codekingpro/portable-devtools

1// Copyright 2026 Google LLC2//3// Licensed under the Apache License, Version 2.0 (the "License");4// you may not use this file except in compliance with the License.5// You may obtain a copy of the License at6//7//     http://www.apache.org/licenses/LICENSE-2.08//9// Unless required by applicable law or agreed to in writing, software10// distributed under the License is distributed on an "AS IS" BASIS,11// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12// See the License for the specific language governing permissions and13// limitations under the License.14 15syntax = "proto3";16 17package google.api;18 19import "google/protobuf/any.proto";20import "google/protobuf/timestamp.proto";21 22option go_package = "google.golang.org/genproto/googleapis/api/distribution;distribution";23option java_multiple_files = true;24option java_outer_classname = "DistributionProto";25option java_package = "com.google.api";26option objc_class_prefix = "GAPI";27 28// `Distribution` contains summary statistics for a population of values. It29// optionally contains a histogram representing the distribution of those values30// across a set of buckets.31//32// The summary statistics are the count, mean, sum of the squared deviation from33// the mean, the minimum, and the maximum of the set of population of values.34// The histogram is based on a sequence of buckets and gives a count of values35// that fall into each bucket. The boundaries of the buckets are given either36// explicitly or by formulas for buckets of fixed or exponentially increasing37// widths.38//39// Although it is not forbidden, it is generally a bad idea to include40// non-finite values (infinities or NaNs) in the population of values, as this41// will render the `mean` and `sum_of_squared_deviation` fields meaningless.42message Distribution {43  // The range of the population values.44  message Range {45    // The minimum of the population values.46    double min = 1;47 48    // The maximum of the population values.49    double max = 2;50  }51 52  // `BucketOptions` describes the bucket boundaries used to create a histogram53  // for the distribution. The buckets can be in a linear sequence, an54  // exponential sequence, or each bucket can be specified explicitly.55  // `BucketOptions` does not include the number of values in each bucket.56  //57  // A bucket has an inclusive lower bound and exclusive upper bound for the58  // values that are counted for that bucket. The upper bound of a bucket must59  // be strictly greater than the lower bound. The sequence of N buckets for a60  // distribution consists of an underflow bucket (number 0), zero or more61  // finite buckets (number 1 through N - 2) and an overflow bucket (number N -62  // 1). The buckets are contiguous: the lower bound of bucket i (i > 0) is the63  // same as the upper bound of bucket i - 1. The buckets span the whole range64  // of finite values: lower bound of the underflow bucket is -infinity and the65  // upper bound of the overflow bucket is +infinity. The finite buckets are66  // so-called because both bounds are finite.67  message BucketOptions {68    // Specifies a linear sequence of buckets that all have the same width69    // (except overflow and underflow). Each bucket represents a constant70    // absolute uncertainty on the specific value in the bucket.71    //72    // There are `num_finite_buckets + 2` (= N) buckets. Bucket `i` has the73    // following boundaries:74    //75    //    Upper bound (0 <= i < N-1):     offset + (width * i).76    //77    //    Lower bound (1 <= i < N):       offset + (width * (i - 1)).78    message Linear {79      // Must be greater than 0.80      int32 num_finite_buckets = 1;81 82      // Must be greater than 0.83      double width = 2;84 85      // Lower bound of the first bucket.86      double offset = 3;87    }88 89    // Specifies an exponential sequence of buckets that have a width that is90    // proportional to the value of the lower bound. Each bucket represents a91    // constant relative uncertainty on a specific value in the bucket.92    //93    // There are `num_finite_buckets + 2` (= N) buckets. Bucket `i` has the94    // following boundaries:95    //96    //    Upper bound (0 <= i < N-1):     scale * (growth_factor ^ i).97    //98    //    Lower bound (1 <= i < N):       scale * (growth_factor ^ (i - 1)).99    message Exponential {100      // Must be greater than 0.101      int32 num_finite_buckets = 1;102 103      // Must be greater than 1.104      double growth_factor = 2;105 106      // Must be greater than 0.107      double scale = 3;108    }109 110    // Specifies a set of buckets with arbitrary widths.111    //112    // There are `size(bounds) + 1` (= N) buckets. Bucket `i` has the following113    // boundaries:114    //115    //    Upper bound (0 <= i < N-1):     bounds[i]116    //    Lower bound (1 <= i < N);       bounds[i - 1]117    //118    // The `bounds` field must contain at least one element. If `bounds` has119    // only one element, then there are no finite buckets, and that single120    // element is the common boundary of the overflow and underflow buckets.121    message Explicit {122      // The values must be monotonically increasing.123      repeated double bounds = 1;124    }125 126    // Exactly one of these three fields must be set.127    oneof options {128      // The linear bucket.129      Linear linear_buckets = 1;130 131      // The exponential buckets.132      Exponential exponential_buckets = 2;133 134      // The explicit buckets.135      Explicit explicit_buckets = 3;136    }137  }138 139  // Exemplars are example points that may be used to annotate aggregated140  // distribution values. They are metadata that gives information about a141  // particular value added to a Distribution bucket, such as a trace ID that142  // was active when a value was added. They may contain further information,143  // such as a example values and timestamps, origin, etc.144  message Exemplar {145    // Value of the exemplar point. This value determines to which bucket the146    // exemplar belongs.147    double value = 1;148 149    // The observation (sampling) time of the above value.150    google.protobuf.Timestamp timestamp = 2;151 152    // Contextual information about the example value. Examples are:153    //154    //   Trace: type.googleapis.com/google.monitoring.v3.SpanContext155    //156    //   Literal string: type.googleapis.com/google.protobuf.StringValue157    //158    //   Labels dropped during aggregation:159    //     type.googleapis.com/google.monitoring.v3.DroppedLabels160    //161    // There may be only a single attachment of any given message type in a162    // single exemplar, and this is enforced by the system.163    repeated google.protobuf.Any attachments = 3;164  }165 166  // The number of values in the population. Must be non-negative. This value167  // must equal the sum of the values in `bucket_counts` if a histogram is168  // provided.169  int64 count = 1;170 171  // The arithmetic mean of the values in the population. If `count` is zero172  // then this field must be zero.173  double mean = 2;174 175  // The sum of squared deviations from the mean of the values in the176  // population. For values x_i this is:177  //178  //     Sum[i=1..n]((x_i - mean)^2)179  //180  // Knuth, "The Art of Computer Programming", Vol. 2, page 232, 3rd edition181  // describes Welford's method for accumulating this sum in one pass.182  //183  // If `count` is zero then this field must be zero.184  double sum_of_squared_deviation = 3;185 186  // If specified, contains the range of the population values. The field187  // must not be present if the `count` is zero.188  Range range = 4;189 190  // Defines the histogram bucket boundaries. If the distribution does not191  // contain a histogram, then omit this field.192  BucketOptions bucket_options = 6;193 194  // The number of values in each bucket of the histogram, as described in195  // `bucket_options`. If the distribution does not have a histogram, then omit196  // this field. If there is a histogram, then the sum of the values in197  // `bucket_counts` must equal the value in the `count` field of the198  // distribution.199  //200  // If present, `bucket_counts` should contain N values, where N is the number201  // of buckets specified in `bucket_options`. If you supply fewer than N202  // values, the remaining values are assumed to be 0.203  //204  // The order of the values in `bucket_counts` follows the bucket numbering205  // schemes described for the three bucket types. The first value must be the206  // count for the underflow bucket (number 0). The next N-2 values are the207  // counts for the finite buckets (number 1 through N-2). The N'th value in208  // `bucket_counts` is the count for the overflow bucket (number N-1).209  repeated int64 bucket_counts = 7;210 211  // Must be in increasing order of `value` field.212  repeated Exemplar exemplars = 10;213}214