Central instance to access all CloudMonitoring related resource activities
The response of cloudmonitoring.metricDescriptors.delete.
The request of cloudmonitoring.metricDescriptors.list.
The response of cloudmonitoring.metricDescriptors.list.
The request of cloudmonitoring.timeseriesDescriptors.list
The response of cloudmonitoring.timeseriesDescriptors.list
The request of cloudmonitoring.timeseries.list
The response of cloudmonitoring.timeseries.list
A metricDescriptor defines the name, label keys, and data type of a particular metric.
Create a new metric.
Delete an existing metric.
A label in a metric is a description of this metric, including the key of this description (what the description is), and the value for this description.
List metric descriptors that match the query. If the query is not set, then all of the metric descriptors will be returned. Large responses will be paginated, use the nextPageToken returned in the response to request subsequent pages of results by setting the pageToken query parameter to the value of the nextPageToken.
A builder providing access to all methods supported on
metricDescriptor resources.
It is not used directly, but through the
CloudMonitoring hub.
A type in a metric contains information about how the metric is collected and what its data points look like.
Point is a single point in a time series. It consists of a start time, an end time, and a value.
Distribution data point value type. When writing distribution points, try to be consistent with the boundaries of your buckets. If you must modify the bucket boundaries, then do so by merging, partitioning, or appending rather than skewing them.
The histogram’s bucket. Buckets that form the histogram of a distribution value. If the upper bound of a bucket, say U1, does not equal the lower bound of the next bucket, say L2, this means that there is no event in [U1, L2).
The overflow bucket is a special bucket that does not have the upperBound field; it includes all of the events that are no less than its lower bound.
The underflow bucket is a special bucket that does not have the lowerBound field; it includes all of the events that are less than its upper bound.
The monitoring data is organized as metrics and stored as data points that are recorded over time. Each data point represents information like the CPU utilization of your virtual machine. A historical record of these data points is called a time series.
TimeseriesDescriptor identifies a single time series.
List the descriptors of the time series that match the metric and labels values and that have data points in the interval. Large responses are paginated; use the nextPageToken returned in the response to request subsequent pages of results by setting the pageToken query parameter to the value of the nextPageToken.
A builder providing access to all methods supported on
timeseriesDescriptor resources.
It is not used directly, but through the
CloudMonitoring hub.
When writing time series, TimeseriesPoint should be used instead of Timeseries, to enforce single point for each time series in the timeseries.write request.
List the data points of the time series that match the metric and labels values and that have data points in the interval. Large responses are paginated; use the nextPageToken returned in the response to request subsequent pages of results by setting the pageToken query parameter to the value of the nextPageToken.
A builder providing access to all methods supported on
timesery resources.
It is not used directly, but through the
CloudMonitoring hub.
Put data points to one or more time series for one or more metrics. If a time series does not exist, a new time series will be created. It is not allowed to write a time series point that is older than the existing youngest point of that time series. Points that are older than the existing youngest point of that time series will be discarded silently. Therefore, users should make sure that points of a time series are written sequentially in the order of their end time.
The request of cloudmonitoring.timeseries.write
The response of cloudmonitoring.timeseries.write