Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: In accordance with embodiments, there are provided mechanisms and methods for facilitating rules-based synchronous 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 detecting, by a rules-management server computing device, at least one of efficient classes and inefficient classes associated with a query, and designating a set of rules to the query, where one or more of the set of rules are designated to the query to prevent the inefficient classes from being processed or allow the query to fail fast. The method may further include executing the query without processing the inefficient classes such that results are generated within a predictable amount of time, where executing includes accessing contents of one or more portions of a database as identified by the set of rules, and transmitting the results in response to the query.

Abstract: 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.

Abstract: 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.