Oracle International Corporation

Hanlin Daniel Chien

Prasad V Bagal

Harish Nandyala

Santhosh Selvaraj

Sameer Arun Joshi

Sha Chang

Ricardo Rey Diez

David Cavazos Woo

Ana L Solis

Emily Ronshien Su

Rajiv Wickremesinghe

Mahanteshwar Chimangala Maheshwar

Hemanth Pikkili

Sahaj Agarwal

Techniques are describe herein for associating storage management properties with a group of files of a database referred to herein as a 'file group'. In this system, storage management properties are defined at the database-level. Thus, multiple databases can be stored across a single disk group, gaining the benefits of having multiple block access devices working in parallel, but each respective database may be associated with a respective file group in a one-to-one relationship, so that each database can have different storage management properties.

Database-level automatic storage management

Duplicate bug report detection using machine learning algorithms and automated feedback incorporation is disclosed. For each set of bug reports, a user-classification of the set of bug reports as including duplicate bug reports or non-duplicate bug reports is identified. Also for each set of bug reports, correlation values corresponding to a respective feature, of a plurality of features, between bug reports in the set of bug reports is identified. Based on the user-classifications and the correlation values, a model is generated to identify any set of bug reports as including duplicate bug reports or non-duplicate bug reports. The model is applied to classify a particular bug report and a candidate bug report as duplicate bug reports or non-duplicate bug reports.

Duplicate bug report detection using machine learning algorithms and automated feedback incorporation

Embodiments enable a database management system (DBMS) to manage two levels of disk failure groups. These two levels of redundancy are achieved by grouping the disks of the disk group for the DBMS into two levels of failure groups (i.e., 'data sites' each containing two or more 'failure groups' of disks). This system of disk grouping allows a DBMS to potentially tolerate the loss of both an entire first site and part of a second site. Such a DBMS uses a multi-level voting system, based on both failure group-level votes and site-level votes, to identify the current version of administrative data structures (ADS) that store key administrative data. In addition to data sites that store database data, the DBMS includes a quorum site with a single quorum failure group that stores a copy of the ADS. The quorum site contributes a site-level vote during a multi-level voting event.

Hierarchical fault tolerance in system storage

A method, system, and computer program product for upgrade procedures in high-availability clustered systems. The method selects fail-over nodes during rolling patch installation and comprises steps for identifying a first node to upgrade, migrating services of the first node to a second node and shutting down the first node to perform its upgrade, then migrating the services back from the second node to the first node. When performing an upgrade of the other nodes of the cluster (e.g., nodes of the cluster other than the first node), rather than migrating the services of the node to be upgraded to another node that still needs to be upgraded, instead migrate the services of the node to be upgraded to a node that has already been upgraded. This reduces the brown-out time at least to the extent that this method incurs only one migration per node rather than two migrations per node.

Dynamic client fail-over during a rolling patch installation based on temporal server conditions

Growing community of inventors

San Jose, CA, United States of America

Hanlin Daniel Chien