INC-143121 · Issue 610732
Timeout for loading predictors made configurable
Resolved in Pega Version 8.3.6
When using an extremely large number of predictors, the Report definition pzADMPredictorsFilter was suffering timeouts due to the time for loading predictors from the database exceeding the time threshold allowed. This has been resolved by marking the rule as editable to allow custom setting of the threshold according to need.
INC-148899 · Issue 615702
Adaptive models update correctly
Resolved in Pega Version 8.3.6
Some models had the recorded responses column updated, but the models (number of Positive, Negative and Processed Responses) were not updated. Investigation showed that deleting the modelRuleConfiguration through the stateManager/client did not delete modelFactories related to the configuration. If a new configuration came in with a different algorithm, the update issue occurred. This has been resolved by reseting the configuration according to its factory in that specific case.
INC-155822 · Issue 618267
Locking added to avoid null pointer error for auto-populate property
Resolved in Pega Version 8.3.6
After configuring the auto populate property "OrgProduct" which referred to a data page, the system experiencing heavy load led to the property not getting properly initialized. This resulted in a WrongModeException and NullPointerException. To resolve this, the system has been updated to lock the requestor when Queue Processors execute their activity. This will prevent race conditions and concurrent modifications if other threads are accessing the same requestor.
INC-157629 · Issue 626633
Duplicate key exception resolved for adaptive model
Resolved in Pega Version 8.3.6
During the model snapshot update, a DuplicateKeyException was generated while trying to insert a record in to the predictor table. This did not affect the model's learning, but did appear ion the model monitoring report. This was traced to a local scenario of having the same outcome values defined on the model with different cases (Accept and accept). All predictors used in an Adaptive model are inserted into the model monitoring tables as a part of the monitoring job: because the monitoring tables are not case sensitive, this lead to a unique constraint exception since there were multiple IH predictors with the same name. To resolve this, validation has been added which will skip adding duplicates from new responses.
INC-160331 · Issue 628710
ML model continue tag error changed to debug logging
Resolved in Pega Version 8.3.6
Log entries were seen indicating "The continue tag is found without a start tag for window Line1 1234567890 in text". This appeared to be coming from the MLCommand java class. Investigation showed this occurred when the ML model believed a portion of the text (token) was a continuation of the entity because it has not found the starting part of the entity. This situation is not currently treated as a valid output, so an update has been made to change the logging level from error to debug for this case.
SR-69015 · Issue 619995
Unescaping characters implemented for expressions
Resolved in Pega Version 8.3.6, Resolved in Pega Version 8.4.4, Resolved in Pega Version 8.5.3, Resolved in Pega Version 8.6
An issue where expression builder statements were evaluated differently at runtime than at testing has been resolved. Pega Platform expressions with String literals(that is, sequences of characters enclosed in quotation marks) now unescape characters in strategy shapes such as Set Property or Filter.
INC-202111 · Issue 710106
Logging extended for PRPCPropertyInfoProvider
Resolved in Pega Version 8.7.3
In order to assist with diagnosing issues with Kafka and JSON, additional logging has been added for PRPCPropertyInfoProvider.
INC-208976 · Issue 719165
Enhanced SSA metrics made available
Resolved in Pega Version 8.7.3
In order to better diagnose delays related to the time when a Campaign is scheduled to start and the time when the Dataflow actually starts to run, an update has been made which will generate detailed metrics to cover some of the strategy execution key performance intensive areas. Additional lower level internal metrics related to SSA engine execution have also been made available by way of a DSS to collect more runtime insight for diagnosis. To enable the collection of these Level 2 SSA internal metrics, set the dataflow/shape/strategy/detailed_metrics/level2 DSS in the Pega-DecisionEngine rule set to 'true'. A comprehensive set of enhanced metrics will be available in Pega 8.8.
INC-217290 · Issue 721375
Added support for creating predictive models in Production
Resolved in Pega Version 8.7.3
While creating a new predictive model rule in Prediction studio, the case was going into broken process after selecting the template with the error message "Error loading D_ProjectList , Reason : No databases defined in properties file:/databases.properties". This was an unexpected use case for creating models in Production level, and has been resolved by updating the flows to turn off the draft mode in this scenario.
INC-218145 · Issue 715678
DSS introduced to control DSM clipboard page serialization
Resolved in Pega Version 8.7.3
When using a Kafka dataset to consume a message from an external topic that had an attribute name with a special character contained in a page list structure, using a JSON data transform for the mapping in a realtime dataflow resulted in the error "Exception in stage: KafkaDS; LegacyModelAspectInvokableRuleContainer.invoke-Exception encountered a :java.lang.UnsupportedOperationException." To resolve this, a new DSS dataset/CLASS_NAME/DATASET_NAME/JSONDataTransform/deserialization/useDSMPage has been introduced. When the value is set to true, the process will follow the previous behavior of DSM clipboard pages being generated when Kafka records are deserialized using JSON data transform. When the value is set to false, the JSON data transform will generate regular clipboard pages and convert them later to DSM clipboard pages. This would avoid errors when a JSON data transform calls methods from the Clipboard API that are not implemented by DSM pages. This DSS is set per data set instance. CLASS_NAME and DATASET_NAME are placeholders which should be replaced by data set's pyClassName and pyPurpose property values. In addition, a similar DSS, dataset/CLASS_NAME/DATASET_NAME/JSONDataTransform/serialization/useDSMPage, has been introduced for serialization.