Class: Google::Cloud::Monitoring::Dashboard::V1::Aggregation

Inherits:
Object
  • Object
show all
Extended by:
Protobuf::MessageExts::ClassMethods
Includes:
Protobuf::MessageExts
Defined in:
proto_docs/google/monitoring/dashboard/v1/common.rb

Overview

Describes how to combine multiple time series to provide a different view of the data. Aggregation of time series is done in two steps. First, each time series in the set is aligned to the same time interval boundaries, then the set of time series is optionally reduced in number.

Alignment consists of applying the per_series_aligner operation to each time series after its data has been divided into regular alignment_period time intervals. This process takes all of the data points in an alignment period, applies a mathematical transformation such as averaging, minimum, maximum, delta, etc., and converts them into a single data point per period.

Reduction is when the aligned and transformed time series can optionally be combined, reducing the number of time series through similar mathematical transformations. Reduction involves applying a cross_series_reducer to all the time series, optionally sorting the time series into subsets with group_by_fields, and applying the reducer to each subset.

The raw time series data can contain a huge amount of information from multiple sources. Alignment and reduction transforms this mass of data into a more manageable and representative collection of data, for example "the 95% latency across the average of all tasks in a cluster". This representative data can be more easily graphed and comprehended, and the individual time series data is still available for later drilldown. For more details, see Filtering and aggregation.

Defined Under Namespace

Modules: Aligner, Reducer

Instance Attribute Summary collapse

Instance Attribute Details

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

Returns The alignment_period specifies a time interval, in seconds, that is used to divide the data in all the [time series][google.monitoring.v3.TimeSeries] into consistent blocks of time. This will be done before the per-series aligner can be applied to the data.

The value must be at least 60 seconds. If a per-series aligner other than ALIGN_NONE is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner ALIGN_NONE is specified, then this field is ignored.

The maximum value of the alignment_period is 2 years, or 104 weeks.

Returns:

  • (::Google::Protobuf::Duration) —

    The alignment_period specifies a time interval, in seconds, that is used to divide the data in all the [time series][google.monitoring.v3.TimeSeries] into consistent blocks of time. This will be done before the per-series aligner can be applied to the data.

    The value must be at least 60 seconds. If a per-series aligner other than ALIGN_NONE is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner ALIGN_NONE is specified, then this field is ignored.

    The maximum value of the alignment_period is 2 years, or 104 weeks.



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# File 'proto_docs/google/monitoring/dashboard/v1/common.rb', line 115

class Aggregation
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  # The `Aligner` specifies the operation that will be applied to the data
  # points in each alignment period in a time series. Except for
  # `ALIGN_NONE`, which specifies that no operation be applied, each alignment
  # operation replaces the set of data values in each alignment period with
  # a single value: the result of applying the operation to the data values.
  # An aligned time series has a single data value at the end of each
  # `alignment_period`.
  #
  # An alignment operation can change the data type of the values, too. For
  # example, if you apply a counting operation to boolean values, the data
  # `value_type` in the original time series is `BOOLEAN`, but the `value_type`
  # in the aligned result is `INT64`.
  module Aligner
    # No alignment. Raw data is returned. Not valid if cross-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][google.api.MetricDescriptor.MetricKind.DELTA].
    # The output is `delta = y1 - y0`.
    #
    # This alignment is valid for
    # [CUMULATIVE][google.api.MetricDescriptor.MetricKind.CUMULATIVE] and
    # `DELTA` metrics. If the selected alignment period results in periods
    # with no data, then the aligned value for such a period is created by
    # interpolation. The `value_type`  of the aligned result is the same as
    # the `value_type` of the input.
    ALIGN_DELTA = 1

    # Align and convert to a rate. The result is computed as
    # `rate = (y1 - y0)/(t1 - t0)`, or "delta over time".
    # Think of this aligner as providing the slope of the line that passes
    # through the value at the start and at the end of the `alignment_period`.
    #
    # This aligner is valid for `CUMULATIVE`
    # and `DELTA` metrics with numeric values. If the selected alignment
    # period results in periods with no data, then the aligned value for
    # such a period is created by interpolation. The output is a `GAUGE`
    # metric with `value_type` `DOUBLE`.
    #
    # If, by "rate", you mean "percentage change", see the
    # `ALIGN_PERCENT_CHANGE` aligner instead.
    ALIGN_RATE = 2

    # Align by interpolating between adjacent points around the alignment
    # period boundary. This aligner is valid for `GAUGE` metrics with
    # numeric values. The `value_type` of the aligned result is the same as the
    # `value_type` of the input.
    ALIGN_INTERPOLATE = 3

    # Align by moving the most recent data point before the end of the
    # alignment period to the boundary at the end of the alignment
    # period. This aligner is valid for `GAUGE` metrics. The `value_type` of
    # the aligned result is the same as the `value_type` of the input.
    ALIGN_NEXT_OLDER = 4

    # Align the time series by returning the minimum value in each alignment
    # period. This aligner is valid for `GAUGE` and `DELTA` metrics with
    # numeric values. The `value_type` of the aligned result is the same as
    # the `value_type` of the input.
    ALIGN_MIN = 10

    # Align the time series by returning the maximum value in each alignment
    # period. This aligner is valid for `GAUGE` and `DELTA` metrics with
    # numeric values. The `value_type` of the aligned result is the same as
    # the `value_type` of the input.
    ALIGN_MAX = 11

    # Align the time series by returning the mean value in each alignment
    # period. This aligner is valid for `GAUGE` and `DELTA` metrics with
    # numeric values. The `value_type` of the aligned result is `DOUBLE`.
    ALIGN_MEAN = 12

    # Align the time series by returning the number of values in each alignment
    # period. This aligner is valid for `GAUGE` and `DELTA` metrics with
    # numeric or Boolean values. The `value_type` of the aligned result is
    # `INT64`.
    ALIGN_COUNT = 13

    # Align the time series by returning the sum of the values in each
    # alignment period. This aligner is valid for `GAUGE` and `DELTA`
    # metrics with numeric and distribution values. The `value_type` of the
    # aligned result is the same as the `value_type` of the input.
    ALIGN_SUM = 14

    # Align the time series by returning the standard deviation of the values
    # in each alignment period. This aligner is valid for `GAUGE` and
    # `DELTA` metrics with numeric values. The `value_type` of the output is
    # `DOUBLE`.
    ALIGN_STDDEV = 15

    # Align the time series by returning the number of `True` values in
    # each alignment period. This aligner is valid for `GAUGE` metrics with
    # Boolean values. The `value_type` of the output is `INT64`.
    ALIGN_COUNT_TRUE = 16

    # Align the time series by returning the number of `False` values in
    # each alignment period. This aligner is valid for `GAUGE` metrics with
    # Boolean values. The `value_type` of the output is `INT64`.
    ALIGN_COUNT_FALSE = 24

    # Align the time series by returning the ratio of the number of `True`
    # values to the total number of values in each alignment period. This
    # aligner is valid for `GAUGE` metrics with Boolean values. The output
    # value is in the range [0.0, 1.0] and has `value_type` `DOUBLE`.
    ALIGN_FRACTION_TRUE = 17

    # Align the time series by using [percentile
    # aggregation](https://en.wikipedia.org/wiki/Percentile). The resulting
    # data point in each alignment period is the 99th percentile of all data
    # points in the period. This aligner is valid for `GAUGE` and `DELTA`
    # metrics with distribution values. The output is a `GAUGE` metric with
    # `value_type` `DOUBLE`.
    ALIGN_PERCENTILE_99 = 18

    # Align the time series by using [percentile
    # aggregation](https://en.wikipedia.org/wiki/Percentile). The resulting
    # data point in each alignment period is the 95th percentile of all data
    # points in the period. This aligner is valid for `GAUGE` and `DELTA`
    # metrics with distribution values. The output is a `GAUGE` metric with
    # `value_type` `DOUBLE`.
    ALIGN_PERCENTILE_95 = 19

    # Align the time series by using [percentile
    # aggregation](https://en.wikipedia.org/wiki/Percentile). The resulting
    # data point in each alignment period is the 50th percentile of all data
    # points in the period. This aligner is valid for `GAUGE` and `DELTA`
    # metrics with distribution values. The output is a `GAUGE` metric with
    # `value_type` `DOUBLE`.
    ALIGN_PERCENTILE_50 = 20

    # Align the time series by using [percentile
    # aggregation](https://en.wikipedia.org/wiki/Percentile). The resulting
    # data point in each alignment period is the 5th percentile of all data
    # points in the period. This aligner is valid for `GAUGE` and `DELTA`
    # metrics with distribution values. The output is a `GAUGE` metric with
    # `value_type` `DOUBLE`.
    ALIGN_PERCENTILE_05 = 21

    # Align and convert to a percentage change. This aligner is valid for
    # `GAUGE` and `DELTA` metrics with numeric values. This alignment returns
    # `((current - previous)/previous) * 100`, where the value of `previous` is
    # determined based on the `alignment_period`.
    #
    # If the values of `current` and `previous` are both 0, then the returned
    # value is 0. If only `previous` is 0, the returned value is infinity.
    #
    # A 10-minute moving mean is computed at each point of the alignment period
    # prior to the above calculation to smooth the metric and prevent false
    # positives from very short-lived spikes. The moving mean is only
    # applicable for data whose values are `>= 0`. Any values `< 0` are
    # treated as a missing datapoint, and are ignored. 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` `DOUBLE`.
    ALIGN_PERCENT_CHANGE = 23
  end

  # A Reducer operation describes how to aggregate data points from multiple
  # time series into a single time series, where the value of each data point
  # in the resulting series is a function of all the already aligned values in
  # the input time series.
  module Reducer
    # No cross-time series reduction. The output of the `Aligner` is
    # returned.
    REDUCE_NONE = 0

    # Reduce by computing the mean value across time series for each
    # alignment period. This reducer is valid for
    # [DELTA][google.api.MetricDescriptor.MetricKind.DELTA] and
    # [GAUGE][google.api.MetricDescriptor.MetricKind.GAUGE] metrics with
    # numeric or distribution values. The `value_type` of the output is
    # [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
    REDUCE_MEAN = 1

    # Reduce by computing the minimum value 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 value 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 `DOUBLE`.
    REDUCE_STDDEV = 5

    # Reduce by computing the number 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 `INT64`.
    REDUCE_COUNT = 6

    # Reduce by computing the number 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 `INT64`.
    REDUCE_COUNT_TRUE = 7

    # Reduce by computing the number 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 `INT64`.
    REDUCE_COUNT_FALSE = 15

    # Reduce by computing the ratio of the number of `True`-valued data points
    # to the total number of data points for each alignment period. This
    # reducer is valid for `DELTA` and `GAUGE` metrics of Boolean `value_type`.
    # The output value is in the range [0.0, 1.0] and has `value_type`
    # `DOUBLE`.
    REDUCE_FRACTION_TRUE = 8

    # Reduce by computing the [99th
    # percentile](https://en.wikipedia.org/wiki/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 `DOUBLE`.
    REDUCE_PERCENTILE_99 = 9

    # Reduce by computing the [95th
    # percentile](https://en.wikipedia.org/wiki/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 `DOUBLE`.
    REDUCE_PERCENTILE_95 = 10

    # Reduce by computing the [50th
    # percentile](https://en.wikipedia.org/wiki/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 `DOUBLE`.
    REDUCE_PERCENTILE_50 = 11

    # Reduce by computing the [5th
    # percentile](https://en.wikipedia.org/wiki/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 `DOUBLE`.
    REDUCE_PERCENTILE_05 = 12
  end
end

#cross_series_reducer ⇒ ::Google::Cloud::Monitoring::Dashboard::V1::Aggregation::Reducer

Returns The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.

Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.

Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned.

Returns:

  • (::Google::Cloud::Monitoring::Dashboard::V1::Aggregation::Reducer) —

    The reduction operation to be used to combine time series into a single time series, where the value of each data point in the resulting series is a function of all the already aligned values in the input time series.

    Not all reducer operations can be applied to all time series. The valid choices depend on the metric_kind and the value_type of the original time series. Reduction can yield a time series with a different metric_kind or value_type than the input time series.

    Time series data must first be aligned (see per_series_aligner) in order to perform cross-time series reduction. If cross_series_reducer is specified, then per_series_aligner must be specified, and must not be ALIGN_NONE. An alignment_period must also be specified; otherwise, an error is returned.



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# File 'proto_docs/google/monitoring/dashboard/v1/common.rb', line 115

class Aggregation
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  # The `Aligner` specifies the operation that will be applied to the data
  # points in each alignment period in a time series. Except for
  # `ALIGN_NONE`, which specifies that no operation be applied, each alignment
  # operation replaces the set of data values in each alignment period with
  # a single value: the result of applying the operation to the data values.
  # An aligned time series has a single data value at the end of each
  # `alignment_period`.
  #
  # An alignment operation can change the data type of the values, too. For
  # example, if you apply a counting operation to boolean values, the data
  # `value_type` in the original time series is `BOOLEAN`, but the `value_type`
  # in the aligned result is `INT64`.
  module Aligner
    # No alignment. Raw data is returned. Not valid if cross-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][google.api.MetricDescriptor.MetricKind.DELTA].
    # The output is `delta = y1 - y0`.
    #
    # This alignment is valid for
    # [CUMULATIVE][google.api.MetricDescriptor.MetricKind.CUMULATIVE] and
    # `DELTA` metrics. If the selected alignment period results in periods
    # with no data, then the aligned value for such a period is created by
    # interpolation. The `value_type`  of the aligned result is the same as
    # the `value_type` of the input.
    ALIGN_DELTA = 1

    # Align and convert to a rate. The result is computed as
    # `rate = (y1 - y0)/(t1 - t0)`, or "delta over time".
    # Think of this aligner as providing the slope of the line that passes
    # through the value at the start and at the end of the `alignment_period`.
    #
    # This aligner is valid for `CUMULATIVE`
    # and `DELTA` metrics with numeric values. If the selected alignment
    # period results in periods with no data, then the aligned value for
    # such a period is created by interpolation. The output is a `GAUGE`
    # metric with `value_type` `DOUBLE`.
    #
    # If, by "rate", you mean "percentage change", see the
    # `ALIGN_PERCENT_CHANGE` aligner instead.
    ALIGN_RATE = 2

    # Align by interpolating between adjacent points around the alignment
    # period boundary. This aligner is valid for `GAUGE` metrics with
    # numeric values. The `value_type` of the aligned result is the same as the
    # `value_type` of the input.
    ALIGN_INTERPOLATE = 3

    # Align by moving the most recent data point before the end of the
    # alignment period to the boundary at the end of the alignment
    # period. This aligner is valid for `GAUGE` metrics. The `value_type` of
    # the aligned result is the same as the `value_type` of the input.
    ALIGN_NEXT_OLDER = 4

    # Align the time series by returning the minimum value in each alignment
    # period. This aligner is valid for `GAUGE` and `DELTA` metrics with
    # numeric values. The `value_type` of the aligned result is the same as
    # the `value_type` of the input.
    ALIGN_MIN = 10

    # Align the time series by returning the maximum value in each alignment
    # period. This aligner is valid for `GAUGE` and `DELTA` metrics with
    # numeric values. The `value_type` of the aligned result is the same as
    # the `value_type` of the input.
    ALIGN_MAX = 11

    # Align the time series by returning the mean value in each alignment
    # period. This aligner is valid for `GAUGE` and `DELTA` metrics with
    # numeric values. The `value_type` of the aligned result is `DOUBLE`.
    ALIGN_MEAN = 12

    # Align the time series by returning the number of values in each alignment
    # period. This aligner is valid for `GAUGE` and `DELTA` metrics with
    # numeric or Boolean values. The `value_type` of the aligned result is
    # `INT64`.
    ALIGN_COUNT = 13

    # Align the time series by returning the sum of the values in each
    # alignment period. This aligner is valid for `GAUGE` and `DELTA`
    # metrics with numeric and distribution values. The `value_type` of the
    # aligned result is the same as the `value_type` of the input.
    ALIGN_SUM = 14

    # Align the time series by returning the standard deviation of the values
    # in each alignment period. This aligner is valid for `GAUGE` and
    # `DELTA` metrics with numeric values. The `value_type` of the output is
    # `DOUBLE`.
    ALIGN_STDDEV = 15

    # Align the time series by returning the number of `True` values in
    # each alignment period. This aligner is valid for `GAUGE` metrics with
    # Boolean values. The `value_type` of the output is `INT64`.
    ALIGN_COUNT_TRUE = 16

    # Align the time series by returning the number of `False` values in
    # each alignment period. This aligner is valid for `GAUGE` metrics with
    # Boolean values. The `value_type` of the output is `INT64`.
    ALIGN_COUNT_FALSE = 24

    # Align the time series by returning the ratio of the number of `True`
    # values to the total number of values in each alignment period. This
    # aligner is valid for `GAUGE` metrics with Boolean values. The output
    # value is in the range [0.0, 1.0] and has `value_type` `DOUBLE`.
    ALIGN_FRACTION_TRUE = 17

    # Align the time series by using [percentile
    # aggregation](https://en.wikipedia.org/wiki/Percentile). The resulting
    # data point in each alignment period is the 99th percentile of all data
    # points in the period. This aligner is valid for `GAUGE` and `DELTA`
    # metrics with distribution values. The output is a `GAUGE` metric with
    # `value_type` `DOUBLE`.
    ALIGN_PERCENTILE_99 = 18

    # Align the time series by using [percentile
    # aggregation](https://en.wikipedia.org/wiki/Percentile). The resulting
    # data point in each alignment period is the 95th percentile of all data
    # points in the period. This aligner is valid for `GAUGE` and `DELTA`
    # metrics with distribution values. The output is a `GAUGE` metric with
    # `value_type` `DOUBLE`.
    ALIGN_PERCENTILE_95 = 19

    # Align the time series by using [percentile
    # aggregation](https://en.wikipedia.org/wiki/Percentile). The resulting
    # data point in each alignment period is the 50th percentile of all data
    # points in the period. This aligner is valid for `GAUGE` and `DELTA`
    # metrics with distribution values. The output is a `GAUGE` metric with
    # `value_type` `DOUBLE`.
    ALIGN_PERCENTILE_50 = 20

    # Align the time series by using [percentile
    # aggregation](https://en.wikipedia.org/wiki/Percentile). The resulting
    # data point in each alignment period is the 5th percentile of all data
    # points in the period. This aligner is valid for `GAUGE` and `DELTA`
    # metrics with distribution values. The output is a `GAUGE` metric with
    # `value_type` `DOUBLE`.
    ALIGN_PERCENTILE_05 = 21

    # Align and convert to a percentage change. This aligner is valid for
    # `GAUGE` and `DELTA` metrics with numeric values. This alignment returns
    # `((current - previous)/previous) * 100`, where the value of `previous` is
    # determined based on the `alignment_period`.
    #
    # If the values of `current` and `previous` are both 0, then the returned
    # value is 0. If only `previous` is 0, the returned value is infinity.
    #
    # A 10-minute moving mean is computed at each point of the alignment period
    # prior to the above calculation to smooth the metric and prevent false
    # positives from very short-lived spikes. The moving mean is only
    # applicable for data whose values are `>= 0`. Any values `< 0` are
    # treated as a missing datapoint, and are ignored. 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` `DOUBLE`.
    ALIGN_PERCENT_CHANGE = 23
  end

  # A Reducer operation describes how to aggregate data points from multiple
  # time series into a single time series, where the value of each data point
  # in the resulting series is a function of all the already aligned values in
  # the input time series.
  module Reducer
    # No cross-time series reduction. The output of the `Aligner` is
    # returned.
    REDUCE_NONE = 0

    # Reduce by computing the mean value across time series for each
    # alignment period. This reducer is valid for
    # [DELTA][google.api.MetricDescriptor.MetricKind.DELTA] and
    # [GAUGE][google.api.MetricDescriptor.MetricKind.GAUGE] metrics with
    # numeric or distribution values. The `value_type` of the output is
    # [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
    REDUCE_MEAN = 1

    # Reduce by computing the minimum value 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 value 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 `DOUBLE`.
    REDUCE_STDDEV = 5

    # Reduce by computing the number 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 `INT64`.
    REDUCE_COUNT = 6

    # Reduce by computing the number 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 `INT64`.
    REDUCE_COUNT_TRUE = 7

    # Reduce by computing the number 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 `INT64`.
    REDUCE_COUNT_FALSE = 15

    # Reduce by computing the ratio of the number of `True`-valued data points
    # to the total number of data points for each alignment period. This
    # reducer is valid for `DELTA` and `GAUGE` metrics of Boolean `value_type`.
    # The output value is in the range [0.0, 1.0] and has `value_type`
    # `DOUBLE`.
    REDUCE_FRACTION_TRUE = 8

    # Reduce by computing the [99th
    # percentile](https://en.wikipedia.org/wiki/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 `DOUBLE`.
    REDUCE_PERCENTILE_99 = 9

    # Reduce by computing the [95th
    # percentile](https://en.wikipedia.org/wiki/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 `DOUBLE`.
    REDUCE_PERCENTILE_95 = 10

    # Reduce by computing the [50th
    # percentile](https://en.wikipedia.org/wiki/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 `DOUBLE`.
    REDUCE_PERCENTILE_50 = 11

    # Reduce by computing the [5th
    # percentile](https://en.wikipedia.org/wiki/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 `DOUBLE`.
    REDUCE_PERCENTILE_05 = 12
  end
end

#group_by_fields ⇒ ::Array<::String>

Returns The set of fields to preserve when cross_series_reducer is specified. The group_by_fields determine how the time series are partitioned into subsets prior to applying the aggregation operation. 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 cross_series_reducer 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 group_by_fields are aggregated away. If group_by_fields 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 cross_series_reducer is not defined, this field is ignored.

Returns:

  • (::Array<::String>) —

    The set of fields to preserve when cross_series_reducer is specified. The group_by_fields determine how the time series are partitioned into subsets prior to applying the aggregation operation. 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 cross_series_reducer 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 group_by_fields are aggregated away. If group_by_fields 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 cross_series_reducer is not defined, this field is ignored.



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# File 'proto_docs/google/monitoring/dashboard/v1/common.rb', line 115

class Aggregation
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  # The `Aligner` specifies the operation that will be applied to the data
  # points in each alignment period in a time series. Except for
  # `ALIGN_NONE`, which specifies that no operation be applied, each alignment
  # operation replaces the set of data values in each alignment period with
  # a single value: the result of applying the operation to the data values.
  # An aligned time series has a single data value at the end of each
  # `alignment_period`.
  #
  # An alignment operation can change the data type of the values, too. For
  # example, if you apply a counting operation to boolean values, the data
  # `value_type` in the original time series is `BOOLEAN`, but the `value_type`
  # in the aligned result is `INT64`.
  module Aligner
    # No alignment. Raw data is returned. Not valid if cross-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][google.api.MetricDescriptor.MetricKind.DELTA].
    # The output is `delta = y1 - y0`.
    #
    # This alignment is valid for
    # [CUMULATIVE][google.api.MetricDescriptor.MetricKind.CUMULATIVE] and
    # `DELTA` metrics. If the selected alignment period results in periods
    # with no data, then the aligned value for such a period is created by
    # interpolation. The `value_type`  of the aligned result is the same as
    # the `value_type` of the input.
    ALIGN_DELTA = 1

    # Align and convert to a rate. The result is computed as
    # `rate = (y1 - y0)/(t1 - t0)`, or "delta over time".
    # Think of this aligner as providing the slope of the line that passes
    # through the value at the start and at the end of the `alignment_period`.
    #
    # This aligner is valid for `CUMULATIVE`
    # and `DELTA` metrics with numeric values. If the selected alignment
    # period results in periods with no data, then the aligned value for
    # such a period is created by interpolation. The output is a `GAUGE`
    # metric with `value_type` `DOUBLE`.
    #
    # If, by "rate", you mean "percentage change", see the
    # `ALIGN_PERCENT_CHANGE` aligner instead.
    ALIGN_RATE = 2

    # Align by interpolating between adjacent points around the alignment
    # period boundary. This aligner is valid for `GAUGE` metrics with
    # numeric values. The `value_type` of the aligned result is the same as the
    # `value_type` of the input.
    ALIGN_INTERPOLATE = 3

    # Align by moving the most recent data point before the end of the
    # alignment period to the boundary at the end of the alignment
    # period. This aligner is valid for `GAUGE` metrics. The `value_type` of
    # the aligned result is the same as the `value_type` of the input.
    ALIGN_NEXT_OLDER = 4

    # Align the time series by returning the minimum value in each alignment
    # period. This aligner is valid for `GAUGE` and `DELTA` metrics with
    # numeric values. The `value_type` of the aligned result is the same as
    # the `value_type` of the input.
    ALIGN_MIN = 10

    # Align the time series by returning the maximum value in each alignment
    # period. This aligner is valid for `GAUGE` and `DELTA` metrics with
    # numeric values. The `value_type` of the aligned result is the same as
    # the `value_type` of the input.
    ALIGN_MAX = 11

    # Align the time series by returning the mean value in each alignment
    # period. This aligner is valid for `GAUGE` and `DELTA` metrics with
    # numeric values. The `value_type` of the aligned result is `DOUBLE`.
    ALIGN_MEAN = 12

    # Align the time series by returning the number of values in each alignment
    # period. This aligner is valid for `GAUGE` and `DELTA` metrics with
    # numeric or Boolean values. The `value_type` of the aligned result is
    # `INT64`.
    ALIGN_COUNT = 13

    # Align the time series by returning the sum of the values in each
    # alignment period. This aligner is valid for `GAUGE` and `DELTA`
    # metrics with numeric and distribution values. The `value_type` of the
    # aligned result is the same as the `value_type` of the input.
    ALIGN_SUM = 14

    # Align the time series by returning the standard deviation of the values
    # in each alignment period. This aligner is valid for `GAUGE` and
    # `DELTA` metrics with numeric values. The `value_type` of the output is
    # `DOUBLE`.
    ALIGN_STDDEV = 15

    # Align the time series by returning the number of `True` values in
    # each alignment period. This aligner is valid for `GAUGE` metrics with
    # Boolean values. The `value_type` of the output is `INT64`.
    ALIGN_COUNT_TRUE = 16

    # Align the time series by returning the number of `False` values in
    # each alignment period. This aligner is valid for `GAUGE` metrics with
    # Boolean values. The `value_type` of the output is `INT64`.
    ALIGN_COUNT_FALSE = 24

    # Align the time series by returning the ratio of the number of `True`
    # values to the total number of values in each alignment period. This
    # aligner is valid for `GAUGE` metrics with Boolean values. The output
    # value is in the range [0.0, 1.0] and has `value_type` `DOUBLE`.
    ALIGN_FRACTION_TRUE = 17

    # Align the time series by using [percentile
    # aggregation](https://en.wikipedia.org/wiki/Percentile). The resulting
    # data point in each alignment period is the 99th percentile of all data
    # points in the period. This aligner is valid for `GAUGE` and `DELTA`
    # metrics with distribution values. The output is a `GAUGE` metric with
    # `value_type` `DOUBLE`.
    ALIGN_PERCENTILE_99 = 18

    # Align the time series by using [percentile
    # aggregation](https://en.wikipedia.org/wiki/Percentile). The resulting
    # data point in each alignment period is the 95th percentile of all data
    # points in the period. This aligner is valid for `GAUGE` and `DELTA`
    # metrics with distribution values. The output is a `GAUGE` metric with
    # `value_type` `DOUBLE`.
    ALIGN_PERCENTILE_95 = 19

    # Align the time series by using [percentile
    # aggregation](https://en.wikipedia.org/wiki/Percentile). The resulting
    # data point in each alignment period is the 50th percentile of all data
    # points in the period. This aligner is valid for `GAUGE` and `DELTA`
    # metrics with distribution values. The output is a `GAUGE` metric with
    # `value_type` `DOUBLE`.
    ALIGN_PERCENTILE_50 = 20

    # Align the time series by using [percentile
    # aggregation](https://en.wikipedia.org/wiki/Percentile). The resulting
    # data point in each alignment period is the 5th percentile of all data
    # points in the period. This aligner is valid for `GAUGE` and `DELTA`
    # metrics with distribution values. The output is a `GAUGE` metric with
    # `value_type` `DOUBLE`.
    ALIGN_PERCENTILE_05 = 21

    # Align and convert to a percentage change. This aligner is valid for
    # `GAUGE` and `DELTA` metrics with numeric values. This alignment returns
    # `((current - previous)/previous) * 100`, where the value of `previous` is
    # determined based on the `alignment_period`.
    #
    # If the values of `current` and `previous` are both 0, then the returned
    # value is 0. If only `previous` is 0, the returned value is infinity.
    #
    # A 10-minute moving mean is computed at each point of the alignment period
    # prior to the above calculation to smooth the metric and prevent false
    # positives from very short-lived spikes. The moving mean is only
    # applicable for data whose values are `>= 0`. Any values `< 0` are
    # treated as a missing datapoint, and are ignored. 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` `DOUBLE`.
    ALIGN_PERCENT_CHANGE = 23
  end

  # A Reducer operation describes how to aggregate data points from multiple
  # time series into a single time series, where the value of each data point
  # in the resulting series is a function of all the already aligned values in
  # the input time series.
  module Reducer
    # No cross-time series reduction. The output of the `Aligner` is
    # returned.
    REDUCE_NONE = 0

    # Reduce by computing the mean value across time series for each
    # alignment period. This reducer is valid for
    # [DELTA][google.api.MetricDescriptor.MetricKind.DELTA] and
    # [GAUGE][google.api.MetricDescriptor.MetricKind.GAUGE] metrics with
    # numeric or distribution values. The `value_type` of the output is
    # [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
    REDUCE_MEAN = 1

    # Reduce by computing the minimum value 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 value 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 `DOUBLE`.
    REDUCE_STDDEV = 5

    # Reduce by computing the number 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 `INT64`.
    REDUCE_COUNT = 6

    # Reduce by computing the number 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 `INT64`.
    REDUCE_COUNT_TRUE = 7

    # Reduce by computing the number 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 `INT64`.
    REDUCE_COUNT_FALSE = 15

    # Reduce by computing the ratio of the number of `True`-valued data points
    # to the total number of data points for each alignment period. This
    # reducer is valid for `DELTA` and `GAUGE` metrics of Boolean `value_type`.
    # The output value is in the range [0.0, 1.0] and has `value_type`
    # `DOUBLE`.
    REDUCE_FRACTION_TRUE = 8

    # Reduce by computing the [99th
    # percentile](https://en.wikipedia.org/wiki/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 `DOUBLE`.
    REDUCE_PERCENTILE_99 = 9

    # Reduce by computing the [95th
    # percentile](https://en.wikipedia.org/wiki/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 `DOUBLE`.
    REDUCE_PERCENTILE_95 = 10

    # Reduce by computing the [50th
    # percentile](https://en.wikipedia.org/wiki/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 `DOUBLE`.
    REDUCE_PERCENTILE_50 = 11

    # Reduce by computing the [5th
    # percentile](https://en.wikipedia.org/wiki/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 `DOUBLE`.
    REDUCE_PERCENTILE_05 = 12
  end
end

#per_series_aligner ⇒ ::Google::Cloud::Monitoring::Dashboard::V1::Aggregation::Aligner

An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.

Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.

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

Returns:

  • (::Google::Cloud::Monitoring::Dashboard::V1::Aggregation::Aligner) —

    An Aligner describes how to bring the data points in a single time series into temporal alignment. Except for ALIGN_NONE, all alignments cause all the data points in an alignment_period to be mathematically grouped together, resulting in a single data point for each alignment_period with end timestamp at the end of the period.

    Not all alignment operations may be applied to all time series. The valid choices depend on the metric_kind and value_type of the original time series. Alignment can change the metric_kind or the value_type of the time series.

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



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# File 'proto_docs/google/monitoring/dashboard/v1/common.rb', line 115

class Aggregation
  include ::Google::Protobuf::MessageExts
  extend ::Google::Protobuf::MessageExts::ClassMethods

  # The `Aligner` specifies the operation that will be applied to the data
  # points in each alignment period in a time series. Except for
  # `ALIGN_NONE`, which specifies that no operation be applied, each alignment
  # operation replaces the set of data values in each alignment period with
  # a single value: the result of applying the operation to the data values.
  # An aligned time series has a single data value at the end of each
  # `alignment_period`.
  #
  # An alignment operation can change the data type of the values, too. For
  # example, if you apply a counting operation to boolean values, the data
  # `value_type` in the original time series is `BOOLEAN`, but the `value_type`
  # in the aligned result is `INT64`.
  module Aligner
    # No alignment. Raw data is returned. Not valid if cross-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][google.api.MetricDescriptor.MetricKind.DELTA].
    # The output is `delta = y1 - y0`.
    #
    # This alignment is valid for
    # [CUMULATIVE][google.api.MetricDescriptor.MetricKind.CUMULATIVE] and
    # `DELTA` metrics. If the selected alignment period results in periods
    # with no data, then the aligned value for such a period is created by
    # interpolation. The `value_type`  of the aligned result is the same as
    # the `value_type` of the input.
    ALIGN_DELTA = 1

    # Align and convert to a rate. The result is computed as
    # `rate = (y1 - y0)/(t1 - t0)`, or "delta over time".
    # Think of this aligner as providing the slope of the line that passes
    # through the value at the start and at the end of the `alignment_period`.
    #
    # This aligner is valid for `CUMULATIVE`
    # and `DELTA` metrics with numeric values. If the selected alignment
    # period results in periods with no data, then the aligned value for
    # such a period is created by interpolation. The output is a `GAUGE`
    # metric with `value_type` `DOUBLE`.
    #
    # If, by "rate", you mean "percentage change", see the
    # `ALIGN_PERCENT_CHANGE` aligner instead.
    ALIGN_RATE = 2

    # Align by interpolating between adjacent points around the alignment
    # period boundary. This aligner is valid for `GAUGE` metrics with
    # numeric values. The `value_type` of the aligned result is the same as the
    # `value_type` of the input.
    ALIGN_INTERPOLATE = 3

    # Align by moving the most recent data point before the end of the
    # alignment period to the boundary at the end of the alignment
    # period. This aligner is valid for `GAUGE` metrics. The `value_type` of
    # the aligned result is the same as the `value_type` of the input.
    ALIGN_NEXT_OLDER = 4

    # Align the time series by returning the minimum value in each alignment
    # period. This aligner is valid for `GAUGE` and `DELTA` metrics with
    # numeric values. The `value_type` of the aligned result is the same as
    # the `value_type` of the input.
    ALIGN_MIN = 10

    # Align the time series by returning the maximum value in each alignment
    # period. This aligner is valid for `GAUGE` and `DELTA` metrics with
    # numeric values. The `value_type` of the aligned result is the same as
    # the `value_type` of the input.
    ALIGN_MAX = 11

    # Align the time series by returning the mean value in each alignment
    # period. This aligner is valid for `GAUGE` and `DELTA` metrics with
    # numeric values. The `value_type` of the aligned result is `DOUBLE`.
    ALIGN_MEAN = 12

    # Align the time series by returning the number of values in each alignment
    # period. This aligner is valid for `GAUGE` and `DELTA` metrics with
    # numeric or Boolean values. The `value_type` of the aligned result is
    # `INT64`.
    ALIGN_COUNT = 13

    # Align the time series by returning the sum of the values in each
    # alignment period. This aligner is valid for `GAUGE` and `DELTA`
    # metrics with numeric and distribution values. The `value_type` of the
    # aligned result is the same as the `value_type` of the input.
    ALIGN_SUM = 14

    # Align the time series by returning the standard deviation of the values
    # in each alignment period. This aligner is valid for `GAUGE` and
    # `DELTA` metrics with numeric values. The `value_type` of the output is
    # `DOUBLE`.
    ALIGN_STDDEV = 15

    # Align the time series by returning the number of `True` values in
    # each alignment period. This aligner is valid for `GAUGE` metrics with
    # Boolean values. The `value_type` of the output is `INT64`.
    ALIGN_COUNT_TRUE = 16

    # Align the time series by returning the number of `False` values in
    # each alignment period. This aligner is valid for `GAUGE` metrics with
    # Boolean values. The `value_type` of the output is `INT64`.
    ALIGN_COUNT_FALSE = 24

    # Align the time series by returning the ratio of the number of `True`
    # values to the total number of values in each alignment period. This
    # aligner is valid for `GAUGE` metrics with Boolean values. The output
    # value is in the range [0.0, 1.0] and has `value_type` `DOUBLE`.
    ALIGN_FRACTION_TRUE = 17

    # Align the time series by using [percentile
    # aggregation](https://en.wikipedia.org/wiki/Percentile). The resulting
    # data point in each alignment period is the 99th percentile of all data
    # points in the period. This aligner is valid for `GAUGE` and `DELTA`
    # metrics with distribution values. The output is a `GAUGE` metric with
    # `value_type` `DOUBLE`.
    ALIGN_PERCENTILE_99 = 18

    # Align the time series by using [percentile
    # aggregation](https://en.wikipedia.org/wiki/Percentile). The resulting
    # data point in each alignment period is the 95th percentile of all data
    # points in the period. This aligner is valid for `GAUGE` and `DELTA`
    # metrics with distribution values. The output is a `GAUGE` metric with
    # `value_type` `DOUBLE`.
    ALIGN_PERCENTILE_95 = 19

    # Align the time series by using [percentile
    # aggregation](https://en.wikipedia.org/wiki/Percentile). The resulting
    # data point in each alignment period is the 50th percentile of all data
    # points in the period. This aligner is valid for `GAUGE` and `DELTA`
    # metrics with distribution values. The output is a `GAUGE` metric with
    # `value_type` `DOUBLE`.
    ALIGN_PERCENTILE_50 = 20

    # Align the time series by using [percentile
    # aggregation](https://en.wikipedia.org/wiki/Percentile). The resulting
    # data point in each alignment period is the 5th percentile of all data
    # points in the period. This aligner is valid for `GAUGE` and `DELTA`
    # metrics with distribution values. The output is a `GAUGE` metric with
    # `value_type` `DOUBLE`.
    ALIGN_PERCENTILE_05 = 21

    # Align and convert to a percentage change. This aligner is valid for
    # `GAUGE` and `DELTA` metrics with numeric values. This alignment returns
    # `((current - previous)/previous) * 100`, where the value of `previous` is
    # determined based on the `alignment_period`.
    #
    # If the values of `current` and `previous` are both 0, then the returned
    # value is 0. If only `previous` is 0, the returned value is infinity.
    #
    # A 10-minute moving mean is computed at each point of the alignment period
    # prior to the above calculation to smooth the metric and prevent false
    # positives from very short-lived spikes. The moving mean is only
    # applicable for data whose values are `>= 0`. Any values `< 0` are
    # treated as a missing datapoint, and are ignored. 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` `DOUBLE`.
    ALIGN_PERCENT_CHANGE = 23
  end

  # A Reducer operation describes how to aggregate data points from multiple
  # time series into a single time series, where the value of each data point
  # in the resulting series is a function of all the already aligned values in
  # the input time series.
  module Reducer
    # No cross-time series reduction. The output of the `Aligner` is
    # returned.
    REDUCE_NONE = 0

    # Reduce by computing the mean value across time series for each
    # alignment period. This reducer is valid for
    # [DELTA][google.api.MetricDescriptor.MetricKind.DELTA] and
    # [GAUGE][google.api.MetricDescriptor.MetricKind.GAUGE] metrics with
    # numeric or distribution values. The `value_type` of the output is
    # [DOUBLE][google.api.MetricDescriptor.ValueType.DOUBLE].
    REDUCE_MEAN = 1

    # Reduce by computing the minimum value 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 value 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 `DOUBLE`.
    REDUCE_STDDEV = 5

    # Reduce by computing the number 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 `INT64`.
    REDUCE_COUNT = 6

    # Reduce by computing the number 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 `INT64`.
    REDUCE_COUNT_TRUE = 7

    # Reduce by computing the number 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 `INT64`.
    REDUCE_COUNT_FALSE = 15

    # Reduce by computing the ratio of the number of `True`-valued data points
    # to the total number of data points for each alignment period. This
    # reducer is valid for `DELTA` and `GAUGE` metrics of Boolean `value_type`.
    # The output value is in the range [0.0, 1.0] and has `value_type`
    # `DOUBLE`.
    REDUCE_FRACTION_TRUE = 8

    # Reduce by computing the [99th
    # percentile](https://en.wikipedia.org/wiki/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 `DOUBLE`.
    REDUCE_PERCENTILE_99 = 9

    # Reduce by computing the [95th
    # percentile](https://en.wikipedia.org/wiki/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 `DOUBLE`.
    REDUCE_PERCENTILE_95 = 10

    # Reduce by computing the [50th
    # percentile](https://en.wikipedia.org/wiki/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 `DOUBLE`.
    REDUCE_PERCENTILE_50 = 11

    # Reduce by computing the [5th
    # percentile](https://en.wikipedia.org/wiki/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 `DOUBLE`.
    REDUCE_PERCENTILE_05 = 12
  end
end