
    # gauge metric with value type
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    #
    # One can think of this aligner as conceptually providing the slope of
    # the line that passes through the value at the start and end of the
    # window. In other words, this is conceptually ((y1 - y0)/(t1 - t0)),
    # and the output unit is one that has a "/time" dimension.
    #
    # If, by rate, you are looking for percentage change, see the
    # +ALIGN_PERCENT_CHANGE+ aligner option.
    ALIGN_RATE = 2

    # Align by interpolating between adjacent points around the
    # period boundary. This alignment is valid for gauge
    # metrics with numeric values. The value type of the result is the same
    # as the value type of the input.
    ALIGN_INTERPOLATE = 3

    # Align by shifting the oldest data point before the period
    # boundary to the boundary. This alignment is valid for gauge
    # metrics. The value type of the result is the same as the
    # value type of the input.
    ALIGN_NEXT_OLDER = 4

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the minimum of all data points in the
    # period. This alignment is valid for gauge and delta metrics with numeric
    # values. The value type of the result is the same as the value
    # type of the input.
    ALIGN_MIN = 10

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the maximum of all data points in the
    # period. This alignment is valid for gauge and delta metrics with numeric
    # values. The value type of the result is the same as the value
    # type of the input.
    ALIGN_MAX = 11

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the average or arithmetic mean of all
    # data points in the period. This alignment is valid for gauge and delta
    # metrics with numeric values. The value type of the output is
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    ALIGN_MEAN = 12

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the count of all data points in the
    # period. This alignment is valid for gauge and delta metrics with numeric
    # or Boolean values. The value type of the output is
    # {Google::Api::MetricDescriptor::ValueType::INT64 INT64}.
    ALIGN_COUNT = 13

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the sum of all data points in the
    # period. This alignment is valid for gauge and delta metrics with numeric
    # and distribution values. The value type of the output is the
    # same as the value type of the input.
    ALIGN_SUM = 14

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the standard deviation of all data
    # points in the period. This alignment is valid for gauge and delta metrics
    # with numeric values. The value type of the output is
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    ALIGN_STDDEV = 15

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the count of True-valued data points in the
    # period. This alignment is valid for gauge metrics with
    # Boolean values. The value type of the output is
    # {Google::Api::MetricDescriptor::ValueType::INT64 INT64}.
    ALIGN_COUNT_TRUE = 16

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the count of False-valued data points in the
    # period. This alignment is valid for gauge metrics with
    # Boolean values. The value type of the output is
    # {Google::Api::MetricDescriptor::ValueType::INT64 INT64}.
    ALIGN_COUNT_FALSE = 24

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the fraction of True-valued data points in the
    # period. This alignment is valid for gauge metrics with Boolean values.
    # The output value is in the range [0, 1] and has value type
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    ALIGN_FRACTION_TRUE = 17

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the 99th percentile of all data
    # points in the period. This alignment is valid for gauge and delta metrics
    # with distribution values. The output is a gauge metric with value type
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    ALIGN_PERCENTILE_99 = 18

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the 95th percentile of all data
    # points in the period. This alignment is valid for gauge and delta metrics
    # with distribution values. The output is a gauge metric with value type
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    ALIGN_PERCENTILE_95 = 19

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the 50th percentile of all data
    # points in the period. This alignment is valid for gauge and delta metrics
    # with distribution values. The output is a gauge metric with value type
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    ALIGN_PERCENTILE_50 = 20

    # Align time series via aggregation. The resulting data point in
    # the alignment period is the 5th percentile of all data
    # points in the period. This alignment is valid for gauge and delta metrics
    # with distribution values. The output is a gauge metric with value type
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    ALIGN_PERCENTILE_05 = 21

    # Align and convert to a percentage change. This alignment is valid for
    # gauge and delta metrics with numeric values. This alignment conceptually
    # computes the equivalent of "((current - previous)/previous)*100"
    # where previous value is determined based on the alignmentPeriod.
    # In the event that previous is 0 the calculated value is infinity with the
    # exception that if both (current - previous) and previous are 0 the
    # calculated value is 0.
    # A 10 minute moving mean is computed at each point of the time window
    # prior to the above calculation to smooth the metric and prevent false
    # positives from very short lived spikes.
    # Only applicable for data that is >= 0. Any values < 0 are treated as
    # no data. While delta metrics are accepted by this alignment special care
    # should be taken that the values for the metric will always be positive.
    # The output is a gauge metric with value type
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    ALIGN_PERCENT_CHANGE = 23
  end

  # A Reducer describes how to aggregate data points from multiple
  # time series into a single time series.
  module Reducer
    # No cross-time series reduction. The output of the aligner is
    # returned.
    REDUCE_NONE = 0

    # Reduce by computing the mean across time series for each
    # alignment period. This reducer is valid for delta and
    # gauge metrics with numeric or distribution values. The value type of the
    # output is {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    REDUCE_MEAN = 1

    # Reduce by computing the minimum across time series for each
    # alignment period. This reducer is valid for delta and
    # gauge metrics with numeric values. The value type of the output
    # is the same as the value type of the input.
    REDUCE_MIN = 2

    # Reduce by computing the maximum across time series for each
    # alignment period. This reducer is valid for delta and
    # gauge metrics with numeric values. The value type of the output
    # is the same as the value type of the input.
    REDUCE_MAX = 3

    # Reduce by computing the sum across time series for each
    # alignment period. This reducer is valid for delta and
    # gauge metrics with numeric and distribution values. The value type of
    # the output is the same as the value type of the input.
    REDUCE_SUM = 4

    # Reduce by computing the standard deviation across time series
    # for each alignment period. This reducer is valid for delta
    # and gauge metrics with numeric or distribution values. The value type of
    # the output is {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    REDUCE_STDDEV = 5

    # Reduce by computing the count of data points across time series
    # for each alignment period. This reducer is valid for delta
    # and gauge metrics of numeric, Boolean, distribution, and string value
    # type. The value type of the output is
    # {Google::Api::MetricDescriptor::ValueType::INT64 INT64}.
    REDUCE_COUNT = 6

    # Reduce by computing the count of True-valued data points across time
    # series for each alignment period. This reducer is valid for delta
    # and gauge metrics of Boolean value type. The value type of
    # the output is {Google::Api::MetricDescriptor::ValueType::INT64 INT64}.
    REDUCE_COUNT_TRUE = 7

    # Reduce by computing the count of False-valued data points across time
    # series for each alignment period. This reducer is valid for delta
    # and gauge metrics of Boolean value type. The value type of
    # the output is {Google::Api::MetricDescriptor::ValueType::INT64 INT64}.
    REDUCE_COUNT_FALSE = 15

    # Reduce by computing the fraction of True-valued data points across time
    # series for each alignment period. This reducer is valid for delta
    # and gauge metrics of Boolean value type. The output value is in the
    # range [0, 1] and has value type
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}.
    REDUCE_FRACTION_TRUE = 8

    # Reduce by computing 99th percentile of data points across time series
    # for each alignment period. This reducer is valid for gauge and delta
    # metrics of numeric and distribution type. The value of the output is
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}
    REDUCE_PERCENTILE_99 = 9

    # Reduce by computing 95th percentile of data points across time series
    # for each alignment period. This reducer is valid for gauge and delta
    # metrics of numeric and distribution type. The value of the output is
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}
    REDUCE_PERCENTILE_95 = 10

    # Reduce by computing 50th percentile of data points across time series
    # for each alignment period. This reducer is valid for gauge and delta
    # metrics of numeric and distribution type. The value of the output is
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}
    REDUCE_PERCENTILE_50 = 11

    # Reduce by computing 5th percentile of data points across time series
    # for each alignment period. This reducer is valid for gauge and delta
    # metrics of numeric and distribution type. The value of the output is
    # {Google::Api::MetricDescriptor::ValueType::DOUBLE DOUBLE}
    REDUCE_PERCENTILE_05 = 12
  end
end

Class: Google::Monitoring::V3::Aggregation

Overview

Defined Under Namespace

Instance Attribute Summary collapse

Instance Attribute Details

#alignment_period ⇒ Google::Protobuf::Duration

#cross_series_reducer ⇒ Google::Monitoring::V3::Aggregation::Reducer

#group_by_fields ⇒ Array<String>

#per_series_aligner ⇒ Google::Monitoring::V3::Aggregation::Aligner

#alignment_period ⇒ `Google::Protobuf::Duration`

#cross_series_reducer ⇒ `Google::Monitoring::V3::Aggregation::Reducer`

#group_by_fields ⇒ `Array<String>`

#per_series_aligner ⇒ `Google::Monitoring::V3::Aggregation::Aligner`