Published Release Notes

Data flow runs retry connections that fail

Real-time and batch data flow runs now retry dataset connections that fail when the related service is temporarily unavailable, for example, when a connection to a Cassandra database times out. With the automatic retries, you no longer need to run data-heavy and CPU-intensive jobs multiple times and the maintenance of data flow runs diminishes significantly.

For more information about accessing event details, see Creating a real-time run for data flows and Creating a batch run for data flows.

Optimized performance of decision strategies

Strategies that are part of data flows are now automatically optimized to achieve the best performance. You can also choose the properties that a strategy outputs to further increase the efficiency without additional strain on your hardware. 

For more information, see Adding strategies to dataflows. 

Merged test-coverage report improves overview of application quality

You can now merge multiple application-level test coverage reports from built-on apps into a single report. This single test-coverage report provides an overview of your application's quality, and eliminates the need to manually collate data from multiple reports.

For more information, see Generating a merged coverage report.

Analyze decision components with simulation tests

Gain a better understanding of how strategy components influence decision outcomes by using decision funnel explanations. By running simulation tests with the new Decision funnel explanation purpose, you can now break down the results of a decision funnel into granular analyses to assess how certain components and expressions influence the overall outcome.

For more information, see Interpret the decision funnel with simulation tests.

Updates to creating and cleaning the test data for automated tests

Creating test data for automated tests is now easier and more customizable. During test case or suite creation, you can now create objects, load work and data objects, and add user pages from the clipboard. These objects and pages can be further configured and will be available in the clipboard when you run the test.

For test cases, you can now define a separate set of actions to perform after the tested rule is run, such as applying a data transform, loading a data page or object, or running an activity,

You can also prevent test data from being removed from the clipboard after a test is run so that subsequent tests can use the same test data.

For more information, see Setting up your test environment and Cleaning up your test environment.

Support for machine learning as a service models

In Pega Platform™, you can now run advanced machine learning and artificial intelligence models that you develop in third-party tools. By configuring a connection with an external machine learning as a service provider, such as Google AI Platform, you can use the predictive power of your custom models to improve the predictions in your customer strategies.

For more information, see Connecting to a Machine Learning as a Service model and Configuring a machine learning service connection.

Test coverage support for more rule types

Test coverage has been expanded to include the following rule types. Test coverage support for these rule types enables developers to more accurately measure the effectiveness of their tests.

• Collection
• Declare trigger
• Map value
• Navigation 
• Report definition
• Scorecard

For more information about test coverage, see Test coverage.

Upgrade impact

The new rule types may impact the test coverage metrics for your applications. Due to the increased number of supported rules, the reported test coverage percentage will decrease.

What steps are required to update the application to be compatible with this change?

Run your coverage reports after upgrading to see the latest metrics.

Stream-based alternative for interaction history

Accelerate the processing of high-volume interaction history data by incorporating a stream-based interaction history into your application. A stream-based interaction history processes large volumes of data more quickly than a traditional relational database interaction history so that you can react to your customers' needs in real time.

For more information, see Process high-volume interactions more efficiently and Aggregates-only mode for a stream-based interaction history.

Aggregates-only mode for a stream-based interaction history

Activate the aggregates-only mode to efficiently transition your application from a relational database interaction history to a stream-based interaction history. By transitioning from a traditional relational database interaction history to a stream-based interaction history, you enable your application to process large volumes of data more quickly so that you can react to your customers' needs in real time.

For more information, see Transitioning to a stream-based interaction history.

Ensure event strategy reliability through unit tests

You can now automate event strategy testing and increase the reliability of the event strategy configuration through unit tests. Avoid errors and ensure that the strategy delivers the expected results by configuring assertions on property values, aggregates, and result counts.

For more information, see Increased event strategy reliability through unit testing.