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Code generated by protoc-gen-go. DO NOT EDIT. versions: protoc-gen-go v1.25.0 protoc v3.13.0 source: google/api/distribution.proto
`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.
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.
The arithmetic mean of the values in the population. If `count` is zero then this field must be zero.
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.
If specified, contains the range of the population values. The field must not be present if the `count` is zero.
Defines the histogram bucket boundaries. If the distribution does not contain a histogram, then omit this field.
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).
`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.
Exactly one of these three fields must be set. Types that are assignable to Options: *Distribution_BucketOptions_LinearBuckets *Distribution_BucketOptions_ExponentialBuckets *Distribution_BucketOptions_ExplicitBuckets
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.
Value of the exemplar point. This value determines to which bucket the exemplar belongs.
The observation (sampling) time of the above value.
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.
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)).
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)).
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.
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