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 per_series_aligner operation
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 Filtering and
aggregation <https://cloud.google.com/monitoring/api/v3/aggregation>__.
Attributes | |
|---|---|
| Name | Description |
alignment_period |
google.protobuf.duration_pb2.Duration
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 104 weeks
(2 years) for charts, and 90,000 seconds (25 hours) for
alerting policies.
|
per_series_aligner |
google.cloud.monitoring_v3.types.Aggregation.Aligner
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.
|
cross_series_reducer |
google.cloud.monitoring_v3.types.Aggregation.Reducer
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.
|
group_by_fields |
Sequence[str]
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.
|
Classes
Aligner
Aligner(value)The 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 alignment_period.
An alignment operation can change the data type of the values, too.
For example, if you apply a counting operation to boolean values,
the data value_type in the original time series is BOOLEAN,
but the value_type in the aligned result is INT64.
Reducer
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.