Drill Into a Suspected Cause

Click More Details for the Suspected Cause to review:

  • Simplified timeline
  • Metrics graphed over time

Two types of graphed metrics display:

  • Top Deviating Metrics
  • Suspected Cause Metrics

Examine Top Deviating Metrics

In the Top deviating metrics graph, you can view the metric associated with the entity that is deviating from its normal range. Deviating metrics can indicate why an anomaly was important enough to surface. (The system does not surface anomalies for every transitory or slight deviation in metrics. Such anomalies would be of dubious value, since their customer impact is minimal. For the same reason, anomalies are surfaced for Business Transactions which have a CPM of under 20.)

You can:

  • Scroll along the graph to compare a metric's value with its Expected Range at any time point.
  • Hover over a time point to view the metric's value and Expected Range in numerical form.

In this example, the Average response time of the business transaction /web-api/getDepositSummary is 3487 milliseconds, which is above its expected range for the selected time point.

Examine Suspected Cause Metrics

Click Suspected root causes to view the suspected causes of the issue. You can view up to three suspected causes. In this example, there is only one suspected cause, which is a front end issue on customer-services-Node--2. Click More Details to view the details of the root causes. AppDynamics SaaS ranks the suspected causes as 1, 2, or, 3. The first suspected cause is displayed as Rank 1 and so on. You can view the AI-generated summary of the suspected cause, which helps you to understand the underlying cause of the issue.

Note:

Top deviating metrics describe the Business Transaction or the Database, while suspected cause metrics describe tiers or nodes further down in the entity tree. This means that if you have ART as a top deviating metric and as a suspected cause metric, those are two different metrics. Likewise, EPM as a top deviating metric and as a suspected cause metric are also two different metrics. While the suspected cause metric likely contributes to the way the top deviating metric behaves, the values of the two metrics will differ.

You can view the graph of the suspected cause metric of the tiers or nodes. For example, the value of Average response time of customer-services-Node--2 is 6008.00 milliseconds, which is above the expected range. This is the root cause of the anomaly.