Abstract: Queries that reference materialized views may be rewritten to compensate for stale materialized views. A query may be received that references a materialized view. A determination may be made as to whether the materialized view is stale. For a stale materialized view, the query may be rewritten to generate a query plan that obtains changes made to base tables for the materialized view not included in the stale materialized view and considers the change(s) when generating a result for the query from the materialized view. The rewritten query may then be performed to provide a result as if the materialized view were up-to-date.

Abstract: Stored procedures are generated to perform incremental updates to a materialized view for a database. When a request to create a materialized view is received, one or more internal tables are created from the database. A stored procedure is generated that when executed will update the materialized view of the database. The stored procedure may obtain changes to the database that were not included in the internal tables and update the internal tables to include the obtained changes. The stored procedure may be performed automatically and in response to requests.

Abstract: A database system may use a machine learning model creation system to create a machine learning model from data stored in the database system responsive to a request from a client. The database system may obtain an executable version of the machine learning model, based on an uncompiled hardware agnostic version of the machine learning model, according to the hardware configuration of one or more computing resources selected by the database system to perform requests to the database system that invoke the machine learning model to generate predictions.

Abstract: A database system may include a machine learning model which may be used to perform various data analytics for data stored in the database system. In response to a request to invoke the machine learning model to generate a prediction from data stored in the database system, the database system may perform one or more optimization operations, as part of a query plan, to prepare the data to make it suitable for use by the machine learning model.

Abstract: Materialized views for a database system may be automatically refreshed according to performance benefits. Materialized views may be ordered according to determined performance benefits for the materialized views indicating the performance benefit obtained when a materialized view is used to perform a query at the database system. Materialized views may be selected for refresh operations according to the ordering based on a capacity of the database system to perform refresh operations.

Abstract: Match rules for rewriting queries to use materialized views may be dynamically generated by a database system. A database system may generate rules that indicate whether a given query can use a materialized view and how to rewrite the given query to use the materialized view. A query may be received and the rules may be applied to the query to determine that the query can use the materialized view and to rewrite the query to use the materialized view. The rewritten query can then be executed.

Abstract: Stored procedures are generated to perform incremental updates to a materialized view for a database. When a request to create a materialized view is received, one or more internal tables are created from the database. A stored procedure is generated that when executed will update the materialized view of the database. The stored procedure may obtain changes to the database that were not included in the internal tables and update the internal tables to include the obtained changes. The stored procedure may be performed automatically and in response to requests.

Abstract: Stored procedures are generated to perform incremental updates to a materialized view for a database. When a request to create a materialized view is received, one or more internal tables are created from the database. A stored procedure is generated that when executed will update the materialized view of the database. The stored procedure may obtain changes to the database that were not included in the internal tables and update the internal tables to include the obtained changes. The stored procedure may be performed automatically and in response to requests.

Abstract: Algebraic operations are applied to change result sets for source tables to update a materialized view of a database incrementally. A procedure is determined for updating a materialized view that generates result sets for changes to source tables for a materialized view. The procedure performs one or more operations to update the materialized view using the result sets as operands in a relational algebra operator in the procedure to include the changes in the result sets in the materialized view.

Abstract: Databases may enforce foreign key constraints for efficient materialized view updates. A materialized view may be created for a database. Updates to the database may be received and performed according to a foreign key constraint enforced with respect to the updates. An update to the materialized view may be performed to make the materialized view consistent with the updates to the database. Performance of a join operation to update the materialized view that would return a result violating the foreign key constraint may be excluded from the update to the materialized view.

Abstract: Systems and associated methods for highly parallel processing of parameterized simulations are described. Embodiments permit processing of stochastic data-intensive simulations in a highly parallel fashion in order to distribute the intensive workload. Embodiments utilize methods of seeding records in a database with a source of pseudo-random numbers, such as a compressed seed for a pseudo-random number generator, such that seeded records may be processed independently in a highly parallel fashion. Thus, embodiments provide systems and associated methods facilitating quicker data-intensive simulation by enabling highly parallel asynchronous simulations.

Abstract: Systems and associated methods for highly parallel processing of parameterized simulations are described. Embodiments permit processing of stochastic data-intensive simulations in a highly parallel fashion in order to distribute the intensive workload. Embodiments utilize methods of seeding records in a database with a source of pseudo-random numbers, such as a compressed seed for a pseudo-random number generator, such that seeded records may be processed independently in a highly parallel fashion. Thus, embodiments provide systems and associated methods facilitating quicker data-intensive simulation by enabling highly parallel asynchronous simulations.

Abstract: Systems and associated methods for highly parallel processing of parameterized simulations are described. Embodiments permit processing of stochastic data-intensive simulations in a highly parallel fashion in order to distribute the intensive workload. Embodiments utilize methods of seeding records in a database with a source of pseudo-random numbers, such as a compressed seed for a pseudo-random number generator, such that seeded records may be processed independently in a highly parallel fashion. Thus, embodiments provide systems and associated methods facilitating quicker data-intensive simulation by enabling highly parallel asynchronous simulations.

Abstract: Systems and associated methods for highly parallel processing of parameterized simulations are described. Embodiments permit processing of stochastic data-intensive simulations in a highly parallel fashion in order to distribute the intensive workload. Embodiments utilize methods of seeding records in a database with a source of pseudo-random numbers, such as a compressed seed for a pseudo-random number generator, such that seeded records may be processed independently in a highly parallel fashion. Thus, embodiments provide systems and associated methods facilitating quicker data-intensive simulation by enabling highly parallel asynchronous simulations.

Abstract: In one embodiment, a computer-implemented method includes selecting one or more sub-expressions of a query during compile time. One or more pilot runs are performed by one or more computer processors. The one or more pilot runs include a pilot run associated with each of one or more of the selected sub-expressions, and each pilot run includes at least partial execution of the associated selected sub-expression. The pilot runs are performed during execution time. Statistics are collected on the one or more pilot runs during performance of the one or more pilot runs. The query is optimized based at least in part on the statistics collected during the one or more pilot runs, where the optimization includes basing cardinality and cost estimates on the statistics collected during the pilot runs.

Abstract: A computer-implemented method includes partitioning a plurality of records into a plurality of splits. Each split includes at least a portion of the plurality of records. The method further includes providing at least one split of the plurality of splits to a mapper. The mapper scans the input data set, transforms each input record using a map function, and extracts a grouping key in parallel. The method further includes assigning at least a portion the records of the at least one split to a group. Each assignment to the group is based on a strata of the assigned record, and filtering the records of the group. Each filtering is based on a comparison of a weight of a record to a local threshold of the mapper. The method further includes shuffling the group to a reducer and providing a stratified sampling of the plurality of records based on the group.

Abstract: Stratified sampling of a plurality of records is performed. A plurality of records are partitioned into a plurality of splits, wherein each split includes at least a portion of the plurality of records. The split of the plurality of splits is provided to a mapper. The mapper assigns at least a portion the records of the at least one split to a group based on a strata of the assigned records, and filters the records of the group based on a comparison of the weights of the records to a local threshold of the mapper. The mapper updates the local threshold of the mapper by communicating with a coordinator. The mapper shuffles the group to a reducer, where the reducer filters the records of the group based on the weights of the records. The reducer provides a stratified sampling of the plurality of records based on the group.

Abstract: A computer-implemented method includes partitioning a plurality of records into a plurality of splits. Each split includes at least a portion of the plurality of records. The method further includes providing at least one split of the plurality of splits to a mapper. The mapper scans the input data set, transforms each input record using a map function, and extracts a grouping key in parallel. The method further includes assigning at least a portion the records of the at least one split to a group. Each assignment to the group is based on a strata of the assigned record, and filtering the records of the group. Each filtering is based on a comparison of a weight of a record to a local threshold of the mapper. The method further includes shuffling the group to a reducer and providing a stratified sampling of the plurality of records based on the group.

Abstract: An aspect of error handling includes a parsing block for pre-processing a document indexing application, a filtering block for discarding irrelevant documents, a transformation block to clean up and annotate input data by identifying a document language, and a processor configured for grouping inputs to collect documents for a same entity in a single spot. The processor processes a query operation. An aspect of error handling also includes a data package including a data result component that includes data generated based on successful completion of at least a portion of the query operation. The data package also includes an error information data component based on one or more errors encountered during processing of the query operation. An indicator separates the error information data from the data result. The system also includes a memory associated with a distributed file system for storing a final write output relating to the query operation.

Abstract: Embodiments relate to a method and computer program product for error handling. The method includes performing at least one query operation. The processing of query operation also includes generating error information data based at least an error encountered during performance of the query operation and generating a data result relating to any portion of the query operation successfully completed. The data result is processed together with the error information data based on encountering any errors. The data result and error information are provided together in a package but separated by an indicator to distinguish between them.