Salesforce Com, Inc.

Salesforce, Inc.

Brian Esserlieu

Samarpan Jain

Jan Asita Fernando

Cody Marcel

Saikiran Perumala

Jay Hurst

Sahil Ramrakhyani

Seshank Kalvala

James Ferguson

Charles Fineman

Jonathan Bruce

Brian Jaffery Tajuddin

Siddhi Mehta

Reuben Cornel

Adam Torman

Eli Levine

Douglas Bitting

Cloves Carneiro Junior

Alexander Warshavsky

Carl Montgomery

Methods and corresponding systems and apparatuses for configuring user access to data stored in and/or accessed through an external computer system are described. Access permissions can be configured through defining a permission set relative to a proxy entity and assigning the permission set to one or more users. A proxy entity is a local representation of an external data entity. A proxy entity can be a virtual entity in that the proxy entity does not contain underlying data. A proxy entity can, however, include metadata describing its corresponding external data entity. A computer system maintaining a proxy entity can store mapping information linking the proxy entity to an external data entity. The mapping information and the permission set can be used to determine an access permission relative to the external data entity and to communicate this access permission to the external computer system so that access can be provided accordingly.

Access controls for external data records

Systems, methods, and computer-readable media for asynchronous (async) querying are described. In embodiments, an application server obtains a user-issued query comprising one or more query components; identifies data space characteristics of a data storage space associated with a user that issued the user-issued query; and analyzes the one or more query components to obtain performance data. The performance data is indicative of resource consumption for execution of the user-issued query. The application server selects a query execution engine to execute the user-issued query from among a plurality of query execution engines, and provides the user-issued query to the selected query execution engine. The selection is based on the data space characteristics and the performance data. Other embodiments may be described and/or claimed.

Technologies for runtime selection of query execution engines

Techniques and architectures for data ingestion in an environment having a distributed data storage system. A request to import data into the environment. The data to be imported from an external source through an application programming interface (API). The request is analyzed to determine if the request corresponds to a request to import a large data load. The data is staged if the request is for the large data load. Data management jobs are created to cause the data to be stored in one or more nodes of the distributed data storage system. The data management jobs are transmitted to corresponding nodes in the distributed data storage system. The jobs are executed asynchronously to cause the data to be stored in the nodes.

Techniques and architectures for importation of large data load

Systems, apparatus, methods and computer program products for deleting data of an object within a multi-tenant database via an efficient delete operation are described herein. The new delete operation (or method) may be configured to identify one or more records to be deleted within a data structure such as an object. In order to ensure that the operation is efficient, the mechanism may determine characteristics of a data store and determine whether the information provided within the data structure provides an efficient identification of the data to be deleted. Upon a successful validation, an initial delete request may be translated to an appropriate delete operation for the underlying database.

Efficiently deleting data from objects in a multi-tenant database system

In accordance with embodiments, there are provided mechanisms and methods for facilitating metadata-based statistics-oriented query processing for large datasets in an on-demand services environment. In one embodiment and by way of example, a method comprises evaluating metadata associated with a query placed on behalf of a tenant in a multi-tenant environment, and computing process statistics for the query based on the metadata, where the process statistics reveal an estimation of resources needed for execution of the query within a predictable amount of time and using fewer than or equal to an allocated number of scans of a database. The method may further include associating, based on the process statistics, a set of rules and the estimated resources to process the query, and executing the query based on the set of rules and using the estimated resources such that the query is processed within the predictable amount of time and using fewer than or equal to the allocated number of scans of the database.

Metadata-based statistics-oriented processing of queries in an on-demand environment

Disclosed are examples of systems, apparatus, methods and computer program products for deleting data of an object within a multi-tenant database. Described is a mechanism for performing operations such as an efficient delete operation by introducing a new delete operation (or method) that is configured to allow a data structure such as an object to identify one or more records to be deleted. In order to ensure that the operation is efficient, the mechanism may determine characteristics of a data store and determine whether the information provided within the data structure provides an efficient identification of the data to be deleted. Upon a successful validation, an initial delete request may be translated to an appropriate delete operation for the underlying database.

In accordance with embodiments, there are provided mechanisms and methods for facilitating dynamic selection and application for rules for query processing for large datasets in an on-demand services environment according to one embodiment. In one embodiment and by way of example, a method comprises determining processing patterns of a query based on historical performances associated with the query placed on behalf of a tenant in a multi-tenant environment, and dynamically applying one or more rules to the query for processing of the query within a predictable amount of time, where the one or more rules are dynamically selected from sets of rules based on the processing patterns. The method may further include executing the query based on the one or more rules by scanning one or more portions of a database having contents pertinent to the query and generating results by processing the query based on the contents and within the predictable amount of time.

Dynamic selection and application of rules for processing of queries in an on-demand environment

Big object bulk data ingest

Disclosed are examples of systems, apparatus, methods and computer program products for deleting data of an object within a multi-tenant database. Described is a mechanism for performing operations such as an efficient delete operation by introducing a new delete operation (or method) that is configured to allow a data structure such as an object to identify one or more records to be deleted. In order to ensure that the operation is efficient, the mechanism may perform a validation of the values provided to the delete operation based on the type of data store used for storing the records. For example, the validation may ensure that the values provided to the method correspond to a composite primary key when records are stored in a non-relational data store. Upon a successful validation, an initial delete request may be translated to an appropriate delete operation for the underlying database.

Growing community of inventors

Walnut Creek, CA, United States of America

Brian Esserlieu