INC-201366 · Issue 691509
Performance improvements for stale thread warnings
Resolved in Pega Version 8.6.3
Stale thread warnings were causing performance issues during dataflow run execution. Stale thread/slow component warnings are added as part of the dataflow execution when a processing thread takes more than 5 minutes to process a single dataflow record. The stacktrace of the dataflow thread is added as part of the warning for debugging purposes, but in some scenarios the stacktrace can become very large. This has been resolved by removing the stacktraces from the warning, improving the query logic, and adding the run ID to the exception method to assist if there is an error.
INC-201610 · Issue 691879
Removed services check and added warnings for simulations
Resolved in Pega Version 8.6.3
Attempting to run an audience simulation resulted in the error "Running simulations is not possible, because the required services are not available. Contact your system administrator to enable the data flow and real-time data grid services". Investigation showed the @DsmServices.pxHasFunctionalNodes("DataFlow","Batch") function call contained in the 'when' rule pyUnavailableDecisionServices was returning false even if all the nodes were in the cluster and all the DSM Services were in NORMAL status. To resolve this, the services check has been disabled and the simulation run will show a warning or fail if a data flow run is queued for more than 30 secs or if there is an issue with querying the underlying metrics storage.
INC-202937 · Issue 695942
RealTimeProcessingDelay made configurable
Resolved in Pega Version 8.6.3
When using interaction history summaries in Engagement Policy strategies, the check whether a particular action was sent previously was not returning any results for the customers that did have an action "Sent". These records were present in the IH Fact table but the IH Summary tables were missing these records. This has been resolved by making the realTimeProcessingDelay configurable for reading from IH in a real-time flow configurable (done as part of IH pre-aggregation). This may be useful if there can be a difference in time between machines inserting into IH which causes pre-aggregation to miss processing records. The relevant DSS is "interactionHistory/realTimeProcessingDelay", and the default is 5 seconds. This must be set before starting pre-aggregation. This is the difference between the end position and the log time. RuleSet: Pega-DecisionEngine DSS Name: interactionHistory/realTimeProcessingDelay Value: <new delay in seconds>
INC-204682 · Issue 695705
HandleResponses updated for use with optimized campaign
Resolved in Pega Version 8.6.3
After updating from Pega 8.4, responses were not processed in the pxHandleResponses Data flow if the "pyCampaignOptimization" flag was set to true. This was traced to a missed use case for an optimized campaign, and has been resolved by modifying the condition in HandleResponses.
INC-205010 · Issue 698490
Updated expression string comparison
Resolved in Pega Version 8.6.3
An expression defined in the proposition filter and/or When rule was evaluated differently depending on whether it was run from within Decision Strategy or run isolated in Expression Builder or a When rule. This has been resolved by updating the string comparison.
INC-205378 · Issue 698443
Added handling for double quotes on Substrategy shapes
Resolved in Pega Version 8.6.3
After update, there were compilation issues on calling substrategies from strategies using datapage.pxResults as another page. This was traced to the handling for a parameterized data page with double quotes on Substrategy shapes, and has been resolved.
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.