When you read metric data using the Metrics Explorer or the Monitoring API, you can use aggregation to summarize the time series data. Aggregation typically starts with an alignment step in which each time series' data is placed on the same time boundaries. Next, a new time series is created by combining the data points from multiple time series, using operations like average, sum, minimum, maximum, and so forth.
For more information, see the projects.timeSeries.list API method.
Cloud Monitoring has a large number of built-in system metrics, but you can also create your own custom metrics. You must create a metric descriptor that describes your custom metric and one or more time series that contain its data. You can use your custom metric data in charts and alerts. You can also create custom metrics based on logs data. For more information, see the following pages:
- Metrics, Time Series, and Resources provides an overview.
- Using Custom Metrics shows you how to create, write, and read custom metrics.
- Logs-based Metrics describes how to create custom metrics from logs.
- Monitoring API, is the API used to for
custom metrics. See the
Stackdriver provides different kinds of filters that let you select sets of items. Filters are strings that contain combinations of comparisons appropriate to their kind. For example:
- Logging filters let you select particular log entries based on log names, where the logs came from, their payload content, and so on.
- Monitoring filters let you select particular metric descriptors, resource groups, and time series data.
- Trace filters let you select traces based on span names, latency, and label values.
A Workspace is used in Cloud Monitoring to monitor the resources you care about, whether they are in a GCP project, an AWS account, or multiple projects and AWS accounts. Monitoring organizes itself around Workspaces rather than around GCP projects, as other GCP services do. For more information, see Workspaces.