Distribution represents a frequency distribution of double-valued sample points. It contains the size of the population of sample points plus additional optional information: - the arithmetic mean of the samples - the minimum and maximum of the samples - the sum-squared-deviation of the samples, used to compute variance - a histogram of the values of the sample points

Attributes

• bucketCounts (type: list(String.t), default: nil) - The number of samples in each histogram bucket. bucket_counts are optional. If present, they must sum to the count value. The buckets are defined below in bucket_option. There are N buckets. bucket_counts[0] is the number of samples in the underflow bucket. bucket_counts[1] to bucket_counts[N-1] are the numbers of samples in each of the finite buckets. And bucket_counts[N] is the number of samples in the overflow bucket. See the comments ofbucket_optionbelow for more details. Any suffix of trailing zeros may be omitted. *count(*type:*String.t, *default:*nil) - The total number of samples in the distribution. Must be >= 0. *exemplars(*type:*list(GoogleApi.ServiceControl.V1.Model.Exemplar.t), *default:*nil) - Example points. Must be in increasing order ofvaluefield. *explicitBuckets(*type:*GoogleApi.ServiceControl.V1.Model.ExplicitBuckets.t, *default:*nil) - Buckets with arbitrary user-provided width. *exponentialBuckets(*type:*GoogleApi.ServiceControl.V1.Model.ExponentialBuckets.t, *default:*nil) - Buckets with exponentially growing width. *linearBuckets(*type:*GoogleApi.ServiceControl.V1.Model.LinearBuckets.t, *default:*nil) - Buckets with constant width. *maximum(*type:*float(), *default:*nil) - The maximum of the population of values. Ignored ifcountis zero. *mean(*type:*float(), *default:*nil) - The arithmetic mean of the samples in the distribution. Ifcountis zero then this field must be zero. *minimum(*type:*float(), *default:*nil) - The minimum of the population of values. Ignored ifcountis zero. *sumOfSquaredDeviation(*type:*float(), *default:*nil) - The sum of squared deviations from the mean: Sum[i=1..count]((x_i - mean)^2) where each x_i is a sample values. Ifcount` is zero then this field must be zero, otherwise validation of the request fails.