Watchdog Insights for RUM

Watchdog Insights for RUM

Overview

Datadog Real User Monitoring (RUM) offers Watchdog Insights to help you navigate to the root cause of problems with contextual insights in the RUM Explorer. Watchdog Insights complement your expertise and instincts by recommending outliers and potential performance bottlenecks impacting a subset of users.

Watchdog Insights for RUM is in beta. Access to this feature is provisioned to customers using Real User Monitoring. If you have feedback, contact Datadog support.

In this example, Watchdog Insights identifies that the deployed application instance on view.url_host:www.shopist.io caused most of the errors in a given time range (for example, the past day).

The Watchdog Insights banner appears in the RUM Explorer results page and displays insights about the current query:

To see an overview of all insights, expand the Watchdog Insight banner:

To access the full Watchdog Insights panel, click View all:

Every insight comes with embedded interactions and a side panel with troubleshooting information. The insight interactions and side panel vary based on the Watchdog Insights type.

Collections

Error outliers

One type of insight includes error outliers that display fields such as faceted tags or attributes containing characteristics of errors that match the current query. Statistically overrepresented key:value pairs among errors provide hints into the root cause of problems.

Typical examples of error outliers include env:staging, version:1234, and browser.name:Chrome.

In the banner card and side panel card view, you can see:

  • The field name.
  • The proportion of errors and overall RUM events that the field contributes to.

In the full side panel, you can see a timeseries of RUM errors with the field.

Latency outliers

Another type of insight includes latency outliers that display fields such as faceted tags or attributes) associated with performance bottlenecks and match the current query. key:value pairs with worse performance than the baseline provide hints into performance bottlenecks among a subset of real users.

Latency outliers are computed for Core Web Vitals such as First Contentful Paint, First Input Delay, and Cumulative Layout Shift, and Loading Time.

In the banner card view, you can see:

  • The field name.
  • The performance metric value containing the field and the baseline for the rest of the data.

In the side panel card view, you can see a timeseries of the performance metric for the field and baseline for the rest of the data.

In the full side panel view, you can see a list of RUM events that contain the field. Look for the root cause of the performance issue in the performance waterfall.

Further Reading