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Published Release Notes

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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:

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.

External data flow rules are deprecated

Valid from Pega Version 8.5

External data flows are now deprecated and no longer supported. To improve your user experience with Pega Platform™, the user interface elements associated with these rules are hidden from view by default. Identifying unused features allows Pega to focus on developing and supporting the features that you need.

For more information, see Deprecated: External data flows.

Visual Business Director data is automatically cleaned after a retention period expires

Valid from Pega Version 8.5

To avoid negative impact on system resources, such as memory and disk space, Pega Platform™ automatically cleans out collections data accumulated in Visual Business Director after the time period specified in the vbd/dataRetentionTimeout dynamic system setting.

Upgrade impact

In versions of Pega Platform earlier than 8.5, collections data was not automatically removed. From version 8.5, the data is removed after 465 days (15 months) by default.

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

If the default data retention period does not meet your requirements, you can change it by editing the vbd/dataRetentionTimeout setting.

For more information, see "Configuring the data retention period for Visual Business Director" in the Pega Customer Decision Hub 8.5 Upgrade Guide on the Pega Customer Decision Hub product page.

Uploading customer responses into adaptive models is no longer available

Valid from Pega Version 8.5

The option to train adaptive models by uploading a static list of historical interaction records has been deprecated in Pega Platform™ 8.5.

Upgrade impact

In versions of Pega Platform earlier than 8.5, it was possible to train an adaptive model by uploading historical data of customer interaction. After the upgrade to version 8.5, this option is no longer available.

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

Use data from a report definition to train adaptive models. For more information, see Training adaptive models.

Support for validations in Declare Expression rules

Valid from Pega Version 8.5

Pega Platform™ now correctly evaluates validations on target properties in Declare Expression rules. As a result, any validations defined on properties that have Declare Expression rules in previous releases, now work correctly. 

Upgrade impact

Any unintended validations on properties that have configured Declare Expression rules, both default and user-defined, might cause issues in an application upon saving work objects and data objects that refer to Declare Expression rules.

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

If you experience issues when saving objects, debug your application by setting the declareexp/target/validation/disable dynamic system setting to true. If your application works correctly after the change, it means that validations on Declare Expressions cause the issues. For your application to work correctly, analyze the log files and remove any unintended validations.

For more information, see Declare Expression rules.

Upgraded selected third-party JAR files to support Pega Platform functionalities

Valid from Pega Version 8.6

Pega Platform™ 8.6 now provides upgraded versions of the JAR files that support various functionalities within the Platform, such as generating documents or PPTX files.

Upgrade impact

If you have custom implementations and use any classes from the upgraded JAR files directly in your code or through JAR APIs, after your upgrade to Pega Platform 8.6, your application might experience unexpected run-time behavior if the upgraded JAR version lacks any elements from the previous version. For example, custom implementations can typically include activities, functions, or non-autogenerated sections. Unexpected run-time behavior might also occur when you use a third-party library that has dependencies on the upgraded JAR files. If you use only default Pega Platform functionalities without any customizations, the JAR files continue to work correctly without any additional actions.

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

If you have any JAR customizations, ensure that you test the upgraded JAR files for compatibility and upgrade impact. The following table lists the upgraded JAR files that might impact your application:

JAR file nameUpgraded version
apache-mime4j-core0.8.3
apache-mime4j-dom0.8.3
commons-codec1.15
commons-collectionons44.4
commons:commons-math33.6.1
commons-compress1.20
commons-lang33.9
fontbox2.0.19
httpclient4.5.12
httpcore4.4.13
httpmime4.5.12
istack-commons-runtime3.0.8
jackson-annotations2.10.3
jackson-core2.10.3
jackson-databind2.10.3
jaxb-runtime2.3.2
java-libpst0.9.3
jcommaner1.78
junrar4.0.0
metadata-extractor2.13
openjson1.0.11
parso2.0.11
pdfbox2.0.19
poi4.1.2
poi-ooxml4.1.2
poi-ooxml-schemas4.1.2
poi-scratchpad4.1.2
slf4j-api1.7.28
xmlbeans3.1.0
xmpcore6.1.10

For more information, refer to the documentation of  your JAR provider.

Text predictions simplify the configuration of text analytics for conversational channels

Valid from Pega Version 8.6

Enable text analytics for your conversational channels, such as email and chatbot, by configuring text predictions that manage the text models for your channels. This new type of prediction in Prediction Studio consolidates the AI for analyzing the messages in your conversational channels in one place and replaces the text analyzer rule in Dev Studio.

Through text predictions, you can efficiently configure the outcomes that you want to predict by analyzing the text in your channels:

  • Topics (ticket booking, subscription cancellation, support request)
  • Sentiments (positive, neutral, negative)
  • Entities (people, organizations, airport codes)
  • Languages

You can train and build the models that predict these outcomes through an intuitive process, and then monitor the outcomes through user-friendly charts.

For more information, see Predict customer needs and behaviors by using text predictions in your conversational channels.

Upgrade impact

Channels that you configured with text analyzers in the previous version of your system continue to work in the same manner after the upgrade to the current version. When you edit and save the configuration of an existing channel, the text analyzer rule is automatically upgraded to a text prediction. The associated text prediction is now an object where you can manage and monitor the text analytics for your channel. When you create a new channel in the upgraded system, the system automatically creates a text prediction for that channel.

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

  1. Enable the asynchronous model building and reporting in text predictions through job schedulers that use the System Runtime Context (SRC) by adding your application to the SRC.
    For more information, see Automating the runtime context management of background processes.
  2. Enable model building in text predictions by configuring background processing nodes.
    For more information, see Assigning decision management node types to Pega Platform nodes.

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