Published Release Notes

Improved management of batch indexing

You can now cancel and check the status of a batch index process directly from the Search landing page in Dev Studio. The landing page now refreshes automatically every 10 seconds so that you can easily see the most recent status of the batch index process. Additionally, the reindex operation is now 20% faster than in previous versions. These features provide greater visibility into batch indexing and improve your ability to fix issues with a batch indexing process.

For more information about batch indexing, see Rebuilding search indexes from the user interface.

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Use fast processing option does not work

The Use fast processing option on Connect REST and Service REST rule forms does not work unless you obtain the HFix-43000 hotfix from Global Customer Service.

Use the following data model guidelines when using fast processing:

• The JSON property names and the clipboard property names must match.
• The JSON tree structure and the clipboard tree structure must be similar.
• The scalar arrays in JSON must be mapped to the clipboard as page lists.
• Multi-dimensional arrays must be mapped into page lists of page lists with the same embedded property names.

​In addition, page groups, value groups, and Java objects are not supported by fast processing.

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.

Ability to periodically defragment the PR_SYS_LOCKS table

Lock management in Pega 7 is controlled through the database table PR_SYS_LOCKS. Because of the high volume of entries inserted into and deleted from this table during case processing, the data in the PR_SYS_LOCKS table can become highly fragmented, causing degraded performance over time.

You can now periodically defragment this table. The following Dynamic System Settings control when and how often defragmentation takes place using DefragSysLocksDaemon:

• syslocks/defragIntervalType

  • ​Custom interval type to run defragmentation

  • Set this first – determines whether defragmentation occurs daily at a specified hour or at a recurring interval

• ​syslocks/defragHourOfDay
  • ​​Used to run defragmentation at a specified hour
  • Accepts only integer values in the range 0-23
  • Example: run defragmentation daily at 1 AM by entering a value of "1"

  • Can be ignored if syslocks/defragIntervalType indicates a a specified periodic interval

• syslocks/defragIntervalInHours
  • Used to run defragmentation at specified periodic intervals
  • Accepts only integer values > 0

  • Example: run defragmentation every four hours by entering a value of "4"

  • Can be ignored if syslocks/defragIntervalType indicates a specified hour

For more information, see PEGA0056 alert monitors PR_SYS_LOCKS defragmentation performance.

Enhanced refresh token strategy

You now have more precise control over your refresh token expiration strategy. When a refresh token is enabled, you can choose to set its initial expiration based on the value provided by the IDP. The refresh token expiry can be derived from IDP’s session timeout when SSO is used with external IDP for user authentication in the authorization code grant flow. You can also specify a separate refresh token expiration strategy based on your use-case. 

These can be configured in the OAuth2 Client registration rule form.

For more information, see Enhanced refresh token strategy.

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

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