Aggregation(mapping=None, *, ignore_unknown_fields=False, **kwargs)
Describes how to combine multiple time series to provide a different view of the data. Aggregation of time series is done in two steps. First, each time series in the set is aligned to the same time interval boundaries, then the set of time series is optionally reduced in number.
Alignment consists of applying the
to each time series after its data has been divided into regular
alignment_period time intervals. This process takes all of the
data points in an alignment period, applies a mathematical
transformation such as averaging, minimum, maximum, delta, etc., and
converts them into a single data point per period.
Reduction is when the aligned and transformed time series can
optionally be combined, reducing the number of time series through
similar mathematical transformations. Reduction involves applying a
cross_series_reducer to all the time series, optionally sorting
the time series into subsets with
group_by_fields, and applying
the reducer to each subset.
The raw time series data can contain a huge amount of information
from multiple sources. Alignment and reduction transforms this mass
of data into a more manageable and representative collection of
data, for example "the 95% latency across the average of all tasks
in a cluster". This representative data can be more easily graphed
and comprehended, and the individual time series data is still
available for later drilldown. For more details, see
The reduction operation to be used to combine 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. Not all reducer operations can be applied to all time series. The valid choices depend on the
The set of fields to preserve when
Inheritancebuiltins.object > proto.message.Message > Aggregation
Aligner specifies the operation that will be applied to the
data points in each alignment period in a time series. Except for
ALIGN_NONE, which specifies that no operation be applied, each
alignment operation replaces the set of data values in each
alignment period with a single value: the result of applying the
operation to the data values. An aligned time series has a single
data value at the end of each
An alignment operation can change the data type of the values, too.
For example, if you apply a counting operation to boolean values,
value_type in the original time series is
value_type in the aligned result is
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.