Event grouping in Pega Predictive Diagnostic Cloud cases
Pega Predictive Diagnostic Cloud™ (PDC) gathers alert messages, exceptions, and data that the PegaAESRemote ruleset provides, and then organizes them into cases. To better understand how that data helps you track and monitor problems in your system, learn how PDC creates cases.
Events are data points that PDC receives from the systems that it monitors. The most common events are alerts and exceptions, for example, an alert that Pega Platform™ generates every time when an HTTP interaction lasts longer than the threshold setting.
These events gather a wide range of data, for example, session descriptions, stack frame lists, clipboard parameter page data, and the Performance Analysis (PAL) counters of the session at the time of the event. When PDC receives this data, it stores the records in its database. For more information, see Data collected by Pega Predictive Diagnostic Cloud.
Every five minutes, the PDC agent evaluates all events in your application since the last run, and then groups together the events that resulted from the same problem. Such a grouping by problem type is a correlation. PDC uses correlations to create and update cases that group events from the same correlation. For example, to correlate a slow query alert with an appropriate case, PDC identifies the rule that defined the query, and the query that ran slowly. To correlate exceptions that originate from the same part of your application, PDC determines the class where each exception occurred, and then parses the exception messages.
If PDC does not find any case from a specific correlation, it creates a new case with a type that identifies the problem, and then updates the case when additional events from the same correlation occur.
For more information about each case type, see Cases in Pega Predictive Diagnostic Cloud.