VMware Aria Operations for Applications (formerly known as Tanzu Observability by Wavefront) provides observability for several different metric types including time series metrics, delta counters, histograms, and traces/spans. This page looks at the anatomy of a time series metric and shows you how to explore it in the Metrics Browser.
The following videos get you started:
|Browsing Your Data
90-second video that shows how you can find and examine metrics from the Sources browser and from the Metrics browser.
You can also watch the video here .
Wavefront co-founder Clement Pang explains why the concept of cardinality is so important for observability, what high cardinality means, and why we deal so well with high cardinality input. You can also watch the video here .
You can also watch the following videos to learn more about working with metrics:
Time Series Metric Structure
A time series has, at a minimum, the metric name, value/timestamp, and source. In many cases, the metric is ingested with additional information represented as tags.
Simple Time Series
Here’s one example that shows the minimum elements of a time series.
Each time series is a unique combination of:
- Metric name–Describes the metric. There’s often a hierarchy of metrics, each with a corresponding time series.
- Value & Timestamp–Value at the specified time.
- Source–The source of the metric. Host, VM, etc. In contrast to some other observability platforms, this dimension is always part of the metric.
Here’s a screenshot of the time series that is shown in the diagram above in a chart.
Time Series with Tags
In most cases, the time series includes one or more tags to allow a more fine-grained analysis. The
~sample data you can find on each service instance include point tags for environment and availability zone.
Point tags offer a powerful way of labeling data so that you can slice and dice it in almost any way you can imagine. For example, you can use point tags, to label a point’s datacenter, version, etc. and can then group by datacenter or version.
You use point tags to add extra dimensions to your data, and can then focus your exploration just on that dimension.Fine Tune Queries with Point Tags explains how to use point tags.
Here’s a screenshot of the time series that includes point tags in a chart.
How Filtering with Tags Improves Usability
How the point tag filters are useful becomes obvious when the
source= filter is removed. The result of all time series for
~sample.disk.space.used is visually confusing.
When you add filters for
az, the information makes sense.
If a metric stops sending data points for a certain period of time (obsolescence period), it becomes obsolete.
In the Metrics browser and Query Editor, obsolete metrics are no longer shown in the autocomplete drop-down lists.
Select Browse > Metrics to display the Metrics Browser. Use the Metrics Browser to find metrics that are actively sending data points.
To make search easier, you can
- Drill down and go up the hierarchy.
- Filter by source.
- Hide and redisplay metrics or groups of metrics to unclutter your page.
To examine metrics
Hide and Redisplay Metrics
While obsolete metrics are automatically hidden, you can manually hide metrics from the Metrics browser. Manually hiding metrics does not permanently delete a metric or metric namespace.
To hide one or more metrics:
To view hidden metrics:
- Optimizing the Data Shape to Improve Performance
- See the KB article Migrating Objects or Data Between Operations for Applications Environments if your company has several service instances.