Oracle International Corporation

Aurosish Mishra

Shasank Kisan Chavan

Zhen Hua Liu

Amit Ganesh

Tirthankar Lahiri

Juan R Loaiza

Ajit Mylavarapu

Teck Hua Lee

Sukhada Pendse

Shubha Bose

Vineet Marwah

Garret Frederick Swart

Jesse Kamp

Douglas James McMahon

Allison Holloway

Hui Joe Chang

Parthasarathy Elangadu Raghunathan

Shishir Mathur

Meichun Hsu

Yunrui Li

Boris Klots

Vikas Arora

Ananth Raghavan

Beda Christoph Hammerschmidt

Vasudha Krishnaswamy

Hideaki Kimura

Ying Lu

Roger MacNicol

Kartik Kulkarni

Sundeep Abraham

Kamaljit Shergill

Ken Kumar

Jiaqi Yan

Mohamed Zait

Siva Ravada

Ying Hu

Niloy Mukherjee

Sunil Parrokkaren Chakkappen

Ramesh Kumar

Adam Kociubes

Ekrem S C Soylemez

Eugene Karichkin

Vinita Subramanian

Shubhro Jyoti Roy

Can Tuzla

Shashank Gugnani

Douglas J Mcmahon

Allison L Holloway

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

Techniques for accelerating query execution by optimizing data transfer between storage nodes and database nodes are provided. In one technique, a compute node receives a database statement and transmits a set of one or more selection criteria associated with the database statement to a storage node. Based on the database statement, the storage node retrieves a set of data blocks from storage. Each data block comprises multiple rows of an index-organized table (IOT), each row comprising a key section and a non-key section. The storage node applies the set of selection criteria to a data block, resulting in a modified data block. The storage node generates a modified header data for the modified data block and transmits the modified data block to the compute node.

Accelerating query execution by optimizing data transfer between storage nodes and database nodes

Techniques for adaptive, independent compression of key and non-key sections of rows in index-organized tables (IOTs) are provided. In one technique, an IOT is stored that comprises a plurality of rows, each of which comprises a key section and a non-key section. After storing the IOT, a compression technique is performed on the non-key section of each row in the plurality of rows to generate a plurality of compressed non-key sections. However, none of the key sections of the plurality of rows is compressed. In a related technique, instead of compressing the non-key section of each row, the key section of each row is compressed. In a related technique, both sections are compressed, but using different compression techniques. The compression techniques may be determined based on data access history of the different sections of the rows.

Techniques for adaptive independent compression of key and non-key portions of database rows in index organized tables (IOTs)

Techniques related to cache storage formats are disclosed. In some embodiments, a set of values is stored in a cache as a set of first representations and a set of second representations. For example, the set of first representations may be a set of hardware-level representations, and the set of second representations may be a set of non-hardware-level representations. Responsive to receiving a query to be executed over the set of values, a determination is made as to whether or not it would be more efficient to execute the query over the set of first representations than to execute the query over the set of second representations. If the determination indicates that it would be more efficient to execute the query over the set of first representations than to execute the query over the set of second representations, the query is executed over the set of first representations.

Storage formats for in-memory caches

A shared-nothing database system is provided in which parallelism and workload balancing are increased by assigning the rows of each table to 'slices', and storing multiple copies (“duplicas”) of each slice across the persistent storage of multiple nodes of the shared-nothing database system. When the data for a table is distributed among the nodes of a shared-nothing system in this manner, requests to read data from a particular row of the table may be handled by any node that stores a duplica of the slice to which the row is assigned. For each slice, a single duplica of the slice is designated as the “primary duplica”. All DML operations (e.g. inserts, deletes, updates, etc.) that target a particular row of the table are performed by the node that has the primary duplica of the slice to which the particular row is assigned. The changes made by the DML operations are then propagated from the primary duplica to the other duplicas (“secondary duplicas”) of the same slice.

System and method for an ultra highly available, high performance, persistent memory optimized, scale-out database

A manager file system (MFS), implemented using an OS process, exposes a path in the native file system as the root of its file system (e.g., mount point). The MFS informs the OS kernel that any file system operation whose path contains the mount point as its prefix, should be handed over to the process by invoking an appropriate interface method that the process implements as part of its file system implementation. The MFS manages one or more applications and leverages the authentication mechanism of the OS to provide fined-grained access control of MFS managed applications.

Techniques for using an in-memory only file system as an interface for managing computer systems and user space file systems

A manager file system (MFS) runs as a user space file system. The MFS, implemented using an OS process, exposes a mount point as a communication endpoint to the single process. Mounting, unmounting, and changing configuration of individual database file systems (DBFSs) are done by overloading extended attributes on the mount point. The MFS services all DBFSs mounted at different mount points registered to the single process of the MFS and ensures optimal resource utilization among the DBFSs in the single process while guaranteeing resource isolation.

Scalable framework for managing concurrent accesses to database file systems

A manager file system (MFS) runs as a user space file system. The MFS, implemented using an OS process, exposes a mount point as a communication endpoint to the single process. Mounting, unmounting, and changing configuration of individual database file systems (DBFSs) are done by overloading extended attributes on the mount point. The MFS services all DBFSs mounted at different mount points registered to the single process of the MFS and ensures optimal resource utilization among the DBFSs in the single process while guaranteeing resource isolation. Multiple MFSs may be created to manage sets of DBFSs.

Autonomous deployment of multiple database file systems

A shared-nothing database system is provided in which parallelism and workload balancing are increased by assigning the rows of each table to 'slices', and storing multiple copies ('duplicas') of each slice across the persistent storage of multiple nodes of the shared-nothing database system. When the data for a table is distributed among the nodes of a shared-nothing system in this manner, requests to read data from a particular row of the table may be handled by any node that stores a duplica of the slice to which the row is assigned. For each slice, a single duplica of the slice is designated as the &#x201c;primary duplica&#x201d;. All DML operations (e.g. inserts, deletes, updates, etc.) that target a particular row of the table are performed by the node that has the primary duplica of the slice to which the particular row is assigned. The changes made by the DML operations are then propagated from the primary duplica to the other duplicas (&#x201c;secondary duplicas&#x201d;) of the same slice.

Techniques are introduced herein for maintaining geometry-type data on persistent storage and in memory. Specifically, a DBMS that maintains a database table, which includes at least one column storing spatial data objects (SDOs), also maintains metadata for the database table that includes definition data for one or more virtual columns of the table. According to an embodiment, the definition data includes one or more expressions that calculate minimum bounding box values for SDOs stored in the geometry-type column in the table. The one or more expressions in the metadata maintained for the table are used to create one or more in-memory columns that materialize the bounding box data for the represented SDOs. When a query that uses spatial-type operators to perform spatial filtering over data in the geometry-type column is received, the DBMS replaces the spatial-type operators with operators that operate over the scalar bounding box information materialized in memory.

Techniques for in-memory spatial object filtering

Techniques are described for materializing computations in memory. In an embodiment, responsive to a database server instance receiving a query, the database server instance identifies a set of computations for evaluation during execution of the query. Responsive to identifying the set of computations, the database server instance evaluates at least one computation in the set of computations to obtain a first set of computation results for a first computation in the set of computations. After evaluating the at least one computation, the database server instance stores, within an in-memory unit, the first set of computation results. The database server also stores mapping data that maps a set of metadata values associated with the first computation to the first set of computation results.

Materializing internal computations in-memory to improve query performance

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

Techniques are provided for automatic verification of query results obtained from data formatted different from row-major format. Techniques are also provided for automatic triage of query results obtained from data formatted different from row-major format. In an embodiment, the query results may be obtained from querying data formatted in column-major format. In an embodiment, when the query results are determined to be incorrect, a cause of the error in the query results can be automatically identified.

Automatic verification and triage of query results

Techniques are described herein for maintaining two copies of the same semi-structured data, where each copy is organized in a different format. One copy is in a first-format that may be convenient for storage, but inefficient for query processing. For example, the first-format may be a textual format that needs to be parsed every time a query needs to access individual data items within a semi-structured object. The database system intelligently loads semi-structured first-format data into volatile memory and, while doing so, converts the semi-structured first-format data to a second-format. Because the data in volatile memory is in the second-format, processing queries against the second-format data both allows disk I/O to be avoided, and increases the efficiency of the queries themselves. For example, the parsing that may be necessary to run a query against a cached copy of the first-format data is avoided.

Efficient in-memory DB query processing over any semi-structured data formats

Growing community of inventors

Belmont, CA, United States of America

Aurosish Mishra