Salesforce, Inc.

Mitchell Christensen

Parth Vijay Vaishnav

Kevin Han

Wei Gao

Binyuan Chen

David Clark

Thomas Jones

Harpreet Singh

Jalaleldeen Aref

Daniel May

Apparatus and method for a scalable pricing engine. A scalable pricing engine is operable in a low-scale mode and a high-scale or scalable mode to process pricing data in response to pricing requests. The pricing engine operates in a first runtime environment in the low-scale mode in which it can service pricing requests up to a threshold number and in which the pricing data can be updated and tested. The pricing engine also operates in a second runtime environment which is a scalable runtime supporting pricing requests above the specified threshold. The scalable runtime and pricing engine rely on a scalable read-only data service which distributes a read-only copy of at least a portion of the pricing data from the primary database, a high-scale cache to reduce access latency, data processing and network resource allocations, and data reduction techniques when processing requests and responses.

System and method for a scalable pricing engine

A rules engine (RE) may operate in conjunction with a database providing functionality, such as transactional support in data access environments on behalf of tenants. The database may have a data repository accessible by multiple tenants, and tenants may have a private context. RE rules may be defined in the data repository having an extension point corresponding to an extension defined in the private context. Execution of database functionality may affect data defined in the database. Execution of RE rules corresponding to database functionality affects corresponding data associated with the RE. Various techniques, e.g., fact handles, event listeners, etc. may be used to coordinate tracking and synchronizing changes between RE data and/or the database. A flag or other indicator may signify state preservation between multiple calls to the database and/or the RE, e.g., to support analysis involving transactions having overlapping intermediary results such as results from performing data lookups.

Multi-context stateful rule execution

Implementation of a guided rules editor comprises displaying a view of a user interface (UI) of the guided rules editor to a user on the client device. Metadata of an organization to which the user belongs is fetched and used to identify which data of the organization is eligible for rule creation and attributes of the metadata are used to determine a layout of the one or more UI selection elements. One or more UI selection elements are rendered in one or more positions in the view on a display screen according to the layout and at least a portion of the one or more UI selection elements are populated with the data of the organization. Responsive to receiving user input of the one or more UI selection elements, an organization-specific rule is generated and saved to a data repository for execution by a rules engine after rule compilation.

Metadata driven guided rules editor

Apparatus and method for asynchronous pricing. For example, some implementations include an asynchronous pricing service in addition to the pricing service. When performing a first pricing operation on a first set of pricing data, the pricing service performs operations on a pricing engine. In response to a second pricing request at the asynchronous pricing service with a second set of pricing data, the second set of pricing data is subdivided into a plurality of portions and a corresponding plurality of pricing jobs are specified, which are independently executed by the pricing engine to produce a corresponding plurality of partial pricing results. The asynchronous pricing service aggregates the partial pricing results to generate a second pricing result.

System and method for asynchronous pricing to improve pricing service scalability

Embodiments are directed to a pluggable architecture for performance of pricing operations. An embodiment of a storage medium includes instructions for installing multiple pricing methods at a pricing service including a pluggable architecture for pricing methods, each pricing method including a set of pricing functions; receiving a pricing request for a sales transaction including one or more sales items, the pricing request including a pricing context for each sales item; and performing a price calculation for the sales transaction, including resolving a pricing method for each sales item, applying the set of pricing functions for the resolved pricing method for each sales item, the application of the set of pricing functions being based at least in part on the pricing context for the sales item, calculating a price for each sales item based upon an outcome of the pricing functions, and aggregating the calculated prices for the sales items.

Pluggable architecture for performance of pricing operations

Pluggable pricing modules with functional extension points. A plurality of pricing methods are installed with a pricing service. The pricing service has a pluggable architecture to accept pricing methods including a set of pricing functions, and one or more extension points that provide programmatic hooks within an operational flow of the pricing service to provide optional interfaces to functional modules external to the pricing service. In response to a pricing request for a sales transaction, the pricing request including a pricing resolving selected pricing method for each sales item of the one or more sales items from the plurality of pricing methods. At least one of the one or more extension points are used to call an external functional module to interact with the operational flow of the pricing service. Aggregated results are provided to the source of the pricing request.

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Livermore, CA, United States of America

Mitchell Christensen