Oracle International Corporation

Oracle International Corpration

Derek Taylor

Ryder B Rishel

Chi-Kim Hoang

Varadarajan Aravamudhan

Tirthankar Lahiri

Chih-Ping Wang

Stephen Schleimer

John William Miller

Nagender Bandi

Paul Tuck

Susan Sokeng Cheung

Samuel Drake

William S Rishel

Yu-Han Chou

David Aspinwall

Susan Cheung

John Miller

A database is stored as a plurality of database shards in a distributed database grid comprising a plurality of grid elements, each including a mid-tier database system. A first grid element receives, from an application executing in the same memory as a mid-tier database system of the first grid element, a first database transaction including at least one database operation on specific data stored in a first database shard that belongs to the first grid element. The first grid element performs and commits the first database transaction without participation of another grid element of the plurality of grid elements. The first grid element receives a second database transaction that requires access to another database shard that does not belong to the first grid element. Multiple grid elements of the plurality of grid elements perform the second database transaction and commit the second database transaction using a two-phase commit protocol.

Direct-connect functionality in a distributed database grid

An approach for consistent database recovery for distributed database systems uses 'synchronization points'. A synchronization point is a global timestamp for which across all nodes of a distributed database system, the nodes have stored change records for any transaction occurring at and before the synchronization point in persistent logs. Each node may employ asynchronous flushing. However, on a periodic basis, each node coordinates to establish a synchronization point, which may entail ensuring change records for transactions that committed at or before the synchronization point are stored in persistent logs. In effect, a synchronization point represents that any transaction committed at or before the synchronization point has been durably committed.

Providing consistent database recovery after database failure for distributed databases with non-durable storage leveraging background synchronization point

Methods and systems are described for applying the use of shards within a single memory address space. A database request is processed by providing the request from a client to a processor, the processor then distributing the request to multiple threads within a single process but executing in a shared memory address environment, wherein each thread performs the request on a distinct shard, and aggregating the results of the multiple threads being aggregated and returning a final result to the client. By parallelizing operations in this way, the request response time can be reduced and the total amount of communication overhead can be reduced.

Method and system for parallelizing database requests

As described herein, a database machine is provided with specialized hardware that can be used to accelerate the sort function. This hardware lowers the computation cost of performing a raw sort operation over the result rows. The hardware may be embodied in a direct circuit (e.g., ASIC), a programmable circuit (e.g., FPGA), a parallel compute engine (e.g., GPU) or any parallel computer. A hardware-assisted sort procedure provides for the early return of up to K results. This early return feature is critically valuable in database operations because often an entire result set is not required. For requests that require only the first L results, when L&#x3c;=K the query can be satisfied with only a single pass over the data. The hardware- or GPU-assisted sort procedure, referred to herein as 'scraper sort,' may be based on modifications of well-known, existing parallel sort algorithms.

Early return of partial sort results in a database system

Load balancing in a parallel database system is performed using multi-reordering, in which a sequence of multiple processors (two, three, or more) that have small average load (for example, the smallest of any such sequence) is selected to participate in load balancing. In the case of three adjacent low-load processors, the load of all three is evenly distributed over two of them, and the remaining processor becomes a free processor. The free processor is moved adjacent to a high-load processor, the load of which is then shared with the free processor. Data is moved in a 'transaction' so that the view of data is always consistent. The database continues to service requests from a former location while data undergoes a transfer.

Load balancing in parallel database systems using multi-reordering

Growing community of inventors

San Jose, CA, United States of America

Derek Taylor