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

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.

 

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:

Support for the JSON Web Token Bearer grant type for accessing external APIs

Valid from Pega Version 8.4

You can now access external APIs by using the new OAuth 2.0 JSON Web Token (JWT) Bearer grant type, in an OAuth 2.0 authentication profile. To use the JWT Bearer grant type as a client assertion, source the JWT from an active SSO session, a token profile, or a property reference. You can use JWTs that you obtain during an OpenID Connect SSO in connectors, to achieve user impersonation flows, such as the On-Behalf-Of (OBO) flow. The OAuth 2.0 type authentication profile now also supports authentication of client applications by using Private Key JWTs.

Instances of the OAuth 2.0 provider are now deprecated. As a best practice, use the new, unified authentication profile configuration instead.

For more information, see Configuring an OAuth 2.0 authentication profile.

Upgrade impact

After an upgrade to Pega Platform 8.4 and later, Authentication Profiles can take advantage of the new JWT based OAuth 2.0 grant type and client authentication features. To take advantage of this and other new security features, you must update any existing Authentication Profiles formats must to use those in Pega Platform 8.4 and later.

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

Since these features are available only for profiles created in Pega Platform 8.4 and later, clients must open and then save existing 'Authentication Profile' instances to ensure that the configuration is compatible with the latest authentication formats.

Custom application URL alias in the application definition

Valid from Pega Version 8.4

Create application URL aliases that support your ability to launch multiple Pega applications simultaneously in a single browser. This feature makes it easier for clients and your customers to log into multiple applications using the same browser and access them simultaneously. You configure your application URL alias in the application definition. For details, see Adding an application URL alias.

For more information, see Simplify access with an Application URL alias (8.4)

Upgrade impact

After an upgrade to Pega Platform™ 8.4 and later, review to determine if and how you must update your application rules to reflect the current URL aliasing format. As part of these application rule updates, Pega also updated the URL format and value components of the clipboard property, pxRequestor.pxReqServletNameReal, which you can use to discover a servlet name. If your application uses this property to discover a servlet name, update the pxRequestor.pxReqServlet property in the application rule to use the new, required URL and value formats.

For details steps, see the section, Upgrading from Pega 8.3 or earlier: Guidelines for any required changes in your application URL aliasing, in the appropriate Pega Platform Upgrade Guide available at Deploy Pega Platform

What steps should the customer take to update their application?

After upgrading, you must update your JMeter test scripts. For detailed steps, see Updating JMeter test scripts after migrating to Pega 8.4.

Add the security checklist to applications created before 7.3.1

Valid from Pega Version 7.3.1

The security checklist is now automatically added to applications. You can manually add the security checklist to applications that were created in earlier versions.

You can improve the security of your application by completing the tasks on the checklist.

The following task reflects the procedure on how to manually add the security checklists to Designer Studio prior to 7.3.1:

  1. In the header of Designer Studio, click the name of the application, and then click Definition.
  2. Click the Documentation tab.
  3. In the Application guides section, click Add guide.
  4. In the Application guide& field, enter pxApplicationSecurityChecklist.
  5. Click the Configure icon in the Available in column and select the portals (App Studio and Dev Studio) that you want to add the security checklist to.
  6. Click Save.

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.

Limits on active data flow runs

Valid from Pega Version 8.5

You can now configure a maximum number of concurrent active data flow runs for a node type. Set limits to ensure that you do not run out of system resources and that you have a reasonable processing throughput. If a limit is reached, the system queues subsequent runs and waits for active runs to stop or finish before queued runs can be initiated, starting with the oldest.

For more information see, Limit the number of active runs in data flow services (8.5).

Upgrade impact

If you have many data flow runs active at the same time, you might notice that some of the runs are queued and waiting to be executed.

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

You do not have to take any action. After the active runs stop or finish, the queued runs start automatically. The default limits are intended to protect your system resources, and you should not see a negative impact on the processing of data flows. However, if you want to allow a greater number of active data flow runs to be active at the same time, you can change the limits. For more information, see Limiting active data flow runs.

Failed Robotic Assignments work queue type changed to Standard

Valid from Pega Version 8.5

The default Failed Robotic Assignments work queue type is now Standard. In previous releases, the default type was Robotic. For usage information, see Configuring a work queue for robotic automation.

Upgrade impact

After upgrading to Pega Platform 8.5 and later, you cannot save case types in which you configure the Queue for robot smart shape to route new assignments to the Failed Robotic Assignments work queue. Existing assignments that you routed to the Failed Robotic Assignments work queue are not affected.

How do I update my application to be compatible with this change?

As a best practice, do not use the Failed Robotic Assignments work queue in your custom implementations. Instead, configure the Queue for robot smart shape to route new assignments to a Robotic work queue. When possible, update existing case types to use the robotic work queues that you created in your application.

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