Reference documentation and code samples for the Cloud Monitoring V3 API module Google::Cloud::Monitoring::V3::Aggregation::Reducer.
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.
Constants
REDUCE_NONE
value: 0
No cross-time series reduction. The output of the Aligner is
returned.
REDUCE_MEAN
value: 1
Reduce by computing the mean value 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.
REDUCE_MIN
value: 2
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.
REDUCE_MAX
value: 3
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.
REDUCE_SUM
value: 4
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.
REDUCE_STDDEV
value: 5
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.
REDUCE_COUNT
value: 6
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.
REDUCE_COUNT_TRUE
value: 7
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.
REDUCE_COUNT_FALSE
value: 15
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.
REDUCE_FRACTION_TRUE
value: 8
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.
REDUCE_PERCENTILE_99
value: 9
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.
REDUCE_PERCENTILE_95
value: 10
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.
REDUCE_PERCENTILE_50
value: 11
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.
REDUCE_PERCENTILE_05
value: 12
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.