Oracle International Corporation

Microsoft Technology Licensing, LLC

Ying Lu

Zhen Hua Liu

Beda Christoph Hammerschmidt

Douglas James McMahon

Vikas Arora

Sriram Krishnamurthy

Hui Joe Chang

Hui Zhang

Sundeep Abraham

Joshua Jeffrey Spiegel

Srividya Tata

Muralidhar Krishnaprasad

James W Warner

Anand Manikutty

Geeta Arora

Srikrishnan Suresh

Alfonso Colunga Sosa

Karuna Muthiah

Juan R Loaiza

Tirthankar Lahiri

Ananth Raghavan

Ajit Mylavarapu

Aurosish Mishra

Scott M Meyer

Sukhada Pendse

Andrew Joseph Carter

Siddharth Rajendra Shah

Andrew Rodriguez

Rohan Angrish

Shubha Bose

Susan M Kotsovolos

Qin Yu

Denis B Mukhin

Rahul Manohar Kadwe

Shashank Gugnani

Douglas James Mcmahon

Zhen Liu

Douglas Mcmahon

JSON Duality Views are object views that return JDV objects. JDV objects are virtual because they are not stored in a database as JSON objects. Rather, JDV objects are stored in shredded form across tables and table attributes (e.g. columns) and returned by a DBMS in response to database commands that request a JDV object from a JSON Duality View. Through JSON Duality Views, changes to the state of a JDV object may be specified at the level of a JDV object. JDV objects are updated in a database using optimistic lock.

Natively supporting JSON duality view in a database management system

Herein is a self-tuning database management system (DBMS) storing JavaScript object notation (JSON) documents and operating a JSON datatype as native to the DBMS. In an embodiment, a computer hosts a DBMS that executes a data definition language (DDL) statement that defines, in a database dictionary of the DBMS, a JSON document column of a database table that stores JSON documents as instances of the JSON datatype that is native in the DBMS. The DBMS may autonomously set or adjust configuration settings that control behaviors such as a default width of a JSON document column, in lining or not of the JSON document column, kind and scope and duration of indexing of the JSON document column, and/or caching of the JSON document column such as in an in memory columnar unit (IMCU). The DBMS may use the various configuration settings to control how JSON documents and the native JSON datatype are stored and/or processed.

Technique of efficiently, comprehensively and autonomously support native JSON datatype in RDBMS for both OLTP and OLAP

The disclosed embodiments provide a system for processing queries of a graph database. During operation, the system stores intermediate results of the query in a structure comprising rows in a set of tables and links between pairs of rows in different tables in the set of tables. Next, the system tracks, in the structure, representations of data, relationships, and path consistency in the query. The system then applies one or more operations to existing rows in the structure to generate a final result of the query. Finally, the system provides the final result in a response to the query.

Enforcing path consistency in graph database path query evaluation

Herein is acceleration for JavaScript object notation (JSON) documents in a relational database based on multilevel, multifield, multivalued indexing and querying. In an embodiment, into a table in a relational database, a computer stores many hierarchical data objects that respectively contain multiple levels that respectively contain one or more fields. An index is generated for indexed values in fields in at least two of the multiple levels. Based on the index, a database statement that references the table in the relational database and at least one of the indexed fields is executed. Indices herein provide matching to more fields in more levels of hierarchical data objects thereby decreasing or eliminating further filtering by brute force after index access. Matching ordinal positions within an array field needs no further filtration after index access. A single index provides increased spatial locality of index entries for acceleration. Index maintenance is minimized for acceleration.

Technique of comprehensively supporting multi-value, multi-field, multilevel, multi-position functional index over stored aggregately stored data in RDBMS

Techniques are described to improve query evaluation in computer systems. In an embodiment, a system receives a full text query for evaluation against a collection of hierarchically marked data object sets. The query specifies token(s) and context(s) which indicate hierarchical location(s) to match within a queried hierarchical data structure. To evaluate the query, the system determines a) data object set(s) that contain the query specified token(s) using token list(s), and/or b) data object set(s) that contain the query specified context(s) using label list(s).

Evaluating XML full text search

Techniques are described herein for leveraging and extending relational data sharding infrastructure to provide comprehensive support for sharding semi-structured and unstructured data, in addition to the relational data, in a single sharded database system. Specifically, techniques are described in which a catalog server pre-gathers all data it needs to answer fetches from a dynamically created temporary lob. In addition, techniques are provided in which a catalog server encodes shard identifiers in lob locators sent to the client. Techniques are provided for pushing down various operators to shard servers.

Handling semi-structured and unstructured data in a sharded database environment

The present invention relates to autonomous tuning of a data grid of documents in a database. Herein are techniques for storage cells to autonomously maintain local indices and other optimization metadata and algorithms to accelerate selective access into a distributed collection of documents. In an embodiment, each storage cell persists a respective subset of documents. Each storage cell stores, in memory, respective index(s) that map each item to location(s), in one or more documents of the respective subset of documents, where the item occurs. One or more computers execute, based on at least a subset of the indices of the storage cells, a data access request from a database management system. In an embodiment, a cloud of JSON document services provides an easy-to-use, fully autonomous JSON document database that horizontally and elastically scales to deliver fast execution of document transactions and queries without needing tuning by a database administrator.

Technique of comprehensively support autonomous JSON document object (AJD) cloud service

Techniques are provided for de-normalizing semi-structured hierarchical data into a virtual table. At least a portion of semi-structured data document collection is denormalized for improving the execution of queries that involves a traversal of the semi-structured data hierarchy of the semi-structured data document collection, in an embodiment. Based on the extracted schema of the semi-structured data, a de-normalized arrangement is generated, in which the hierarchical relationship of the semi-structured data is converted into a set of columns. The denormalized arrangement is materialized by applying the de-normalized arrangement onto the semi-structured data. The materialized arrangement, the virtual table, may be stored on a persistent storage or kept in volatile memory. The virtual table may be stored in one format on the persistent storage and in another format in the volatile memory. A received query that involves a traversal of the semi-structured data hierarchy is converted to a relational query that can be executed on the virtual table, in an embodiment. The execution of the relational query on the virtual table improves the performance in generating the resulting data set.

Columnar data arrangement for semi-structured data

Growing community of inventors

Sunnyvale, CA, United States of America

Ying Lu