As you move Decision Services that use an EDC database connection toward production, you might want to get the performance benefits of caching entities and queries used in Decision Services. Even though the first cache use has some performance overhead, subsequent references that use that cached content will see excellent performance.
There are two ways caching can be used in Corticon rules:
1. Entity cache is appropriate when common database-enabled entities are used in the input messages sent to a Decision Service.
Case for using entity caching: Consider a Decision Service that expedites Shipping Requests. The Decision Service might receive a Shipment Request for an Order entity that has a one-to-many association with Customer entities. When the Decision Service receives an Order entity, it would query the database to get the associated Customer entities (for example, the Decision Service needs the customer’s address to estimate delivery lead time). When using an entity cache, the Customers could be queried once, and that data used in expediting other Order entities.
2. Query cache optimizes lookup (query) of database data, such as when a cached entity is extended to the database in a Rulesheet. A Query Cache is read-only -- it should not be expected to receive updates from the rules. If an update is attempted on an entity contained in a query cache, an exception occurs. A Query Cache is optimistic -- that means that updates from outside of the Decision Service will not modify or invalidate the cache contents.
Case for using query caching: A Decision Service that prices online orders using a query cache could query state sales tax rates (or VAT rates) once, and then use that data when calculating the price of all orders it receives.
Case for using query caching: Consider a Decision Service that prices insurance policies. With a query cache, it could query an actuarial table once, and then use the results in pricing multiple insurance policies.
First, you specify the caching settings in the Vocabulary and Rulesheets, and then enable caching in tests and deployed Decision Services.
Case for using entity caching: Consider a Decision Service that expedites Shipping Requests. The Decision Service might receive a Shipment Request for an Order entity that has a one-to-many association with Customer entities. When the Decision Service receives an Order entity, it would query the database to get the associated Customer entities (for example, the Decision Service needs the customer’s address to estimate delivery lead time). When using an entity cache, the Customers could be queried once, and that data used in expediting other Order entities.