Abstract: Disclosed are systems and methods for implementing contract-based polymorphic and parallelizable user-defined scalar and aggregate functions. The systems and methods can include receiving a query including a plurality of user-defined functions, parsing the query into a plurality of nodes (e.g., basic operation unit or atomic operator), generating an execution plan that minimizes data transfer between the plurality of nodes, and executing the plan in a distributed environment. Each of the plurality of user-defined functions can correspond to one of a plurality of nodes.

Abstract: Embodiments of the present invention provide a hardware accelerator that assists a host database system in processing its queries. The hardware accelerator comprises special purpose processing elements that are capable of receiving database query/operation tasks in the form of machine code database instructions, execute them in hardware without software, and return the query/operation result back to the host system.

Abstract: Disclosed are systems and methods for implementing contract-based polymorphic and parallelizable user-defined scalar and aggregate functions. The systems and methods can include receiving a query including a plurality of user-defined functions, parsing the query into a plurality of nodes (e.g., basic operation unit or atomic operator), generating an execution plan that minimizes data transfer between the plurality of nodes, and executing the plan in a distributed environment. Each of the plurality of user-defined functions can correspond to one of a plurality of nodes.

Abstract: Embodiments of the present invention provide hardware-friendly indexing of databases. In particular, forward and reverse indexing are utilized to allow for easy traversal of primary key to foreign key relationships. A novel structure known as a hit list also allows for easy scanning of various indexes in hardware. Group indexing is provided for flexible support of complex group key definition, such as for date range indexing and text indexing. A Replicated Reordered Column (RRC) may also be added to the group index to convert random I/O pattern into sequential I/O of only needed column elements.

Abstract: Data can be stored in a memory for an in-memory processing system such the data is available for processing as soon as it is needed to be processed. A first portion and a second portion of the data can be stored in the memory of the in-memory processing system for processing by the in-memory processing system, such that the second portion of the data is stored in the memory before the in-memory processing system completes the processing of the first portion of the data, thereby allowing the in-memory processing system to process the second portion of the data when the processing system is able to process the second portion of the data.

Abstract: Embodiments of the present invention provide hardware-friendly indexing of databases. In particular, forward and reverse indexing are utilized to allow for easy traversal of primary key to foreign key relationships. A novel structure known as a hit list also allows for easy scanning of various indexes in hardware. Group indexing is provided for flexible support of complex group key definition, such as for date range indexing and text indexing. A Replicated Reordered Column (RRC) may also be added to the group index to convert random I/O pattern into sequential I/O of only needed column elements.

Abstract: Embodiments of the present invention provide hardware-friendly indexing of databases. In particular, forward and reverse indexing are utilized to allow for easy traversal of primary key to foreign key relationships. A novel structure known as a hit list also allows for easy scanning of various indexes in hardware. Group indexing is provided for flexible support of complex group key definition, such as for date range indexing and text indexing. A Replicated Reordered Column (RRC) may also be added to the group index to convert random I/O pattern into sequential I/O of only needed column elements.

Abstract: Data can be stored in a memory for in-memory processing system such the data is available for processing as soon as it is needed to be processed. By way of example, first portion and a second portion of the data can be stored in the memory of the in-memory processing system for processing by the in-memory processing system, such that the second portion of the data is stored in the memory before the in-memory processing system completes the processing of the first portion of the data, thereby allowing the in-memory processing system to process the second portion of the data when the processing system is able to process the second portion of the data. Those skilled in the art will appreciate that this processing can, for example, be achieved by providing an execution plan that includes one or more components configured to faceplate storing data in the memory of an in-memory processing system, such the data is available for processing as soon as it is needed to be processed by the in-memory processing system.

Abstract: Embodiments of the present invention provide a hardware accelerator that assists a host database system in processing its queries. The hardware accelerator comprises special purpose processing elements that are capable of receiving database query/operation tasks in the form of machine code database instructions, execute them in hardware without software, and return the query/operation result back to the host system.

Abstract: Embodiments of the present invention provide for batch and incremental loading of data into a database. In the present invention, the loader infrastructure utilizes machine code database instructions and hardware acceleration to parallelize the load operations with the I/O operations. A large, hardware accelerator memory is used as staging cache for the load process. The load process also comprises an index profiling phase that enables balanced partitioning of the created indexes to allow for pipelined load. The online incremental loading process may also be performed while serving queries.

Abstract: Embodiments of the present invention provide for batch and incremental loading of data into a database. In the present invention, the loader infrastructure utilizes machine code database instructions and hardware acceleration to parallelize the load operations with the I/O operations. A large, hardware accelerator memory is used as staging cache for the load process. The load process also comprises an index profiling phase that enables balanced partitioning of the created indexes to allow for pipelined load. The online incremental loading process may also be performed while serving queries.

Abstract: Embodiments of the present invention provide for batch and incremental loading of data into a database. In the present invention, the loader infrastructure utilizes machine code database instructions and hardware acceleration to parallelize the load operations with the I/O operations. A large, hardware accelerator memory is used as staging cache for the load process. The load process also comprises an index profiling phase that enables balanced partitioning of the created indexes to allow for pipelined load. The online incremental loading process may also be performed while serving queries.

Abstract: Embodiments of the present invention generate and optimize query plans that are at least partially executable in hardware. Upon receiving a query, the query is rewritten and optimized with a bias for hardware execution of fragments of the query. A template-based algorithm may be employed for transforming a query into fragments and then into query tasks. The various query tasks can then be routed to either a hardware accelerator, a software module, or sent back to a database management system for execution. For those tasks routed to the hardware accelerator, the query tasks are compiled into machine code database instructions.

Abstract: Embodiments of the present invention provide for batch and incremental loading of data into a database. In the present invention, the loader infrastructure utilizes machine code database instructions and hardware acceleration to parallelize the load operations with the I/O operations. A large, hardware accelerator memory is used as staging cache for the load process. The load process also comprises an index profiling phase that enables balanced partitioning of the created indexes to allow for pipelined load. The online incremental loading process may also be performed while serving queries.

Abstract: Embodiments of the present invention provide hardware-friendly indexing of databases. In particular, forward and reverse indexing are utilized to allow for easy traversal of primary key to foreign key relationships. A novel structure known as a hit list also allows for easy scanning of various indexes in hardware. Group indexing is provided for flexible support of complex group key definition, such as for date range indexing and text indexing. A Replicated Reordered Column (RRC) may also be added to the group index to convert random I/O pattern into sequential I/O of only needed column elements.

Abstract: Embodiments of the present invention provide processing elements that are capable of performing high level database operations in hardware based on machine code instructions. These processing elements employ a dataflow architecture that operates on data in hardware without interruption or software. A scanning/indexing processing element may comprise logic that analyze database column groups stored in local memory, perform parallel field extraction and comparison, and generates a list of row pointers (row ids or RIDs) referencing those rows whose value(s) satisfy an applied predicate. The scanning/indexing processing may also be used to project database column groups, search and join index structures, and manipulate in-flight metadata flows, composing, merging, reducing, and modifying multi-dimensional lists of intermediate and final results.