Teradata Us, Inc.

Ravi Krishnamurthy

Joseph I Chamdani

Jeremy L Branscome

Krishnan Meiyyappan

Kapil Laxmikant Surlaker

Michael Paul Corwin

Liuxi Yang

Hung Viet Tran

James Shau

Alan Lee Beck

Raj Cherabuddi

Hareesh Boinepelli

Jim Crowley

Embodiments of the present invention provide a run-time scheduler that schedules tasks for database queries on one or more execution resources in a dataflow fashion. In some embodiments, the run-time scheduler may comprise a task manager, a memory manager, and hardware resource manager. When a query is received by a host database management system, a query plan is created for that query. The query plan splits a query into various fragments. These fragments are further compiled into a directed acyclic graph of tasks. Unlike conventional scheduling, the dependency arc in the directed acyclic graph is based on page resources. Tasks may comprise machine code that may be executed by hardware to perform portions of the query. These tasks may also be performed in software or relate to I/O.

Methods and systems for run-time scheduling database operations that are executed in hardware

Embodiments of the present invention provide one or more hardware-friendly data structures that enable efficient hardware acceleration of database operations. In particular, the present invention employs a column-store format for the database. In the database, column-groups are stored with implicit row ids (RIDs) and a RID-to-primary key column having both column-store and row-store benefits via column hopping and a heap structure for adding new data. Fixed-width column compression allow for easy hardware database processing directly on the compressed data. A global database virtual address space is utilized that allows for arithmetic derivation of any physical address of the data regardless of its location. A word compression dictionary with token compare and sort index is also provided to allow for efficient hardware-based searching of text. A tuple reconstruction process is provided as well that allows hardware to reconstruct a row by stitching together data from multiple column groups.

Accessing data in column store database based on hardware compatible data structures

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

Embodiments of the present invention provide fine grain concurrency control for transactions in the presence of database updates. During operations, each transaction is assigned a snapshot version number or SVN. A SVN refers to a historical snapshot of the database that can be created periodically or on demand. Transactions are thus tied to a particular SVN, such as, when the transaction was created. Queries belonging to the transactions can access data that is consistent as of a point in time, for example, corresponding to the latest SVN when the transaction was created. At various times, data from the database stored in a memory can be updated using the snapshot data corresponding to a SVN. When a transaction is committed, a snapshot of the database with a new SVN is created based on the data modified by the transaction and the snapshot is synchronized to the memory. When a transaction query requires data from a version of the database corresponding to a SVN, the data in the memory may be synchronized with the snapshot data corresponding to that SVN.

Methods and systems for real-time continuous updates

Embodiments of the present invention provide a database system that is optimized by using hardware acceleration. The system may be implemented in several variations to accommodate a wide range of queries and database sizes. In some embodiments, the system may comprise a host system that is coupled to one or more hardware accelerator components. The host system may execute software or provide an interface for receiving queries. The host system analyzes and parses these queries into tasks. The host system may then select some of the tasks and translate them into machine code instructions, which are executed by one or more hardware accelerator components. The tasks executed by hardware accelerators are generally those tasks that may be repetitive or processing intensive. Such tasks may include, for example, indexing, searching, sorting, table scanning, record filtering, and the like.

Methods and systems for hardware acceleration of database operations and queries

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.

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Sunnyvale, CA, United States of America

Ravi Krishnamurthy