Published Release Notes

EAR support for JBoss EAP 6

PRPC deployment in JBoss EAP 6 as an EAR archive is now supported. 

If you need to deploy the JBoss EAR file, go to My Support Portal and submit a Support Request. GCS can assist you with the procedure.

Small distribution kit

A new “small” release distribution format for Maintenance Levels (ML) is now available, designed to significantly increase adoption via: 

• Minimal package size and faster download time
• Minimal install time via tactical changes to the upgrade implementation and process

This kit contains streamlined installers (both UI and command-line installers) and specialized error handling for ML updates (for example, a “small” kit will only work if an existing version of the same release is already installed). 

The kit assembly has also been automated, which includes the construction of a PRPC_Rules.jar archive containing cumulative rules since the GA release. 

A “small” kit will work for any prior ML release within a major release (for example, the “small” ML5 distribution kit will update any customer on 7.1 ML1 through 7.1 ML4).

Improved data page performance

Improvements have been made with queueing and processing of asynchronously-loaded data pages to reduce the number of calls to the database and avoid generating performance problems because of unneeded asynchronous requests.

Predictive models monitoring

In Prediction Studio, you can now monitor the predictive performance of your models to validate that they make accurate predictions. Based on that information, you can re-create or adjust the models to provide better business results, such as higher accept rates or decreased customer churn.

For more information, see Monitoring predictive models.

Kafka custom serializer

In Kafka data sets, you can now create and receive messages in your custom formats, as well as in the default JSON format. To use custom logic and formats for serializing and deserializing ClipboardPage objects, create and implement a Java class. When you create a Kafka data set, you can choose to apply JSON or your custom format that uses a PegaSerde implementation.

For more information, see Creating a Kafka data set and Kafka custom serializer/deserialized implementation.

Additional configuration options for File data sets

You can now create File data sets for more advanced scenarios by adding custom Java classes for data encryption and decryption, and by defining a file set in a manifest file.

Additionally, you can improve data management by viewing detailed information in the dedicated meta file for every file that is saved, or by automatically extending the filenames with the creation date and time.

For more information, see Creating a File data set for files on repositories and Requirements for custom stream processing in File data sets.

Simplified testing of event strategies

Evaluate event strategies by creating test runs. During each run, you can enter a number of sample events with simulated property values, such as the event time, the event key, and so on. By testing a strategy against sample data, you can understand the strategy configuration better and troubleshoot potential issues.

For more information, see Evaluate event strategies through test runs.

Data flow life cycle monitoring

You can now generate a report from the Run details section of a Data Flow rule that provides information about run events. The report includes reasons for specific events which you can analyze to troubleshoot and debug issues more quickly. You can export the report and share it with others, such as Global Customer Support.

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

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.