Published Release Notes

Additional upgrade scripts for new columns

In Version 6.2, additional columns (Major, Minor, and Patch) containing ruleset Version information were added to 15 tables in the PRPC database. When upgrading from a pre-Pega 7 release, scripts must be manually run after the upgrade to populate these columns.

These scripts are located in the Resource Kit, under Additional Upgrade Scripts.

• If upgrading to a single schema, run both scripts.
• If upgrading to a split schema, run the _data script against the data schema and run the _rules script against the rules schema.

Choose the scripts for your database type:

• db2zos_rulesetversion_columns_data.sql
• db2zos_rulesetversion_columns_rules.sql

• mssql_rulesetversion_columns_data.sql
• mssql_rulesetversion_columns_rules.sql

• oracle_rulesetversion_columns_data.sql
• oracle_rulesetversion_columns_rules.sql

• postgres_rulesetversion_columns_data.sql
• postgres_rulesetversion_columns_rules.sql

• udb_rulesetversion_columns_data.sql
• udb_rulesetversion_columns_rules.sql

Database

Improvements to data handling were made for search indexing, agent processing, and filtering.  Scripts were provided for proper population of the database tables.

• Importing data from shared obj-external tables is now supported.
• A validation warning will now display when an unexposed property is used in filter or join conditions.
• A Global Search index may now be created across two databases.
• Agent processing on multiple nodes has been improved.
• Improvements were made to controls in Report Definition pages which are used in a dynamic grid.
• Filtering functionality involving special characters has been enhanced.
• Scripts have been provided to populate the RuleSetVersion Major, Minor, and Patch columns.

Decision Management

This release contains a number of fixes that improve the configuration and execution of Batch decisions – specifically, a number of usability improvements and addresses some issues when creating strategies on IE8.  

• Batch Decision functionality has been enhanced in the NBAM environment.
• In NBAM, users can run the strategy execution batch based on the Seed list class instead of the customer class. 
• Interaction data will now work even if Action or Organization  dimensions do not have values set.
• Running a strategy which includes a Data Import of a structured page reference will work in batch mode.
• Security was enhanced on the VBD Planner.
• The Access of Role to Object rules have been enhanced to allow users to create new Dimension Operator records.
• The NBAM Segment On Canvas will now fetch the SegMap data, even when the generated extended segment class has not been added to the Pages and Classes of the strategy.  The SegMap property will (if it exists) be added to a list of properties to fetch, even if the strategy indicates that it is not used.
• When a strategy is defined on a customer class, users may now specify the list of required properties to fetch for the input definition.
• When creating a new strategy, there is now a section to add a Strategy Results class like Business Issue or Group.

System Management

Refinements were made to out-of-place upgrade documentation and processes, as well as for how handling is performed for all of the database vendor specific nuances related to a deploying into a split schema environment.

• A script was created to run the data schema upgrade for z/OS systems.
• In a split-schema configuration, indexing has been enhanced.
• The product installer has been enhanced.
• The stand-alone Static Assembly Utility does not require a password for its process.
• The UpdateManager tool has been improved.

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.