Enum Aggregation.Reducer (2.16.0)

Version 2.16.0

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public enum Aggregation.Reducer extends Enum<Aggregation.Reducer> implements ProtocolMessageEnum

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.

Protobuf enum google.monitoring.dashboard.v1.Aggregation.Reducer

Implements

ProtocolMessageEnum

Inherited Members

Enum.<T>valueOf(Class<T>,String)

Enum.clone()

Enum.compareTo(E)

Enum.equals(Object)

Enum.finalize()

Enum.getDeclaringClass()

Enum.hashCode()

Enum.name()

Enum.ordinal()

Enum.toString()

Object.getClass()

Object.notify()

Object.notifyAll()

Object.wait()

Object.wait(long)

Object.wait(long,int)

Static Fields
Name	Description
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. REDUCE_COUNT = 6;
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. REDUCE_COUNT_FALSE = 15;
REDUCE_COUNT_FALSE_VALUE	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_COUNT_FALSE = 15;
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. REDUCE_COUNT_TRUE = 7;
REDUCE_COUNT_TRUE_VALUE	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_TRUE = 7;
REDUCE_COUNT_VALUE	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 = 6;
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. REDUCE_FRACTION_TRUE = 8;
REDUCE_FRACTION_TRUE_VALUE	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_FRACTION_TRUE = 8;
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. REDUCE_MAX = 3;
REDUCE_MAX_VALUE	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_MAX = 3;
REDUCE_MEAN	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_MEAN = 1;
REDUCE_MEAN_VALUE	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_MEAN = 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. REDUCE_MIN = 2;
REDUCE_MIN_VALUE	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_MIN = 2;
REDUCE_NONE	No cross-time series reduction. The output of the Aligner is returned. REDUCE_NONE = 0;
REDUCE_NONE_VALUE	No cross-time series reduction. The output of the Aligner is returned. REDUCE_NONE = 0;
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. REDUCE_PERCENTILE_05 = 12;
REDUCE_PERCENTILE_05_VALUE	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. REDUCE_PERCENTILE_05 = 12;
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. REDUCE_PERCENTILE_50 = 11;
REDUCE_PERCENTILE_50_VALUE	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_50 = 11;
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. REDUCE_PERCENTILE_95 = 10;
REDUCE_PERCENTILE_95_VALUE	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_95 = 10;
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. REDUCE_PERCENTILE_99 = 9;
REDUCE_PERCENTILE_99_VALUE	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_99 = 9;
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. REDUCE_STDDEV = 5;
REDUCE_STDDEV_VALUE	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_STDDEV = 5;
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. REDUCE_SUM = 4;
REDUCE_SUM_VALUE	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_SUM = 4;
UNRECOGNIZED

Static Methods
Name	Description
forNumber(int value)
getDescriptor()
internalGetValueMap()
valueOf(Descriptors.EnumValueDescriptor desc)
valueOf(int value)	Deprecated. Use #forNumber(int) instead.
valueOf(String name)
values()

Methods
Name	Description
getDescriptorForType()
getNumber()
getValueDescriptor()