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View and investigate anomalies

View Apigee Edge documentation.

This page describes how to view and investigate detected anomalies using the operations anomalies dashboard. You can investigate the anomaly and take appropriate action if necessary. You can also create an anomaly alert to notify you if similar events occur in the future.

A detected anomaly includes the following information:

• The metric that caused the anomaly, such as proxy latency or an HTTP error code.
• The severity of the anomaly. The severity can be slight, moderate, or severe, based on its confidence level in the model. A low confidence level indicates that the severity is slight, while a high confidence level indicates that it is severe.

View anomalies

The operations anomalies dashboard in the Apigee UI is your primary source of information about detected operations anomalies. The dashboard displays a list of recent anomalies.

To open the operations anomalies dashboard:

1. In the Google Cloud console, go to the Analytics > Operations anomalies page.

   Go to Operations anomalies

2. Switch to the organization that you want to monitor.

This displays the operations anomalies dashboard.

By default, the dashboard shows anomalies that have occurred during the previous hour. If no anomalies have been detected during that time period, no rows are displayed in the dashboard. You can select a larger time range from the time range menu in the top right of the dashboard.

Each row in the table corresponds to a detected anomaly, and displays the following information:

• The date and time of the anomaly.
• A brief summary of the anomaly, including the proxy in which it occurred.
• The environment in which the anomaly occurred.
• The region where the anomaly occurred.
• The severity of the anomaly event: slight, moderate, or severe. Severity is based on a statistical measure (p-value) of how unlikely it would be for the event to occur by chance (the more unlikely the event, the greater its severity).

Investigate anomalies

When you see an anomaly in the operations anomalies dashboard, you can investigate it further in the API Monitoring dashboards. Click Investigate in the Summary column for the anomaly to open the API Monitoring Investigate dashboard.

Example: Investigate a fault code anomaly

Suppose you are looking at the operations anomalies dashboard and you notice the anomaly shown below:

After clicking Investigate in the Summary column, the API Monitoring Investigate dashboard, is shown below.

The Anomaly Event Details pane displays an error rate timeline. The graph shows that the anomaly occurred after 07:00 AM, when the error rate jumped from less than 0.4 to more than 0.8.

The error rate in the timeline graph includes errors for all fault codes. To see a breakdown of errors for different fault codes, look at the Fault Code by Time graph displayed below the timeline.

Note: If the Fault Code by Time graph isn't currently displayed, select Fault Code in the Graphs menu to show it:

The circled column of the Fault Code by Time graph corresponds to the time interval containing the time of the anomaly.

Note: A small difference between the data displayed in the graph and the reported time of the anomaly is normal.

You observe that in the interval 07:03 - 07:27, there were 1499 responses with fault code steps.json2xml.SourceUnavailable (an error code that is returned when a JSON to XML policy message source is unavailable). This is the fault code that triggered the anomaly. By contrast, over the preceding four intervals the average number of responses with that fault code was about 291, so the jump to 1499 was certainly an unusual event.

For more information about the SourceUnavailable error message, see JSON to XML policy runtime error troubleshooting.

At this point, there are a couple of ways to continue investigating the cause of the anomaly:

• Drill down on the fault code data at the time of the anomaly by clicking the cell for the anomaly in the Fault Code by Time graph.

  

  This displays distribution tables for steps.json2xml.SourceUnavailable by fault source, proxy, and status code in the right-hand pane.

  In this example, the tables don't provide any additional information because all the fault codes arise from the same fault source, proxy, and status code. But in other situations, the distribution tables can point you to the location and cause of the anomaly.

• Create an alert for the anomaly and set up a notification. After you have done this, operations anomalies will send you a message whenever a similar event occurs in the future.