Published Release Notes

Support for custom database tables in external Cassandra clusters

Pega Platform™ now supports a connection to external Cassandra clusters through a dedicated Database Table data set, which reduces the need for data ingestion and export. You can use custom tables that you store in your external Cassandra cluster in data flows for accessing and saving data. You can access your custom data model by mapping the model to a Pega Platform class.

For more information, see Connecting to an external Cassandra database through a Database Table data set.

Data synchronization does not resume after mobile app restart

If an offline-enabled mobile app that you build with Pega Infinity Mobile Client™ is stopped during an initial data synchronization session, the data synchronization does not resume when the mobile app is restarted. Users must not stop the mobile app before initial data synchronization finishes.

For more information about data synchronization, see Offline capability and Guidelines for creating an offline-enabled application.

Enhanced data flow performance monitoring

The performance alert log now provides more detailed information about the data flows in your application. You can view performance details for data flows that are currently running and data flow runs that have completed successfully. You can also view a performance summary for data flow runs that are running for too long or have failed.

For more information, see:

• PEGA0082 alert: Dataflow started
• PEGA0083 alert: Dataflow complete
• PEGA0062 alert: Data flow execution time above threshold
• PEGA0072 alert: Data flow run failed

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

Ability to support advanced device features with Pega Infinity Mobile Client

Pega Infinity Mobile Client™ now provides a development kit that you can use to make mobile apps support advanced device features that are typically available to native applications, for example, an embedded laser scanner or a projector module, and integrate functions of external software solutions, for example, a cloud-based file storage.

For more information about enhancing mobile apps with device feature support, see the documentation for the development module in the latest Pega Infinity Mobile Client distribution package on Digital Delivery.

Text analytics models migration

Pega Platform™ now supports the exporting and importing of text analytics models. For example, you can export a model to a production system so that it can gather feedback data. You can then update the model with the collected feedback data to increase the model's accuracy.

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)
• Exporting text analytics models
• Importing text analytics models

Support for regular performance alerts

You can now specify how often Pega Platform™ sends you performance alerts about SLA violations in data flow runs. By default, the interval is 5 seconds for single case data flows and 5 minutes for batch and real-time data flows.

The following dynamic system settings control the alert interval:

• Single case runs: dataflow/singlecase/alert/throttleTime
• Batch runs: dataflow/batch/alert/throttleTime
• Real-time runs: dataflow/realtime/alert/throttleTime

For more information, see PEGA0062 alert: Data flow execution time above threshold.

Automatic model training when mapping entities to case properties in Email Bot

You can now initiate automatic feedback to entity models in Pega Email Bot™, during manual mapping of email content to a case property.

To enable automatic feedback, you set the Work-Channel-Triage.pyIsRuntimeFeedback rule to true in Pega Platform™. By default this feature is disabled. Enabling this feature ensures that the email bot is more responsive by automatically copying detected entities, such as names, locations, dates, and ZIP codes, to case type properties of a case type.

For more information, see Email triage, Email channel NLP model and Enabling the NLP model training for the email channel.

Descendant class instances now included in reports

The Report on descendant class instances option on the Data Access tab of the Report Definition rule has a new option to now include data from all descendant classes of the report's primary class. If descendant classes are mapped to multiple class tables, the generated SQL query performs UNIONs to include this data. Previously, only a single class was included in the report.

You can select a subset of descendant classes to include or exclude by adding a filter condition on pxObjClass.

Existing reports retain the older behavior for this option after an upgrade or update. To use the new option, you must set it for each existing report. New reports created in Designer Studio and out-of-the-box Pega 7 Platform template reports from which new reports are created in the Report Browser (pyDefaultReport and pyDefaultSummaryReport) default to the new option. If you have created custom template reports for some application classes, you must change them to enable the new option in reports that are created in the Report Browser for these classes.

See Reporting on data in multiple class tables.

Report Definition query filters can search embedded properties

Filter conditions on Report Definition rules that query the Elasticsearch indexes can now reference embedded properties. Previously, filter conditions could reference only the top-level properties of a class. To reference an embedded property within a filter condition, specify indexes for embedded page lists and page groups, for example, Customers(1).Addresses(1).City = Boston OR Customers(1).Addresses(1).State = MA.

These indexes are ignored in the generated query, and so can potentially match any value in a page list or group. However, filter conditions that reference multiple embedded properties in the same page list or page group, as displayed above, might not be satisfied on the same page.