Published Release Notes

Integrate your application with external Kafka clusters

Configure your application to use an external Kafka cluster for managing real-time data. By having an external Stream service provider, you can perform such maintenance tasks as an upgrade or hotfix deployment faster because external nodes do not require restarting.

For more information, see Externally managed Stream service.

Updated default dynamic system setting for requestor pools

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.

Updated architecture of the data flow service

Benefit from improvements to data flow architecture that increase the stability of data flow runs and minimize the need for manual restarting of data flow jobs. Real-time data flows now use improved node rebalancing for better handling of failed or restarted nodes. If the topology changes, batch data flows no longer attempt to pause and resume the run. As such, there are fewer interactions with the database and between the nodes, resulting in the increased resilience of the Data Flow service.

If you are upgrading from a previous version of Pega Platform™, see Changes to the architecture of the Data Flow service for an overview of the changes to the Data Flow service compared to previous versions.

Changes to the architecture of the Data Flow service

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
• Updates to lifecycle actions
• Starting a run
• Triggering pre- and post-activities
• Selecting a node fail policy
• No service nodes and active runs

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.

Support for predictive models in PMML version 4.4

Pega Platform™ now supports the import of predictive models in Predictive Model Markup Language (PMML) version 4.4. With this feature, you can import PMML models that use the anomaly detection algorithm.

For a list of all supported PMML models, see Supported models for import. 

Condition builder enhancements

To enhance the precision of condition builder in both App Studio and Dev Studio, the new instances include comparator helps you specify the number of field group and field group list instances to which a when rule applies. In addition, a search option that returns results on keypress helps you easily look for existing values.

For more information, see Create conditions in an enhanced condition builder (8.5), Defining conditions in the condition builder.

Limits on active data flow runs

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.

Support for Apache HBase 2.1 and Hadoop 3.0

Support for these versions extends Pega Platform™ compatibility with HBase releases to ensure that your database implementations integrate seamlessly with Pega Platform.

Pega Platform now supports:

• Apache HBase 2.1 for the HBase data set
• Apache Hadoop Distributed File System (HDFS) 3.0 for the HDFS data set

For more information, see Enhance your data sets with Apache HBase 2.1 and Hadoop 3.0 (8.5).

Enhancing your revision management process with Deployment Manager pipelines

Pega Platform 8.5 offers improved synergy between revision management and the automated deployment process provided by Pega's Deployment Manager 4.8 pipelines. Use Deployment Manager 4.8 to increase the efficiency of business-as-usual application changes and automatize the deployment of revision packages.

For more information, see Managing the business-as-usual changes.

Support for Cloud AutoML topic detection models

In Prediction Studio, you can now connect to topic detection models that you create in Cloud AutoML, Google's cloud-based machine learning service. You can then use the models to categorize and route messages from your customers.

For more information, see Broaden your selection of topic detection models by connecting to third-party services (8.5).