Oracle International Corporation

Google Inc.

Hewlett-packard Development Company, L.p.

Facebook, Inc.

Foley & Lardner Llp

Sachin Kulkarni

Amit Ganesh

Vikram Kapoor

Vineet Marwah

Kam Shergill

Jesse Kamp

Roger MacNicol

Manosiz Bhattacharyya

Sarvar M Patel

Sanjeev Kumar

Bin Zhang

Rajas Moonka

William Kevin Wilkinson

Philip Andrew McDonnell

Anshul Mohan Gupta

Damian John Reeves

Shobhit Saxena

Benjamin Kreuter

Karn Seth

Harry Li

Thai Duong

Laurent Demailly

Liat Atsmon Guz

Roger Dermot Macnicol

Prajakta Kalekar

Vinay Goel

Patrick McDonald

Mihaela Ion

Jesse Kemp

A database server stores compressed units in data blocks of a database. A table (or data from a plurality of rows thereof) is first compressed into a 'compression unit' using any of a wide variety of compression techniques. The compression unit is then stored in one or more data block rows across one or more data blocks. As a result, a single data block row may comprise compressed data for a plurality of table rows, as encoded within the compression unit. Storage of compression units in data blocks maintains compatibility with existing data block-based databases, thus allowing the use of compression units in preexisting databases without modification to the underlying format of the database. The compression units may, for example, co-exist with uncompressed tables. Various techniques allow a database server to optimize access to data in the compression unit, so that the compression is virtually transparent to the user.

Storing compression units in relational tables

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing non-uniform datasets. In one aspect, a method includes receiving multiple different datasets that have non-uniform labeling conventions and that are not organized in a hierarchical structure, organizing the multiple different datasets that are not organized in a hierarchical structure, into a predefined hierarchical taxonomy, including, analyzing text included in the dataset, assigning the dataset to one or more categories within the predefined hierarchical taxonomy based on the analysis of the textual content and one or more other characteristics of the dataset including a number of identification entries included in the dataset, selecting content for display within the content interface at the user device using one or more of the multiple different datasets that were organized into the predefined hierarchical taxonomy, providing, through the user frontend, the selected content for presentation at the user device.

Processing non-uniform datasets

A method for linking user identifiers. The method includes receiving, by a linking server and from a company server, a first data structure comprising entries. Each entry comprising a user tag, a first hashed user information, and a second hashed user information. The method includes receiving, by a linking server and from a partner server, a second data structure comprising entries. Each entry comprising a bridge tag, a first hashed bridge information, and a second hashed bridge information. The method includes retrieving, by the linking server and from the first data structure, a first entry of the entries and retrieving, by the linking server and from the second data structure, a first entry of the entries. The method includes linking the user tag of the first entry of the first data structure to the bridge tag of the first entry of the second data structure to generate a first linkage.

Establishing a link between identifiers without disclosing specific identifying information

A method and apparatus is provided for optimizing queries received by a database system that relies on an intelligent data storage server to manage storage for the database system. Storing compression units in hybrid columnar format, the storage manager evaluates simple predicates and only returns data blocks containing rows that satisfy those predicates. The returned data blocks are not necessarily stored persistently on disk. That is, the storage manager is not limited to returning disc block images. The hybrid columnar format enables optimizations that provide better performance when processing typical database workloads including both fetching rows by identifier and performing table scans.

Query and exadata support for hybrid columnar compressed data

Disclosed are a method and system for recovering a distributed system from a failure of a data storage unit. The distributed system includes a plurality of computer systems, each having a read-write computer and a data storage unit. Data is replicated from a particular data storage unit to other data storage units using publish-subscribe model. A read-write computer receives the replicated data, processes the data for any conflicts and stores it in the data storage unit. If a data storage unit fails, another data storage unit that has latest data corresponding to the failed data storage unit is determined and the latest data is replicated to other data storage units. Accordingly, the distributed system continues to have the data of the failed data storage unit. The failed data storage unit may be reconstructed using data from one of the other data storage units in the distributed system.

Data recovery in multi-leader distributed systems

Techniques are described herein for automatically selecting the compression techniques to be used on tabular data. A compression analyzer gives users high-level control over the selection process without requiring the user to know details about the specific compression techniques that are available to the compression analyzer. Users are able to specify, for a given set of data, a 'balance point' along the spectrum between 'maximum performance' and &#x201c;maximum compression&#x201d;. The point thus selected is used by the compression analyzer in a variety of ways. For example, in one embodiment, the compression analyzer uses the user-specified balance point to determine which of the available compression techniques qualify as &#x201c;candidate techniques&#x201d; for the given set of data. The compression analyzer selects the compression technique to use on a set of data by actually testing the candidate compression techniques against samples from the set of data. After testing the candidate compression techniques against the samples, the resulting compression ratios are compared. The compression technique to use on the set of data is then selected based, in part, on the compression ratios achieved during the compression tests performed on the sample data.

Compression analyzer

A highly flexible and extensible structure is provided for physically storing tabular data. The structure, referred to as a compression unit, may be used to store tabular data that logically resides in any type of table-like structure. According to one embodiment, compression units are recursive. Thus, a compression unit may have a 'parent' compression unit to which it belongs, and may have one or more 'child' compression units that belong to it. In one embodiment, compression units include metadata that indicates how the tabular data is stored within them. The metadata for a compression unit may indicate, for example, whether the data is stored in row-major or column major-format, the order of the columns within the compression unit (which may differ from the logical order of the columns dictated by the definition of their logical container), a compression technique for the compression unit, the child compression units (if any), etc.

Structure of hierarchical compressed data structure for tabular data

Embodiments include methods, apparatus, and systems for using range queries in multidimensional data in a database. One embodiment is a method that defines a query box from a search for multidimensional data in a database. The method examines an intersection between a Z-interval and the query box by decomposing the Z-interval into hyper-boxes that are also Z-intervals in Z space. Items in the query box that match the search are discovered.

Methods and apparatus using range queries for multi-dimensional data in a database

Techniques for storing and manipulating tabular data are provided. According to one embodiment, a user may control whether tabular data is stored in row-level or column-major format. Furthermore, the user may control the level of data compression to achieve an optimal balance between query performance and compression ratios. Tabular data from within the same table may be stored in both column-major and row-major format and compressed at different levels. In addition, tabular data can migrate between column-major format and row-major format in response to various events. For example, in response to a request to update or lock a row stored in column-major format, the row may be migrated and subsequently stored into row-major format. In one embodiment, table partitions are used to enhance data compression techniques. For example, compression tests are performed on a representative table partition, and a compression map is generated and applied to other table partitions.

DDL and DML support for hybrid columnar compressed tables

Growing community of inventors

Sunnyvale, CA, United States of America

Sachin Kulkarni