private/google/monitoring/dashboard/v1/common.proto
// 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
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
syntax = "proto3";
package google.monitoring.dashboard.v1;
import "google/api/distribution.proto";
import "google/protobuf/duration.proto";
option go_package = "google.golang.org/genproto/googleapis/monitoring/dashboard/v1;dashboard";
option java_multiple_files = true;
option java_outer_classname = "CommonProto";
option java_package = "com.google.monitoring.dashboard.v1";
option ruby_package = "Google::Cloud::Monitoring::Dashboard::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).
message Aggregation {
// 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`.
enum 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;
}
// 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.
enum 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;
}
// 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.
google.protobuf.Duration alignment_period = 1;
// 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.
Aligner per_series_aligner = 2;
// 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.
Reducer cross_series_reducer = 4;
// 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.
repeated string group_by_fields = 5;
}
// 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.
message PickTimeSeriesFilter {
// The value reducers that can be applied to a `PickTimeSeriesFilter`.
enum 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;
}
// Describes the ranking directions.
enum 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;
}
// `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.
Method ranking_method = 1;
// How many time series to allow to pass through the filter.
int32 num_time_series = 2;
// How to use the ranking to select time series that pass through the filter.
Direction direction = 3;
}
// 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.
message StatisticalTimeSeriesFilter {
// The filter methods that can be applied to a stream.
enum Method {
// Not allowed in well-formed requests.
METHOD_UNSPECIFIED = 0;
// Compute the outlier score of each stream.
METHOD_CLUSTER_OUTLIER = 1;
}
// `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.
Method ranking_method = 1;
// How many time series to output.
int32 num_time_series = 2;
}