Oracle International Corporation

Oracle International Corpration

Chi-Kim Hoang

Tirthankar Lahiri

Sanjay Verma

Mark L McAuliffe

Sherry Listgarten

Sibsankar Haldar

Kirk Meredith Edson

Chih-Ping Wang

Dina Thomas

Subhradyuti Sarkar

Anindya Chandra Patthak

Marie-Anne Neimat

John William Miller

Nagender Bandi

Derek Taylor

Kao Makino

Martin Reames

Qingchun Jiang

Neelam Goyal

Varadarajan Aravamudhan

Ryder B Rishel

Paul Tuck

Susan Cheng

Samuel Drake

Susan Sokeng Cheung

Dilys Thomas

Yu-Han Chou

David Aspinwall

Mark L Mcauliffe

Mark Lawrence Mcauliffe

Susan Cheung

Ryder Rishel

John Miller

Martin A Reames

Columns of a table are stored in either row-major format or column-major format in an in-memory DBMS. For a given table, one set of columns is stored in column-major format; another set of columns for a table are stored in row-major format. This way of storing columns of a table is referred to herein as dual-major format. In addition, a row in a dual-major table is updated 'in-place', that is, updates are made directly to column-major columns without creating an interim row-major form of the column-major columns of the row. Users may submit database definition language (“DDL”) commands that declare the row-major columns and column-major columns of a table.

Combined row and columnar storage for in-memory databases for OLTP and analytics workloads

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

In column domain dictionary compression, column values in one or more columns are tokenized by a single dictionary. The domain of the dictionary is the entire set of columns. A dictionary may not only map a token to a tokenized value, but also to a count ('token count') of the number of occurrences of the token and corresponding tokenized value in the dictionary's domain. Such information may be used to compute queries on the base table.

Column domain dictionary compression

Columns of a table are stored in either row-major format or column-major format in an in-memory DBMS. For a given table, one set of columns is stored in column-major format; another set of columns for a table are stored in row-major format. This way of storing columns of a table is referred to herein as dual-major format. In addition, a row in a dual-major table is updated 'in-place', that is, updates are made directly to column-major columns without creating an interim row-major form of the column-major columns of the row. Users may submit database definition language ('DDL') commands that declare the row-major columns and column-major columns of a table.

A plurality of mid-tier databases form a single, consistent cache grid for data in one or more backend data sources, such as a database system. The mid-tier databases may be standard relational databases. Cache agents at each mid-tier database swap in data from the backend database as needed. Ownership locks maintain consistency in the cache grid. Cache agents prevent database operations that will modify cached data in a mid-tier database unless and until ownership of the cached data can be acquired for the mid-tier database. Cache groups define what backend data may be cached, as well as a general structure in which the backend data is to be cached. Metadata for cache groups is shared to ensure that data is cached in the same form throughout the entire grid. Ownership of cached data can then be tracked through a mapping of cached instances of data to particular mid-tier databases.

Distributed consistent grid of in-memory database caches

A two copy versioning scheme is proposed. The scheme provides efficient implementation for a high read load database and eliminates read dependencies. The versioning scheme provides efficient implementation for transactions executing under ANSI READ COMMITTED isolation, and is extensible to serializable isolation. The two-copy versioning scheme is garbage free meaning that all versions of data records are used during execution and is adapted to index scanning operations. A 'deferred delete' scheme allows read transactions to complete data deallocation operations. Statements that require 'snapshots' are efficiently supported.

Database management system with efficient version control

Clients are permitted to directly attach to a client server system. An oplog management system tracks changes as clients make the changes to control structures of the direct connection client server system. The Oplogging system stores changes in oplogs. Then, if a client fails, the Oplogging system can repair the control structures, either by undoing the changes made by the client, or else completing the changes in place of the client; depending upon principles defined.

Operating logging for online recovery in shared memory information systems

New lock modes are based on the concept of neighborhoods and are applied to spaces in indexes. The new lock modes include a Read, or shared, neighborhood (Snei) lock mode and a Write neighborhood (Xnei) that enhance concurrency among non-serializable transactions.

Neighboring locking technique for increasing concurrency among transactions

Neighborhood locking technique for increasing concurrency among transactions

Growing community of inventors

Palo Alto, CA, United States of America

Chi-Kim Hoang