Abstract:
A system for processing multiple queries comprising a preprocessor (126) for processing input data into a preprocessed file structure (130) suitable for being processed by parallel instruction sets; a query analyser (136) for processing a plurality of input queries (602) into parallel instruction sets (340) which are grouped and rearranged into an optimized order; and a GPU query execution engine (138) for distributing the preprocessed input data across multiple GPU cores and executing the parallel instruction sets thereon.
Abstract:
An example technique includes assigning partitions of a dataset of multidimensional points to a plurality of local memory nodes of a multicore machine and using the local memory nodes for a search query to determine similarity matches in the dataset for a given multidimensional point. The using includes parallel searching with the local memory nodes in the assigned partitions to identify candidate similarity matches to the given multidimensional point using indexes derived from the multidimensional points, the parallel searching for each node progressing through a sequence of search distances and providing an ongoing search result for each search distance from the given multidimensional point and regulating an extent of the parallel searching based on the ongoing search results.
Abstract:
Example data management systems and methods are described. In one implementation, a method identifies multiple files to process based on a received query and identifies multiple execution nodes available to process the multiple files. The method initially creates multiple scansets, each including a portion of the multiple files, and assigns each scanset to one of the execution nodes based on a file assignment model. The multiple scansets are processed by the multiple execution nodes. If the method determines that a particular execution node has finished processing all files in its assigned scanset, an unprocessed file is reassigned from another execution node to the particular execution node.
Abstract:
Storing time series data for a search query includes identifying a time series whose representation is to be pre-computed based on available memory storage, pre-computing at least one representation of the identified time series, and storing the at least one representation in the memory storage.
Abstract:
Example embodiments relate to predicting execution times of concurrent queries. In example embodiments, historical data is iteratively generated for a machine learning model by varying a concurrency level of query executions in a database, determining a query execution plan for a pending concurrent query, extracting query features from the query execution plan, and executing the pending concurrent query to determine a query execution time. The machine learning model may then be created based on the query features, variation in the concurrency level, and the query execution time. The machine learning model is used to generate an execution schedule for production queries, where the execution schedule satisfies service level agreements of the production queries.
Abstract:
A method and a request distributor (302) for dispatching data-related requests to application functions (304) connected to a data storage (306). When a first data- related request is received (3: 1 ), a characteristic of the request is determined and a specialized application function (C) is selected (3:2) out of a set of different specialized application functions (304), based on the characteristic of the request. The first data-related request is then dispatched (3:3) to the selected specialized application function for handling of data in the data storage accordingly.