A condition type that compares a collection of time series against a threshold. .. attribute:: filter
A filter
<https://cloud.google.com/monitoring/api/v3/filters> that
identifies which time series should be compared with the
threshold. The filter is similar to the one that is specified
in the `ListTimeSeries request <https://cloud.google.com/m
onitoring/api/ref_v3/rest/v3/projects.timeSeries/list>`
(that call is useful to verify the time series that will be
retrieved / processed) and must specify the metric type and
optionally may contain restrictions on resource type, resource
labels, and metric labels. This field may not exceed 2048
Unicode characters in length.
A filter
<https://cloud.google.com/monitoring/api/v3/filters>__ that
identifies a time series that should be used as the
denominator of a ratio that will be compared with the
threshold. If a denominator_filter is specified, the time
series specified by the filter field will be used as the
numerator. The filter must specify the metric type and
optionally may contain restrictions on resource type, resource
labels, and metric labels. This field may not exceed 2048
Unicode characters in length.
The comparison to apply between the time series (indicated by
filter and aggregation) and the threshold (indicated
by threshold_value). The comparison is applied on each
time series, with the time series on the left-hand side and
the threshold on the right-hand side. Only COMPARISON_LT
and COMPARISON_GT are supported currently.
The amount of time that a time series must violate the
threshold to be considered failing. Currently, only values
that are a multiple of a minute--e.g., 0, 60, 120, or 300
seconds--are supported. If an invalid value is given, an error
will be returned. When choosing a duration, it is useful to
keep in mind the frequency of the underlying time series data
(which may also be affected by any alignments specified in the
aggregations field); a good duration is long enough so
that a single outlier does not generate spurious alerts, but
short enough that unhealthy states are detected and alerted on
quickly.