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 ``alignment_period`` specifies a time interval, in seconds, that is used to divide the data in all the [time series][google.monitoring.v3.TimeSeries] into consistent blocks of time. This will be done before the per-series aligner can be applied to the data. The value must be at least 60 seconds. If a per-series aligner other than ``ALIGN_NONE`` is specified, this field is required or an error is returned. If no per-series aligner is specified, or the aligner ``ALIGN_NONE`` is specified, then this field is ignored. The maximum value of the ``alignment_period`` is 2 years, or 104 weeks.
An ``Aligner`` describes how to bring the data points in a single time series into temporal alignment. Except for ``ALIGN_NONE``, all alignments cause all the data points in an ``alignment_period`` to be mathematically grouped together, resulting in a single data point for each ``alignment_period`` with end timestamp at the end of the period. Not all alignment operations may be applied to all time series. The valid choices depend on the ``metric_kind`` and ``value_type`` of the original time series. Alignment can change the ``metric_kind`` or the ``value_type`` of the time series. Time series data must be aligned in order to perform cross-time series reduction. If ``cross_series_reducer`` is specified, then ``per_series_aligner`` must be specified and not equal to ``ALIGN_NONE`` and ``alignment_period`` must be specified; otherwise, an error is returned.
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 ``metric_kind`` and the ``value_type`` of the original time series. Reduction can yield a time series with a different ``metric_kind`` or ``value_type`` than the input time series. Time series data must first be aligned (see ``per_series_aligner``) in order to perform cross-time series reduction. If ``cross_series_reducer`` is specified, then ``per_series_aligner`` must be specified, and must not be ``ALIGN_NONE``. An ``alignment_period`` must also be specified; otherwise, an error is returned.
The set of fields to preserve when ``cross_series_reducer`` is specified. The ``group_by_fields`` determine how the time series are partitioned into subsets prior to applying the aggregation operation. Each subset contains time series that have the same value for each of the grouping fields. Each individual time series is a member of exactly one subset. The ``cross_series_reducer`` is applied to each subset of time series. It is not possible to reduce across different resource types, so this field implicitly contains ``resource.type``. Fields not specified in ``group_by_fields`` are aggregated away. If ``group_by_fields`` is not specified and all the time series have the same resource type, then the time series are aggregated into a single output time series. If ``cross_series_reducer`` is not defined, this field is ignored.
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.