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// Copyright 2019 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.api;
import "google/protobuf/any.proto";
import "google/protobuf/timestamp.proto";
option go_package = "google.golang.org/genproto/googleapis/api/distribution;distribution";
option java_multiple_files = true;
option java_outer_classname = "DistributionProto";
option java_package = "com.google.api";
option objc_class_prefix = "GAPI";
// `Distribution` contains summary statistics for a population of values. It
// optionally contains a histogram representing the distribution of those values
// across a set of buckets.
//
// The summary statistics are the count, mean, sum of the squared deviation from
// the mean, the minimum, and the maximum of the set of population of values.
// The histogram is based on a sequence of buckets and gives a count of values
// that fall into each bucket. The boundaries of the buckets are given either
// explicitly or by formulas for buckets of fixed or exponentially increasing
// widths.
//
// Although it is not forbidden, it is generally a bad idea to include
// non-finite values (infinities or NaNs) in the population of values, as this
// will render the `mean` and `sum_of_squared_deviation` fields meaningless.
message Distribution {
// The range of the population values.
message Range {
// The minimum of the population values.
double min = 1;
// The maximum of the population values.
double max = 2;
}
// `BucketOptions` describes the bucket boundaries used to create a histogram
// for the distribution. The buckets can be in a linear sequence, an
// exponential sequence, or each bucket can be specified explicitly.
// `BucketOptions` does not include the number of values in each bucket.
//
// A bucket has an inclusive lower bound and exclusive upper bound for the
// values that are counted for that bucket. The upper bound of a bucket must
// be strictly greater than the lower bound. The sequence of N buckets for a
// distribution consists of an underflow bucket (number 0), zero or more
// finite buckets (number 1 through N - 2) and an overflow bucket (number N -
// 1). The buckets are contiguous: the lower bound of bucket i (i > 0) is the
// same as the upper bound of bucket i - 1. The buckets span the whole range
// of finite values: lower bound of the underflow bucket is -infinity and the
// upper bound of the overflow bucket is +infinity. The finite buckets are
// so-called because both bounds are finite.
message BucketOptions {
// Specifies a linear sequence of buckets that all have the same width
// (except overflow and underflow). Each bucket represents a constant
// absolute uncertainty on the specific value in the bucket.
//
// There are `num_finite_buckets + 2` (= N) buckets. Bucket `i` has the
// following boundaries:
//
// Upper bound (0 <= i < N-1): offset + (width * i).
// Lower bound (1 <= i < N): offset + (width * (i - 1)).
message Linear {
// Must be greater than 0.
int32 num_finite_buckets = 1;
// Must be greater than 0.
double width = 2;
// Lower bound of the first bucket.
double offset = 3;
}
// Specifies an exponential sequence of buckets that have a width that is
// proportional to the value of the lower bound. Each bucket represents a
// constant relative uncertainty on a specific value in the bucket.
//
// There are `num_finite_buckets + 2` (= N) buckets. Bucket `i` has the
// following boundaries:
//
// Upper bound (0 <= i < N-1): scale * (growth_factor ^ i).
// Lower bound (1 <= i < N): scale * (growth_factor ^ (i - 1)).
message Exponential {
// Must be greater than 0.
int32 num_finite_buckets = 1;
// Must be greater than 1.
double growth_factor = 2;
// Must be greater than 0.
double scale = 3;
}
// Specifies a set of buckets with arbitrary widths.
//
// There are `size(bounds) + 1` (= N) buckets. Bucket `i` has the following
// boundaries:
//
// Upper bound (0 <= i < N-1): bounds[i]
// Lower bound (1 <= i < N); bounds[i - 1]
//
// The `bounds` field must contain at least one element. If `bounds` has
// only one element, then there are no finite buckets, and that single
// element is the common boundary of the overflow and underflow buckets.
message Explicit {
// The values must be monotonically increasing.
repeated double bounds = 1;
}
// Exactly one of these three fields must be set.
oneof options {
// The linear bucket.
Linear linear_buckets = 1;
// The exponential buckets.
Exponential exponential_buckets = 2;
// The explicit buckets.
Explicit explicit_buckets = 3;
}
}
// Exemplars are example points that may be used to annotate aggregated
// distribution values. They are metadata that gives information about a
// particular value added to a Distribution bucket, such as a trace ID that
// was active when a value was added. They may contain further information,
// such as a example values and timestamps, origin, etc.
message Exemplar {
// Value of the exemplar point. This value determines to which bucket the
// exemplar belongs.
double value = 1;
// The observation (sampling) time of the above value.
google.protobuf.Timestamp timestamp = 2;
// Contextual information about the example value. Examples are:
//
// Trace: type.googleapis.com/google.monitoring.v3.SpanContext
//
// Literal string: type.googleapis.com/google.protobuf.StringValue
//
// Labels dropped during aggregation:
// type.googleapis.com/google.monitoring.v3.DroppedLabels
//
// There may be only a single attachment of any given message type in a
// single exemplar, and this is enforced by the system.
repeated google.protobuf.Any attachments = 3;
}
// The number of values in the population. Must be non-negative. This value
// must equal the sum of the values in `bucket_counts` if a histogram is
// provided.
int64 count = 1;
// The arithmetic mean of the values in the population. If `count` is zero
// then this field must be zero.
double mean = 2;
// The sum of squared deviations from the mean of the values in the
// population. For values x_i this is:
//
// Sum[i=1..n]((x_i - mean)^2)
//
// Knuth, "The Art of Computer Programming", Vol. 2, page 323, 3rd edition
// describes Welford's method for accumulating this sum in one pass.
//
// If `count` is zero then this field must be zero.
double sum_of_squared_deviation = 3;
// If specified, contains the range of the population values. The field
// must not be present if the `count` is zero.
Range range = 4;
// Defines the histogram bucket boundaries. If the distribution does not
// contain a histogram, then omit this field.
BucketOptions bucket_options = 6;
// The number of values in each bucket of the histogram, as described in
// `bucket_options`. If the distribution does not have a histogram, then omit
// this field. If there is a histogram, then the sum of the values in
// `bucket_counts` must equal the value in the `count` field of the
// distribution.
//
// If present, `bucket_counts` should contain N values, where N is the number
// of buckets specified in `bucket_options`. If you supply fewer than N
// values, the remaining values are assumed to be 0.
//
// The order of the values in `bucket_counts` follows the bucket numbering
// schemes described for the three bucket types. The first value must be the
// count for the underflow bucket (number 0). The next N-2 values are the
// counts for the finite buckets (number 1 through N-2). The N'th value in
// `bucket_counts` is the count for the overflow bucket (number N-1).
repeated int64 bucket_counts = 7;
// Must be in increasing order of `value` field.
repeated Exemplar exemplars = 10;
}
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