Reducer(value)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.
Values:
REDUCE_NONE (0):
No cross-time series reduction. The output of the
Aligner is returned.
REDUCE_MEAN (1):
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].
REDUCE_MIN (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 (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 (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 (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 (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 (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 (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 (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 (9):
Reduce by computing the 99th
percentile <https://en.wikipedia.org/wiki/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 by computing the 95th
percentile <https://en.wikipedia.org/wiki/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 by computing the 50th
percentile <https://en.wikipedia.org/wiki/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 by computing the 5th
percentile <https://en.wikipedia.org/wiki/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.