@JeevanthaPerera

⚠ This is a GenAI-powered tool. All generated answers require validation against the provided references.

Best Approach: Custom Time Tracking Implementation

The most direct and reliable method to capture and display REST connector response time is to implement a custom time tracking solution using DateTime properties. Here's a step-by-step implementation:

1. Create Properties in Your Data Model:
   • pyStartTime (DateTime) - To store when the REST call begins
   • pyEndTime (DateTime) - To store when the REST call completes
   • pyElapsedTime (Decimal) - To store the calculated elapsed time in seconds
2. Capture Start Time Before REST Call:
   • Immediately before making the REST connector call, set the start time:
3. .pyStartTime = @currentDateTime
4. Make the REST Connector Call:
   • Execute your REST connector as normal
5. Capture End Time After REST Call:
   • Immediately after the REST call completes, set the end time:
6. .pyEndTime = @currentDateTime
7. Calculate Elapsed Time:
   • Calculate the difference and convert to seconds:
8. .pyElapsedTime = (.pyEndTime - .pyStartTime) / 1000
9. Display in the Application:
   • Add the pyElapsedTime property to your user interface where you want to display the elapsed time
   • You can format it as needed, for example: @round(.pyElapsedTime, 2) + " seconds"

Implementation Considerations

• Wrapper Activity: Consider creating a wrapper activity that encapsulates the REST connector call along with the time tracking logic. This promotes reusability and consistency.
• Error Handling: Ensure your implementation includes proper error handling to capture end time even if the REST call fails.
• Logging: For debugging and performance analysis, you might want to log the elapsed times to identify slow-performing REST calls:
• log.info("REST Call to " + .EndpointName + " completed in " + .pyElapsedTime + " seconds");
• Persistence: If you need to analyze the response times over time, consider persisting these values to a database table.

Alternative Approaches

While the custom implementation above is the most direct approach, Pega does offer performance monitoring tools:

1. Performance Analyzer (PAL): Provides insights into system resources consumed during requestor sessions, including REST connector calls. However, PAL is primarily a diagnostic tool and doesn't provide a straightforward way to display elapsed time in your application UI.
2. Pega Predictive Diagnostic Cloud (PDC): Helps monitor various system metrics, including integration response times, but is more suited for overall application monitoring rather than displaying specific elapsed times in the UI.

References:

• Tracking utilization statistics with Performance Analyzer
• Monitoring your application
• Data storage in Pega