Oracle International Corporation

Kantikiran Pasupuleti

Nipun Agarwal

Sam Idicula

Sabina Petride

Boris Klots

Benjamin Schlegel

Felix Schmidt

Shenoda Guirguis

Andrea Di Blas

Vikas Aggarwal

John S Kowtko

Techniques are described for efficient query processing and data change propagation to a secondary database system. The secondary database system may execute queries received at a primary database system. Database changes made at the primary system are copied to the secondary system. The primary system receives a query to be executed on either the primary system or the secondary system. The primary system determines whether to send the query to the secondary system based upon whether data objects stored within the secondary system have pending changes that need to be applied to the data objects. The pending changes are stored within in-memory journals within the primary system. The primary system scans for the pending changes to the data objects and sends the pending changes to the secondary system. The secondary system then receives and applies the pending changes to the data objects within the secondary system. Upon applying the pending changes, the secondary system executes the query.

Consistent query execution for big data analytics in a hybrid database

A method and apparatus for adaptive data repartitioning and adaptive data replication is provided. A data set stored in a distributed data processing system is partitioned by a first partitioning key. A live workload comprising a plurality of data processing commands is processed. While processing the live workload, statistical properties of the live workload are maintained. Based on the statistical properties of the live workload with respect to the data set, it is determined to replicate and/or repartition the data set by a second partitioning key. The replicated and/or repartitioned data set is partitioned by the second partitioning key.

Adaptive data repartitioning and adaptive data replication

Techniques related to version control based on a dual-range validity model are disclosed. In an embodiment, an online analytical processing (OLAP) server stores a plurality of version records describing versions of a data item. A version record may describe any open transactions for a version of the data item. The version record may specify a commit timestamp for the data item at a database and a valid timestamp at least as great as the commit timestamp. The commit timestamp and the valid timestamp may specify a validity range. The version record may also specify an expiration timestamp, which along with the valid timestamp may specify an unresolved range. The OLAP server may also identify a valid version of the data item for a query timestamp that corresponds to a query for particular data in the data item and that falls within either the validity range or the unresolved range.

Version control based on a dual-range validity model

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Foster City, CA, United States of America

Kantikiran Pasupuleti