Salesforce Com, Inc.

Salesforce, Inc.

Jan Asita Fernando

Samarpan Jain

James Ferguson

Cody Marcel

Eli Levine

Brian Esserlieu

Jay Hurst

Sahil Ramrakhyani

Saikiran Perumala

Seshank Kalvala

Matthew Francis Davidchuk

Valliappan Annamalai Natarajan

Charles Fineman

Alexandre Hersans

Jonathan Bruce

Alexey Syomichev

William Victor Gray

Brian Jaffery Tajuddin

Siddhi Mehta

Reuben Cornel

Alex S Warshavsky

Adam Torman

Arthur Ping-Tak Che

Sahil Bhutani

Martin Mario Almaraz

Bharadwaj Tanikella

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

Disclosed are examples of systems, apparatus, methods and computer program products for providing run-time querying of multi-tenant non-relational database objects. A database system maintains a multi-tenant non-relational database associated with a number of enterprises, a number of records, and a number of data objects for each of the enterprises, each data object having at least one composite key field. A request is received during runtime to query data related to a data object, and the system determines that the data object is defined to be used in associated with a non-relational database. The system then retrieves a metadata model of the data object, determines that the request includes at least one composite key field for the data object, and processes the requery to query data in synchronous fashion. Finally, a query response is provided to the user.

Run-time querying of multi-tenant non-relational platform objects

Systems, methods, and computer-readable media for asynchronous (async) querying are described. In embodiments, a system may convert a user-issued query into a distributed execution instruction set (DEIS), and issue the DEIS to data stores that may have various database structures. The system may obtain database objects from the data stores, and store a result set indicating the obtained database objects in a location specified by the user-issued query. The system may also provide mechanisms to allow users to view progress of their async query jobs and/or cancel ongoing async query jobs. Other embodiments may be described and/or claimed.

Technologies for asynchronous querying

A method is provided for processing a query. In some embodiments, the method includes creating a historical schema defining an ordered set of fields based on a current schema of an object and a field modification of the current schema. The method includes inserting, based on the field modification, a new field at an end of the historical schema and archiving records into a second storage device. The method includes receiving a query, which includes a command specifying a subset of fields and which indicates a non-current field. The method includes searching in the second storage device for archive records having a second schema including the subset of fields and the non-current field. The first and second schemas follow the field order in accordance with the historical schema. The method further includes accessing the subset of fields and the non-current field in accordance with the command.

Archiving objects in a database environment

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

Methods, systems, and devices supporting data archiving are described. A device (e.g., an application server) may identify a set of archiving jobs for a first database (e.g., a relational database), where each archiving job indicates a set of data records for archiving. The device may receive a data record of the set of data records from the first database, where the data record is filtered by an abstraction layer (e.g., removing some information from the data record such that the filtered data record corresponds to a user's view of the data record). For example, the filtered data record may include a subset of the total information for the stored data record. The device may transform this received, filtered data record from a first format to a second format supported by a second database. The device may then send the transformed data record to the second database for storage.

Offloading data to a cold storage database

A learning system is provided to improve the speed of queries in a relational database management system. The learning system may include a query pipeline which utilizes a transform generator and a transform engine to transform uncommitted data corresponding to slow queries, and a query multiplexer to query primary keys of uncommitted data and committed data if the query has been improved by an existing transform.

Execution of queries in relational databases

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.

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.

Efficiently deleting data from objects in a multi tenant database system

Disclosed are examples of systems, apparatus, methods and computer program products for providing application programming interface representation for multi-tenant non-relational database objects. A database system maintains a multi-tenant non-relational database associated with a number of enterprises, a number of records, and a number of data objects for each of the enterprises. A request is received from a user to retrieve a description of a data object from the number of data objects, and in response, the system determines that the data object is defined to be used in associated with a non-relational database, then retrieves a metadata model representing the data object. The system determines that at least one field of the data object is part of a composite key for the data object, then provides data object information to the user, including information about the composite key.

Application programming interface representation of multi-tenant non-relational platform objects

Disclosed are examples of systems, apparatus, methods and computer program products for providing a web application builder framework in a database system. A database system maintains a multi-tenant non-relational database associated with a number of enterprises, a number of records, and a number of data objects for each of the enterprises. A dynamic virtual table is maintained as well, associated with the number of records and number of data objects. A user request is received to define a composite key for a data object. A metadata model is generated representing the data object, and a data definition script is generated. The dynamic virtual table is updated to include one or more virtual columns corresponding to the data definition script, and one or more columns of a shared table in the non-relational database are updated to match the virtual columns.

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San Francisco, CA, United States of America

Jan Asita Fernando