Published Release Notes

Case-insensitive filtering in reports

Filtering in reports is now case-insensitive, improving the reporting and searching experience. You can turn off case-insensitive filtering, for example, by using an activity. You might want to do this if your index is too big or if the length of time it takes for indexing impacts performance.

For more information on report filtering, see Editing filter conditions.

PATCH support for REST integrations

REST connectors and REST services now include support for integrating with REST APIs that require using the PATCH method. REST integrations that support the PATCH method allow you to make partial updates to individual resources while retaining data that does not require an update. With PATCH support, you no longer need to write complex custom code to integrate with services that require using the PATCH method.

For more information about REST integrations, see Connect REST form - Completing the Methods tab.

Configure REST connectors for standard-compliant endpoints

REST connectors now support all standards-compliant endpoint URLs, regardless of how those URLs use query parameters.

This URL support allows you to do the following actions:

• Include repeated query string parameters in a URL.
• Omit a query string parameter if the value of the parameter is empty when you send a request.
• Apply appropriate encoding for different URL components.

For more information about REST connectors, see Connect REST form.

Elasticsearch reports support string comparison operators

To improve performance, reports that use string comparison operators in filters can now run queries against Elasticsearch instead of querying the database. The following operators are now supported for Elasticsearch queries.

• Starts with, Ends with, Does not start with, and Does not end with
• Contains and Does not contain
• Greater than, Less than, Greater than or equal, and Less than or equal

In cases where a query cannot be run against Elasticsearch, the query is run against the database, for example, if the query includes a join. To determine if a query was run against Elasticsearch, use the Tracer and enable the Query resolution event type. For more information, see Tracer event types to trace.

Predictive models monitoring

In Prediction Studio, you can now monitor the predictive performance of your models to validate that they make accurate predictions. Based on that information, you can re-create or adjust the models to provide better business results, such as higher accept rates or decreased customer churn.

For more information, see Monitoring predictive models.

Kafka custom serializer

In Kafka data sets, you can now create and receive messages in your custom formats, as well as in the default JSON format. To use custom logic and formats for serializing and deserializing ClipboardPage objects, create and implement a Java class. When you create a Kafka data set, you can choose to apply JSON or your custom format that uses a PegaSerde implementation.

For more information, see Creating a Kafka data set and Kafka custom serializer/deserialized implementation.

Additional configuration options for File data sets

You can now create File data sets for more advanced scenarios by adding custom Java classes for data encryption and decryption, and by defining a file set in a manifest file.

Additionally, you can improve data management by viewing detailed information in the dedicated meta file for every file that is saved, or by automatically extending the filenames with the creation date and time.

For more information, see Creating a File data set for files on repositories and Requirements for custom stream processing in File data sets.

Simplified testing of event strategies

Evaluate event strategies by creating test runs. During each run, you can enter a number of sample events with simulated property values, such as the event time, the event key, and so on. By testing a strategy against sample data, you can understand the strategy configuration better and troubleshoot potential issues.

For more information, see Evaluate event strategies through test runs.

Data flow life cycle monitoring

You can now generate a report from the Run details section of a Data Flow rule that provides information about run events. The report includes reasons for specific events which you can analyze to troubleshoot and debug issues more quickly. You can export the report and share it with others, such as Global Customer Support.

For more information about accessing event details, see Creating a real-time run for data flows and Creating a batch run for data flows.

Data flow runs retry connections that fail

Real-time and batch data flow runs now retry dataset connections that fail when the related service is temporarily unavailable, for example, when a connection to a Cassandra database times out. With the automatic retries, you no longer need to run data-heavy and CPU-intensive jobs multiple times and the maintenance of data flow runs diminishes significantly.

For more information about accessing event details, see Creating a real-time run for data flows and Creating a batch run for data flows.