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Published Release Notes

Find release notes for the selected Pega Version and Capability

Browse resolved issues for Platform releases.

This documentation is for non-current versions of Pega Platform. For current release notes, go here.

Use Page-Change-Class method to change a Page class

Valid from Pega Version 8.3

With the release of Pega Platform™ 8.3, using the Property-Set method to change the Page class shows a guardrail warning. Use the Page-Change-Class method to change the Page class instead.

For more information about the Page-Change-Class method, see Changing the class of a page and Page-Change-Class method.

Cassandra 3.11.3 support for Pega Platform

Valid from Pega Version 8.3

Increase your system's reliability and reduce its memory footprint by upgrading the internal Cassandra database to version 3.11.3.

For on-premises Pega Platform™ users, after you upgrade to Pega 8.3, it is recommended that you manually upgrade to Cassandra 3.11.3. You can upgrade to Cassandra 3.11.3 on all operating systems except IBM AIX. If you do not want to upgrade to Cassandra 3.11.3, you can continue to use Cassandra 2.1.20, which is still supported.

For Pega Cloud Services 2.13 and later versions, Cassandra automatically upgrades to version 3.11.3 after your environment is upgraded to Pega Platform 8.3.

For information on how to manually upgrade to Cassandra 3.11.3, see the Pega Platform 8.3 Upgrade Guide for your server and database at Deploy Pega Platform.

Upgrade impact

After an on-premises Pega Platform upgrade, you still have the older version of Cassandra and must manually upgrade.

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

To upgrade Cassandra, you must create a prconfig setting or a dynamic system setting with the new Cassandra version and then do a rolling restart of all the Decision Data Store nodes to upgrade them to the latest version of Cassandra.

 

Changes to URL format for Pega Web Mashup

Valid from Pega Version 8.3

The format of Pega Web Mashup URLs now supports multiple mashups on a single web page. The new URL format prevents a mashup from sharing cookies with another mashup.

If you want to include multiple mashups on a single web page and one of the mashups was created before 8.3, you must regenerate the mashup to reflect the new URL format.

For more information, see <link to help topic>.

Change tracking tab removed from declare expressions

Valid from Pega Version 8.3

To simplify the configuration of a declare expression, the Change tracking tab has been removed from the declare expression rule form. To use the Change tracking tab, on the declare expression rule form, click Actions > Use legacy expression.

For more information, see Supported and unsupported configurations in simplified declare expressions.

Upgrade impact

The declare expression rules have been moved from the pr4_rule table to the new pr4_rule_declare_expression table.

The upgrade can fail if you customize the pr4_rule table, such as increasing the length of an existing column.

After a successful upgrade, the declare expression rule form is simplified and lightweight pages support declare expressions.

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

Read-only data pages by default are lightweight. You could also enable lightweight pages for:

Only simplified declare expressions are supported in lightweight pages. In simplified declare expressions, context-bound rules are not supported. However, a page context could be specified on the New or Save As form of declare expressions. For more information, see Declare Expression rules - Completing the New or Save As form.

Redirectguests mashup configuration has been removed

Valid from Pega Version 8.3

The authentication/redirectguests server configuration and data-pega-redirectguests attribute have been removed and are no longer required when you configure a mashup. This prevents you from needing to maintain multiple nodes to support some use cases that require the configuration value to be true and other uses cases that require the configuration value to be false.

For more information, see Configuring the Mashup channel.

Text analytics models editing and versioning

Valid from Pega Version 8.3

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:

 

Text analytics models migration

Valid from Pega Version 8.3

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:

Rules can no longer access Pega internal Java packages

Valid from Pega Version 8.4

You can no longer create rules that access Java packages that reference internal APIs (syntax com.pega.platform.*.internal*). This change does not affect rules that access Pega public API packages.

If you encounter issues when running existing rules that reference internal Pega APIs, contact Pega Support.

Upgrade impact

After an upgrade to 8.4 and later, clients can no longer save new or modified rules that access Pega internal APIs; existing rules that reference internal APIs can still be run but cannot be modified. 

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

Following a software upgrade to 8.4 or later, clients can refactor existing rules into guardrail compliant rules. To find rules to refactor, run the validation tool from designer studio (Application > Tools > Validation) to identify what rules fail validation; failed rules that include the message "Test compilation failed : Illegal internal class reference : com.pega.internal.XYZ" can updated to reference appropriate APIs.

Updated default dynamic system setting for requestor pools

Valid from Pega Version 8.4

Clients can now enable or disable requestor pools for processing service requests using a new dynamic system setting called EnableRequestorPools with Pega-IntegrationEngine as the owning rulest. Previously, all deployments utilized requestor pools to improve service processing response efficiency; requestor pools eliminated overhead by automatically returning a requestor to the pool after it fulfills a service request. Starting in Pega Platform 8.4, requestor pools are disabled in Client-managed cloud deployments, since these deployments use autoscaling to handle service request traffic. Enabling requestor pools in Kubernetes environments is not recommended, because they can inhibit the default autoscaling settings in the environment.

Requestor pools remain enabled by default in Pega Cloud and on-premises environments.

To help clients navigate this change, Pega has updated its best practice guidance for configuring requestor pools. For an overview, see Requestor pooling for services. For guidance on the use of requestor pools in your application, see the EnableRequestorPools entry in Dynamic system settings data instances.

Upgrade impact

Requestor pools are disabled by default in Pega Platform 8.4 in client-managed cloud deployments. Clients who deployed previous versions of Pega Platform on a Kubernetes environment and who upgrade to Pega Platform 8.4 could see that their services behave differently.

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

If clients that are deployed in a Client-managed cloud environment need to configure their services to use requestor pools and they understand how to configure requestor pools for their optimized use, these clients can re-enable requestor pools. Clients should review the best practice for configuring requestor pools before they re-enable requestor pools. To re-enable requestor pools, you modify the EnableRequestorPools setting in the Pega-IntegrationEngine Owning ruleset from “disabled” to Enabled [no value]. For details, see Editing a dynamic system setting.

Changes to the architecture of the Data Flow service

Valid from Pega Version 8.4

In Pega Platform™ 8.4, the architecture of batch and real-time data flows uses improved node handling to increase the stability of data flow runs. As a result, there are fewer interactions with the database and between the nodes, resulting in increased resilience of the Data Flow service.

If you upgrade from a previous version of Pega Plaftorm, see the following list for an overview of the changes in the behavior of the Data Flow service compared to previous versions:

Responsiveness

Nodes no longer communicate and trigger each other, but run periodic tasks instead. As such, triggering a new run does not cause the service nodes to immediately start the run. Instead, the run starts a few seconds later. The same applies to user actions such as stopping, starting, and updating the run. The system also processes topology changes as periodic tasks, so it might take a few minutes for new nodes to join runs, or for partitions to redistribute when a node leaves a run.

Updates to lifecycle actions

To make lifecycle actions more intuitive, the Stop action consolidates both the Stop and Pause actions. The Start action consolidates both the Resume and Start actions.

You can resume or restart stopped and failed runs with the Start and Restart actions. The Start action is only available for resumable runs and continues the run from where it stopped. The Restart action causes the run to process from the beginning. Completed runs can only be restarted. If a run completes with failures, you can restart it from the beginning, or process only the errors by using the Reprocess failures action.

Starting a run

New data flow runs have the Initializing status, and start automatically. You no longer need to manually start a new run, so the New status is now removed.

If there are no nodes available to process a run, the run gets the Queued status and waits for an available node.

Triggering pre- and post-activities

The system now triggers pre-activities on a random service node, rather than on the node that triggered the run.

The system triggers post-activities only for runs that complete, fail, or complete with failures. If you manually stop a run with the Stop action, the post-activity does not trigger. However, restarting the run with the Restart action triggers first the post-activity, and then the pre-activity.

You can no longer choose to run pre- and post-activities on all nodes.

Selecting a node fail policy

For resumable runs, you can no longer select a node fail policy. If a node fails, the partitions assigned to that node automatically continue the run on different nodes.

For non-resumable runs, you can choose to restart the partitions assigned to the failed node on different nodes, or to fail the partitions assigned to the failed node.

No service nodes and active runs

If the last data flow node for an in-progress run fails, the run remains in the In Progress state, even if no processing takes place. This behavior results from the fact that data flow architecture now prevents unrelated nodes from affecting runs.

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