# frozen_string_literal: true
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# Auto-generated by gapic-generator-ruby. DO NOT EDIT!
module Google
module Cloud
module Monitoring
module Dashboard
module V1
# 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](https://cloud.google.com/monitoring/api/v3/aggregation).
# @!attribute [rw] alignment_period
# @return [::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.
# @!attribute [rw] per_series_aligner
# @return [::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.
# @!attribute [rw] cross_series_reducer
# @return [::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.
# @!attribute [rw] group_by_fields
# @return [::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.
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
# Describes a ranking-based time series filter. Each input time series is
# ranked with an aligner. The filter will allow up to `num_time_series` time
# series to pass through it, selecting them based on the relative ranking.
#
# For example, if `ranking_method` is `METHOD_MEAN`,`direction` is `BOTTOM`,
# and `num_time_series` is 3, then the 3 times series with the lowest mean
# values will pass through the filter.
# @!attribute [rw] ranking_method
# @return [::Google::Cloud::Monitoring::Dashboard::V1::PickTimeSeriesFilter::Method]
# `ranking_method` is applied to each time series independently to produce
# the value which will be used to compare the time series to other time
# series.
# @!attribute [rw] num_time_series
# @return [::Integer]
# How many time series to allow to pass through the filter.
# @!attribute [rw] direction
# @return [::Google::Cloud::Monitoring::Dashboard::V1::PickTimeSeriesFilter::Direction]
# How to use the ranking to select time series that pass through the filter.
class PickTimeSeriesFilter
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
# The value reducers that can be applied to a `PickTimeSeriesFilter`.
module Method
# Not allowed. You must specify a different `Method` if you specify a
# `PickTimeSeriesFilter`.
METHOD_UNSPECIFIED = 0
# Select the mean of all values.
METHOD_MEAN = 1
# Select the maximum value.
METHOD_MAX = 2
# Select the minimum value.
METHOD_MIN = 3
# Compute the sum of all values.
METHOD_SUM = 4
# Select the most recent value.
METHOD_LATEST = 5
end
# Describes the ranking directions.
module Direction
# Not allowed. You must specify a different `Direction` if you specify a
# `PickTimeSeriesFilter`.
DIRECTION_UNSPECIFIED = 0
# Pass the highest `num_time_series` ranking inputs.
TOP = 1
# Pass the lowest `num_time_series` ranking inputs.
BOTTOM = 2
end
end
# A filter that ranks streams based on their statistical relation to other
# streams in a request.
# Note: This field is deprecated and completely ignored by the API.
# @!attribute [rw] ranking_method
# @return [::Google::Cloud::Monitoring::Dashboard::V1::StatisticalTimeSeriesFilter::Method]
# `rankingMethod` is applied to a set of time series, and then the produced
# value for each individual time series is used to compare a given time
# series to others.
# These are methods that cannot be applied stream-by-stream, but rather
# require the full context of a request to evaluate time series.
# @!attribute [rw] num_time_series
# @return [::Integer]
# How many time series to output.
class StatisticalTimeSeriesFilter
include ::Google::Protobuf::MessageExts
extend ::Google::Protobuf::MessageExts::ClassMethods
# The filter methods that can be applied to a stream.
module Method
# Not allowed in well-formed requests.
METHOD_UNSPECIFIED = 0
# Compute the outlier score of each stream.
METHOD_CLUSTER_OUTLIER = 1
end
end
end
end
end
end
end