Parameters that control how we construct the time series for each slice.
| JSON representation |
|---|
{
"forecastHistory"
:
string
,
"granularity"
:
string
,
"metricAggregationMethod"
:
enum (
|
forecastHistory
string (
Duration
format)
Required. How long should we go in the past when fetching the timeline used for forecasting each slice.
This is used in combination with the detectionTime
parameter. The time series we construct will have the following time range: [detectionTime - forecastHistory, detectionTime + granularity]
.
The forecast history might be rounded up, so that a multiple of granularity
is used to process the query.
Note: If there are not enough events in the [detectionTime - forecastHistory, detectionTime + granularity]
time interval, the slice evaluation can fail. For more information, see EvaluatedSlice.status
.
A duration in seconds with up to nine fractional digits, ending with ' s
'. Example: "3.5s"
.
granularity
string (
Duration
format)
Required. The time granularity of the time series (on the x-axis). Each time series point starting at time T will aggregate all events for a particular slice in [T, T + granularity) time windows.
Note: The aggregation is decided based on the metric
parameter.
This granularity defines the query-time aggregation windows and is not necessarily related to any event time granularity in the raw data (though we do recommend that the query-time granularity is not finer than the ingestion-time one).
Currently, the minimal supported granularity is 10 seconds.
A duration in seconds with up to nine fractional digits, ending with ' s
'. Example: "3.5s"
.
metricAggregationMethod
enum (
AggregationMethod
)
Optional. Together with the metric
field, specifies how we will aggregate multiple events to obtain the value of a time series point. See the metric
documentation for more details.
If the metric is not specified or "", then this field will be ignored.
metric
string
Optional. Denotes the name
of a numerical dimension that will have its values aggregated to compute the y-axis of the time series.
The aggregation method must also be specified by setting the metricAggregationMethod
field.
Note: Currently, if the aggregation method is unspecified, we will default to SUM for backward compatibility reasons, but new implementations should set the metricAggregationMethod
explicitly.
If the metric is unspecified, we will use the number of events that each time series point contains as the point value.
Example: Let's assume we have the following three events in our dataset:
{
eventTime: "2020-12-27T00:00:00Z",
dimensions: [
{ name: "d1" stringVal: "v1" },
{ name: "d2" stringVal: "v2" }
{ name: "m1" longVal: 100 }
{ name: "m2" longVal: 11 }
]
},
{
eventTime: "2020-12-27T00:10:00Z",
dimensions: [
{ name: "d1" stringVal: "v1" },
{ name: "d2" stringVal: "v2" }
{ name: "m1" longVal: 200 }
{ name: "m2" longVal: 22 }
]
},
{
eventTime: "2020-12-27T00:20:00Z",
dimensions: [
{ name: "d1" stringVal: "v1" },
{ name: "d2" stringVal: "v2" }
{ name: "m1" longVal: 300 }
{ name: "m2" longVal: 33 }
]
}
These events are all within the same hour, spaced 10 minutes between each of them. Assuming our QueryDataSetRequest
had set slicingParams.dimensionNames
to ["d1", "d2"] and timeseriesParams.granularity
to "3600s", then all the previous events will be aggregated into the same timeseries point
.
The time series point that they're all part of will have the time
set to "2020-12-27T00:00:00Z" and the value
populated based on this metric field:
- If the metric is set to "m1" and metricAggregationMethod to SUM, then the value of the point will be 600.
- If the metric is set to "m2" and metricAggregationMethod to SUM, then the value of the point will be 66.
- If the metric is set to "m1" and metricAggregationMethod to AVERAGE, then the value of the point will be 200.
- If the metric is set to "m2" and metricAggregationMethod to AVERAGE, then the value of the point will be 22.
- If the metric field is "" or unspecified, then the value of the point will be 3, as we will simply count the events.
AggregationMethod
Methods by which we can aggregate multiple events by a given metric
.
| Enums | |
|---|---|
AGGREGATION_METHOD_UNSPECIFIED
|
Unspecified. |
SUM
|
Aggregate multiple events by summing up the values found in the metric
dimension. |
AVERAGE
|
Aggregate multiple events by averaging out the values found in the metric
dimension. |
