SR-D89643 · Issue 548291
Old Tumbling Time data in event strategies given TTL for cleanup
Resolved in Pega Version 8.2.7
Old Tumbling Time data keys in event strategies were not being cleaned up, causing Cassandra timeouts after the dataflow run had been running for several months. The longer the dataflow was running using standard compaction, the more the data was potentially spread out across SSTables and the slower it became. This has been resolved by adding a 'time to live' value for tumbling time windows, and event strategies has been switched to use leveled compaction by default.
SR-D90367 · Issue 556686
Cleanup enhanced for long pyEditElement names
Resolved in Pega Version 8.2.7
A pyEditElement error relating to decision data was seen multiple times in a stack trace. Research showed that while the utility worked as expected for decision data rules with names of less than 30 characters, the pyEditElement section was truncated the name for the decision data. This meant that decision data with the name SampleIssueandSampleGroupTwosalkdjkightntbmkblffvfvfv would be saved as SampleIssueandSampleGroupT for the pyEditElement section. Because of this, the utility failed the match and did not clean up the pyEditElement section. To resolve this, the cleanup utility has been updated to handle pyEditElement sections of decision data with longer names. Additional logging has also been added to improve debugging.
SR-D90579 · Issue 550769
Real Time data flow exception during shutdown corrected
Resolved in Pega Version 8.2.7
A Real Time DataFlow run was failing with a java.util.concurrent.RejectedExecutionException. This occurred when the node was shutting down while picking up a partition, and was due to the life cycle events not being able to update the partition status. To resolve this, a check has been added to prevent the exception form occurring by evaluating whether the Executor is shutdown while distributing messages.
SR-D96836 · Issue 555748
Refinements made to MarkerNode memory use
Resolved in Pega Version 8.2.7
Significant memory usage can be observed when data join in strategies are missing the join conditions. E.g., when the primary source in the strategy can have up to 300 or even more propositions, the join without the join condition would perform a cartesian product with possibly up to 400 or even more records returned. This may cause performance degradation. To guard against this, an update has been made that seeks to prevent repetitive markers being accumulated under a MarkerNode while trying not to rely on any implementation of equality and hashCode for individual marker implementations.