Teradata Us, Inc.

James Shau

Joseph I Chamdani

Krishnan Meiyyappan

Jeremy L Branscome

Kapil Laxmikant Surlaker

Michael Paul Corwin

Hung Viet Tran

Ravi Krishnamurthy

Liuxi Yang

Robert Matthew Wehrmeister

Alan Lee Beck

Robert Hou

Xin Tang

Ravindran Krishnamurthy

Daniel T Yu

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.

Implementing contract-based polymorphic and parallelizable SQL user-defined scalar and aggregate functions

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.

Methods and systems for hardware acceleration of database operations and queries for a versioned database based on multiple hardware accelerators

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.

Accessing data in a column store database based on hardware compatible indexing and replicated reordered columns

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.

Memory management for in-memory processing computing environments and systems

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.

Fast batch loading and incremental loading of data into a database

Fast bulk loading and incremental loading of data into a database

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Saratoga, CA, United States of America

James Shau