Oracle International Corporation

Douglas James McMahon

Zhen Hua Liu

Beda Christoph Hammerschmidt

Ying Lu

Vikas Arora

Sriram Krishnamurthy

Hui Joe Chang

Joshua Jeffrey Spiegel

Sundeep Abraham

Chon Hei Lei

Hui Zhang

Srividya Tata

Aurosish Mishra

Geeta Arora

Alfonso Colunga Sosa

Srikrishnan Suresh

Juan R Loaiza

Tirthankar Lahiri

Ananth Raghavan

Ajit Mylavarapu

George Albert Buzsaki

Sukhada Pendse

Shubha Bose

Jesper Andersen

Christine Pae Guthrie

Shasank Kisan Chavan

Ying Hu

Srinath Krishnaswamy

Rajendra Pingte

Chandrasekharan Iyer

Mohammad Shoaib Lari

Aleksandra Czarlinska

Denis B Mukhin

Rahul Manohar Kadwe

Beethoven Cheng

Shubhro Jyoti Roy

Shashank Gugnani

Prakashkumar Thiagarajan

Asha Makur

Herein are fine grained updates to pieces of JavaScript object notation (JSON) documents by database statements that can update, delete, and insert parts of JSON documents. In an embodiment, a computer receives a request that specifies a modification of a JSON document that is stored in a compressed format in persistent storage. The modification adds additional content to the JSON document, and/or replaces an old value in the JSON document with a new value that is not a same size as the old value. The modification is recorded in a change log. The change log is eventually applied to the compressed format of the JSON document in the persistent storage without entirely rewriting the compressed format of the JSON document in the persistent storage.

Supporting piecewise update of JSON document efficiently

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

Data structures and methods are described for converting a text format data-interchange file into size efficient binary representations. A method comprises receiving a request to convert a data-interchange file, comprising a hierarchy of nodes, into a binary file. The method further comprises generating a tree representation of the nodes that reference a plurality of leaf values. The method further comprises, in response to determining that the binary file is to be compressed, embedding relative node jump offsets when generating the tree representation. The method further comprises, in response to determining that the data-interchange file is immutable, deduplicating the plurality of leaf values in a space optimized manner. The method further comprises, in response to determining that the data-interchange file is mutable, deduplicating the plurality of leaf values in a stream optimized manner. The method further comprises storing the deduplicated plurality of leaf values in the binary file.

Technique of reducing size of binary JSON with/without compression

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

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

Described is a system, method, and computer program product to perform bi-directional mapping of hierarchical data (e.g. JSON, XML) to database object types (e.g., user defined database object types).

Bidirectional mapping of hierarchical data to database object types

Functionalities of new data types are enabled for data stored using native data types ('recognized data types') supported by DBMSs. Values having a latent data type may be stored using a recognized data type of a DBMS. That is, a value stored in a database table may be recognized by a DBMS as having a recognized data type (e.g., character string) but the value may also include information that indicates the value conforms to a particular latent data type. DBMSs compile database statements. The database statement can contain multiple expressions that may specify a latent data type or that may have a recognized data type but that also imply latent data types. By determining that various expressions have latent data types, operations that rely on the expressions as having a latent data type may be validated in a query and executed accordingly.

Enabling data format specific database functionalities over existing data types by marking operand values

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

A 'hybrid derived cache' stores semi-structured data or unstructured text data in an in-memory mirrored form and columns in another form, such as column-major format. The hybrid derived cache may cache scalar type columns in column-major format. The structure of the in-memory mirrored form of semi-structured data or unstructured text data enables and/or enhances access to perform path-based and/or text based query operations. A hybrid derived cache improves cache containment for executing query operations. The in-memory mirrored form is used to compute queries in a transactionally consistent manner through the use of an invalid vector that used to determine when to retrieve the transactionally consistent persistent form of semi-structured data or unstructured text data in lieu of the in-memory form.

Techniques for enabling and integrating in-memory semi-structured data and text document searches with in-memory columnar query processing

Growing community of inventors

Redwood City, CA, United States of America

Douglas James McMahon