Reference documentation and code samples for the Cloud Monitoring V3 API class Google::Cloud::Monitoring::V3::Aggregation.
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.
Inherits
- Object
Extended By
- Google::Protobuf::MessageExts::ClassMethods
Includes
- Google::Protobuf::MessageExts
Methods
#alignment_period
def alignment_period() -> ::Google::Protobuf::Duration
-
(::Google::Protobuf::Duration) — The
alignment_period
specifies a time interval, in seconds, that is used to divide the data in all the time series 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 alignerALIGN_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.
#alignment_period=
def alignment_period=(value) -> ::Google::Protobuf::Duration
-
value (::Google::Protobuf::Duration) — The
alignment_period
specifies a time interval, in seconds, that is used to divide the data in all the time series 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 alignerALIGN_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.
-
(::Google::Protobuf::Duration) — The
alignment_period
specifies a time interval, in seconds, that is used to divide the data in all the time series 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 alignerALIGN_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.
#cross_series_reducer
def cross_series_reducer() -> ::Google::Cloud::Monitoring::V3::Aggregation::Reducer
-
(::Google::Cloud::Monitoring::V3::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 thevalue_type
of the original time series. Reduction can yield a time series with a differentmetric_kind
orvalue_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. Ifcross_series_reducer
is specified, thenper_series_aligner
must be specified, and must not beALIGN_NONE
. Analignment_period
must also be specified; otherwise, an error is returned.
#cross_series_reducer=
def cross_series_reducer=(value) -> ::Google::Cloud::Monitoring::V3::Aggregation::Reducer
-
value (::Google::Cloud::Monitoring::V3::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 thevalue_type
of the original time series. Reduction can yield a time series with a differentmetric_kind
orvalue_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. Ifcross_series_reducer
is specified, thenper_series_aligner
must be specified, and must not beALIGN_NONE
. Analignment_period
must also be specified; otherwise, an error is returned.
-
(::Google::Cloud::Monitoring::V3::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 thevalue_type
of the original time series. Reduction can yield a time series with a differentmetric_kind
orvalue_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. Ifcross_series_reducer
is specified, thenper_series_aligner
must be specified, and must not beALIGN_NONE
. Analignment_period
must also be specified; otherwise, an error is returned.
#group_by_fields
def group_by_fields() -> ::Array<::String>
-
(::Array<::String>) — The set of fields to preserve when
cross_series_reducer
is specified. Thegroup_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. Thecross_series_reducer
is applied to each subset of time series. It is not possible to reduce across different resource types, so this field implicitly containsresource.type
. Fields not specified ingroup_by_fields
are aggregated away. Ifgroup_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. Ifcross_series_reducer
is not defined, this field is ignored.
#group_by_fields=
def group_by_fields=(value) -> ::Array<::String>
-
value (::Array<::String>) — The set of fields to preserve when
cross_series_reducer
is specified. Thegroup_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. Thecross_series_reducer
is applied to each subset of time series. It is not possible to reduce across different resource types, so this field implicitly containsresource.type
. Fields not specified ingroup_by_fields
are aggregated away. Ifgroup_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. Ifcross_series_reducer
is not defined, this field is ignored.
-
(::Array<::String>) — The set of fields to preserve when
cross_series_reducer
is specified. Thegroup_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. Thecross_series_reducer
is applied to each subset of time series. It is not possible to reduce across different resource types, so this field implicitly containsresource.type
. Fields not specified ingroup_by_fields
are aggregated away. Ifgroup_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. Ifcross_series_reducer
is not defined, this field is ignored.
#per_series_aligner
def per_series_aligner() -> ::Google::Cloud::Monitoring::V3::Aggregation::Aligner
-
(::Google::Cloud::Monitoring::V3::Aggregation::Aligner) — An
Aligner
describes how to bring the data points in a single time series into temporal alignment. Except forALIGN_NONE
, all alignments cause all the data points in analignment_period
to be mathematically grouped together, resulting in a single data point for eachalignment_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
andvalue_type
of the original time series. Alignment can change themetric_kind
or thevalue_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, thenper_series_aligner
must be specified and not equal toALIGN_NONE
andalignment_period
must be specified; otherwise, an error is returned.
#per_series_aligner=
def per_series_aligner=(value) -> ::Google::Cloud::Monitoring::V3::Aggregation::Aligner
-
value (::Google::Cloud::Monitoring::V3::Aggregation::Aligner) — An
Aligner
describes how to bring the data points in a single time series into temporal alignment. Except forALIGN_NONE
, all alignments cause all the data points in analignment_period
to be mathematically grouped together, resulting in a single data point for eachalignment_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
andvalue_type
of the original time series. Alignment can change themetric_kind
or thevalue_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, thenper_series_aligner
must be specified and not equal toALIGN_NONE
andalignment_period
must be specified; otherwise, an error is returned.
-
(::Google::Cloud::Monitoring::V3::Aggregation::Aligner) — An
Aligner
describes how to bring the data points in a single time series into temporal alignment. Except forALIGN_NONE
, all alignments cause all the data points in analignment_period
to be mathematically grouped together, resulting in a single data point for eachalignment_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
andvalue_type
of the original time series. Alignment can change themetric_kind
or thevalue_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, thenper_series_aligner
must be specified and not equal toALIGN_NONE
andalignment_period
must be specified; otherwise, an error is returned.