Application errors are often hard to retrieve, or take a lot of time to resolve. When you are suffering from errors, and have a lack of clarity when errors happen, you would like to have useful error diagnostics for analysis.

The ADF Performance Monitor automatically captures detailed diagnostics for each and every error/exception occurrence. You can view your errors to see the highest priority issues your team should focus on. This blog shows the renewed error overview of our newest version of the ADF Performance Monitor – with real production metrics.

Overview of Production Errors

On the overview dashboard, you can click on the errors to open the errors overview:

In this error report, you can see a complete overview of all the errors and its details including: Java Exception class, exception message, timestamp, user ID, browser, click action, component type, event type (action, fetch, query, autosubmit, e.g.), time spent in layer, managed server, e.g.:

In the chart on the top you can see all occurring errors – in this case JboExceptions, SQLExceptions, NullPointerExceptions, SQLSyntaxExceptions, RowAlreadyDeletedExceptions, ClassCastExceptions, NumberFormatExceptions, SQLIntegrityConstraintViolationExceptions, TxnValExceptions, e.g::

Error in ADF Callstack

The ADF request callstack can be viewed. It shows the exact place in the ADF callstack where the error happened. This to understand more of the error‘s root cause. We can see that in this case just before the error occurred, a bindingContainer operation was called that seems to be the cause: initVoClientLancement:

Error Stacktraces

Developers can drill down to these errors, their type, severity and messages. The stacktrace can be viewed to analyse the root cause of the error – in this case a NullPointerException

Filter on Managed Server

You can filter on managed server, by selecting the specific server in the dropdown list (top-right):

Filter on End-User

You can filter on end-user. For example if an end-user contacts you and complains on errors he experiences – you can track the complete request and error history to troubleshoot this user. For example filter on user Klaas:

Export to Excel or CSV file

All error metrics can be exported to an Excel or CSV file for later analysis, to manage errors:

Conclusion

It saves a lot of time to have a quick way to get error metrics and error related information – to troubleshoot production errors.