Module: Google::Cloud::Monitoring::Dashboard::V1::Aggregation::Reducer

Defined in:
proto_docs/google/monitoring/dashboard/v1/common.rb

Overview

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.

Constant Summary collapse

REDUCE_NONE =

No cross-time series reduction. The output of the Aligner is returned.

0
REDUCE_MEAN =

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].

1
REDUCE_MIN =

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.

2
REDUCE_MAX =

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.

3
REDUCE_SUM =

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.

4
REDUCE_STDDEV =

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.

5
REDUCE_COUNT =

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.

6
REDUCE_COUNT_TRUE =

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.

7
REDUCE_COUNT_FALSE =

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.

15
REDUCE_FRACTION_TRUE =

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.

8
REDUCE_PERCENTILE_99 =

Reduce by computing the 99th 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.

9
REDUCE_PERCENTILE_95 =

Reduce by computing the 95th 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.

10
REDUCE_PERCENTILE_50 =

Reduce by computing the 50th 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.

11
REDUCE_PERCENTILE_05 =

Reduce by computing the 5th 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.

12