Data Set rule form - Completing Data Sets

Define the keys.

• The Database Table section displays the database table name that the class is mapped to.
• In the Selectable Keys section, add as many keys as necessary, and map each key to a property.
• In the Partitioning key section, select the property used to split the data into as many equal segments as possible across PRPC nodes.
  • To ensure a balanced distribution, select a property that is suitable for partitioning. For example, assuming a table containing customer information, country information is a suitable property for partitioning because it contains enough shared distinct values, but email address is not because it typically has as many distinct values as customer entries.
  • Another consideration is the correlation between number of segments (the grouped distinct values delivered by the property) and number of PRPC nodes. The ideal distribution is considered to be as many segments as PRPC nodes.

Note:  You can create this data set when you have at least one decision data node in the cluster.

This data set stages data for fast decisioning. You can use it when you want to access data very quickly by using a particular key.

When you create an instance of this data set, you need to define the keys.

• The keys that you specify in a data set define the data records managed in the Cassandra internal storage. Add as many keys as necessary and map each key to a property.
• The first property in the list of keys is the partitioning key used to distribute data across different decision nodes. To keep the decision nodes balanced, make sure that you use a partitioning key property with many distinct values.
• Changing keys in an existing data set is not supported. You have to create another instance.

The HBase data set is designed to read and save data from an external Apache HBase storage. This data set can be used as a source and destination in the Data Flow rules instances.

For configuration details, see Creating HBase data set.

The HDFS data set is designed to read and save data from an external Apache Hadoop File System (HDFS). This data set can be used as a source and destination in the Data Flow rules instances. It supports partitioning so you can create distributed runs with data flows. Becasue this data set does not support the Browse by key option, you cannot use it as a joined data set.

For configuration details, see Creating HDFS data set.

The Monte Carlo data set is a tool for generating any number of random data records for a variety of information types. When you create an instance of this data set, it is filled with varied and realistic-looking data. This data set can be used as a source in the Data Flow rules instances. You can use it for testing purposes in absence of real data.

For configuration details, see Creating Monte Carlo data set.

This type of data set allows you to process continuous data stream of events (records).

Stream tab

The Stream tab contains details about the exposed services (REST and WebSocket). These exposed services handle stream data set as a resource located at http://<HOST>:7003/stream/<DATA_SET_NAME>, for example: http://10.30.27.102:7003/stream/MyEventStream

Settings tab

The Settings tab allows you to set additional options for your stream data set. After saving the rule instance, you cannot change the settings.

Authentication

The REST and WebSockets endpoints are secured by using the Pega 7 Platform common authentication scheme. Each post to the stream requires authenticating with your user name and password. By default the Enable basic authentication check box is selected.

In the Retention period field, you specify how long the data set keeps the records. The default value is 1 day.

In the Log file size field, you specify the size of the log files, between 10 MB and 50 MB. The default value is 10MB.

No configuration required. The data set instance is automatically configured with the Visual Business Director server location as defined by the Visual Business Director connection.

You can create the following data set records for analyzing text-based content posted on social media:

• Facebook
• Twitter
• YouTube

Note: Facebook, Twitter, and YouTube data sets are available when your application has access to the Pega-NLP ruleset.