Published Release Notes

Additional connection pool parameters for JDBC URL external databases

For improved external JDBC URL database connection performance, Pega Platform™ now supports multiple Hikari connection pool parameters directly in the Data-Admin-DB-Name rule. Previously, Pega Platform supported a limited number of connection pool parameters to optimize your database connection using dynamic system settings (DSS). Now you can optimize your JDBC URL connection pool sizing to meet your traffic requirements through additional parameters without switching rules. Additional parameters include client-request parameters to maximize the connectivity to your external databases.

For more information, see Creating and updating external database instances with JDBC URLs.

Upgrade impact

During an upgrade to Pega Infinity™ release 8.6 and later, clients who previously specified their JDBC URL connection parameters through dynamic system settings (DSS) or prconfig file must delete the corresponding DSS or prconfig elements immediately after upgrade. The old parameters that you previously specified through DSS or the prconfig file override the equivalent parameters that you specified in the JDBC URL rule after your upgrade.

For more information about deleting your previous DSS or prconfig elements, see Deleting dynamic system setting connection parameters and Deleting connection parameters in the prconfig file.

Kafka data set enhancements

The Kafka data set is a high-throughput and low-latency platform for handling real-time data feeds that you can use as input for Pega Platform event strategies.

For better integration of Pega Platform with externally hosted Kafka, the following enhancements are implemented:

• Support for Kafka message keys and headers - extended values data format (JSON Data Transform, Apache Avro)
• Custom value processing
• Configuring topic names by using Application Properties
• Data-Admin-Kafka enhancements - supporting a wide range of connection properties

For more information, see Improve your Kafka data set with new enhancements.

Default text analytics model for detecting small talk

Enhance your web chat experience by configuring chatbots to detect small talk. Pega Platform™ provides the default model that is a topic detection model, available for English, German, Spanish, Dutch, Portuguese, Italian, and French.

For more information, see Configure your chatbot for small talk.

Clustering equal to none might cause errors

Some Pega 7 Platform features might not work when theidentification/cluster/protocolsetting in the prconfig.xml file is set to none. Some features that might not work are remote tracing and management actions on the node from the Requestor Management and Agent Management landing pages. Setting the clustering protocol to none is not recommended and will not be supported in a future Pega 7 Platform release.

Java injection vulnerability check

Pega Platform™ now notifies you of Java injection vulnerabilities in activities, functions, and stream rules at design time and at run time.  You can customize Pega Platform to check for additional vulnerabilities to ensure that your application runs without problems.

For more information, see Configuring the Java injection check.

Export and import operations for data sets

You can move data between data sets and Pega 7 Platform instances more easily with export and import operations. You can use the export operation to prepare a backup copy of data outside the Pega 7 Platform. The import operation facilitates initialization of new Pega 7 Platform environments by providing a prepared set of data. You can export data from the data sets that support the Browse operation, and you can import data into the data sets that support the Save operation. Export and Import operations are not available for stream data sets such as Facebook, Stream, Twitter, and YouTube.

For more information, see Exporting data from a data set and Importing data into a data set.

Create connections to repositories

Pega^® Platform can communicate with common repository technologies. Whenever an action creates a RAP, Pega Platform can browse, publish, or fetch artifacts: for example, when exporting an application, product, branch, or component. Repositories are instances of the Data-Repository class which holds all necessary connection information.

Pega Platform includes tools to connect with the following repository types:

• JFrog Artifactory
• Amazon S3

For more information, see Creating a JFrog Artifactory or Amazon S3 repository connection.

Support for encrypted traffic in a cluster

The Pega 7 Platform includes the Ignite platform, which supports encryption for intra-cluster communications. You can now configure encryption for intra-cluster traffic for compliance with regulatory or organizational security requirements.

For more information, see Enabling encrypted traffic between nodes.

Automated Unit Testing (AUT) content removed from the help

Information about Automated Unit Testing (AUT) has been removed from the Pega 7 Platform help, beginning with version 7.2.2. The AUT information is still available in earlier versions of the Pega 7 Platform help and on the PDN in the topic category Testing Applications.

PegaUnit testing is now supported on data pages, activities, data transforms, strategies, decision tables, and decision trees, which you can use to quickly and easily create test cases for your Pega 7 Platform applications instead of using AUT.

For more information, see:

• Pega 7.2.2 behavior when switching between PegaUnit testing and Automated Unit Testing features
• Automated Unit Testing

Text analytics models editing and versioning

Pega Platform™ now supports editing and updating training data for text analytics models.

Pega Platform also supports the versioning of text analytics models. When you update the model, Prediction Studio creates an updated model version. You can then switch between the model versions.

Upgrade impact

In versions of Pega Platform earlier than 8.3, the training data for text models was stored in the database. In Pega Platform version 8.3 and later, the training data for text models is stored in Pega Repository. You cannot build new models without setting the repository. After the repository is set, all text models are automatically upgraded and will work normally.

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

After a successful upgrade, set the repository in Prediction Studio before building or updating any Natural Language Processing (NLP) models.  In Prediction Studio, click Settings > Text Model Data Repository.

For more information, see:

• Increase the accuracy of text analytics models by adding feedback data (8.3)
• Updating training data for text analytics models