Reference documentation and code samples for the Stackdriver Monitoring V3 Client class 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.
Generated from protobuf message google.monitoring.v3.Aggregation
Methods
__construct
Constructor.
| Parameters | |
|---|---|
| Name | Description |
data |
array
Optional. Data for populating the Message object. |
↳ alignment_period |
Google\Protobuf\Duration
The |
↳ per_series_aligner |
int
An |
↳ cross_series_reducer |
int
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 |
↳ group_by_fields |
array
The set of fields to preserve when |
getAlignmentPeriod
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 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.
Generated from protobuf field .google.protobuf.Duration alignment_period = 1;
| Returns | |
|---|---|
| Type | Description |
Google\Protobuf\Duration|null | |
hasAlignmentPeriod
clearAlignmentPeriod
setAlignmentPeriod
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 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.
Generated from protobuf field .google.protobuf.Duration alignment_period = 1;
| Parameter | |
|---|---|
| Name | Description |
var |
Google\Protobuf\Duration
|
| Returns | |
|---|---|
| Type | Description |
$this | |
getPerSeriesAligner
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.
Generated from protobuf field .google.monitoring.v3.Aggregation.Aligner per_series_aligner = 2;
| Returns | |
|---|---|
| Type | Description |
int | |
setPerSeriesAligner
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.
Generated from protobuf field .google.monitoring.v3.Aggregation.Aligner per_series_aligner = 2;
| Parameter | |
|---|---|
| Name | Description |
var |
int
|
| Returns | |
|---|---|
| Type | Description |
$this | |
getCrossSeriesReducer
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.
Generated from protobuf field .google.monitoring.v3.Aggregation.Reducer cross_series_reducer = 4;
| Returns | |
|---|---|
| Type | Description |
int | |
setCrossSeriesReducer
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.
Generated from protobuf field .google.monitoring.v3.Aggregation.Reducer cross_series_reducer = 4;
| Parameter | |
|---|---|
| Name | Description |
var |
int
|
| Returns | |
|---|---|
| Type | Description |
$this | |
getGroupByFields
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.
Generated from protobuf field repeated string group_by_fields = 5;
| Returns | |
|---|---|
| Type | Description |
Google\Protobuf\Internal\RepeatedField | |
setGroupByFields
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.
Generated from protobuf field repeated string group_by_fields = 5;
| Parameter | |
|---|---|
| Name | Description |
var |
string[]
|
| Returns | |
|---|---|
| Type | Description |
$this | |