Oracle International Corporation

Teck Hua Lee

Amit Ganesh

Tirthankar Lahiri

Vineet Marwah

Shasank Kisan Chavan

Allison Holloway

Jesse Kamp

Yunrui Li

Aurosish Mishra

Prashant Singh

Vivekanandhan Raja

Niloy Mukherjee

Sanket Hase

Sujatha Muthulingam

Michael J Gleeson

Krishna Kunchithapadam

Juan R Loaiza

Meichun Hsu

Garret Frederick Swart

Boris Klots

Hugh Zhang

Ajit Mylavarapu

Vasudha Krishnaswamy

Hideaki Kimura

Roger MacNicol

Sukhada Pendse

Kartik Kulkarni

Diar Ahmed

Kevin Chow

Gary Look

Yu Chieh Fu

Zhen Hua Liu

Andrew Witkowski

Hui Joe Chang

Brendon Charles Glazer

Hariharan Lakshmanan

Dennis Lui

You Jung Kim

Shubha Bose

Ritchard Shadian

Chinmayi Krishnappa

Sheldon Lewis

Fan Wu

Vicente Hernandez Jimenez

Sooyeon Jo

Jesus Elohim Martinez Castillo

Sylvia Winters

Vicente Hernandez

Allison L Holloway

Techniques are provided for optimizing storage of database records in segments using sub-segments. A base segment is a container used for storing records that belong to a database object. A database management system receives a request to load, into the database object, a first set of records that are in a first state. In response to receiving the request, the system generates a new sub-segment, which is a container that is separate from the base segment. The system stores the first set of records, in their first state, within the sub-segment. The system then monitors one or more characteristics of the database system. In response to the one or more characteristics satisfying criteria, the system performs a migration of one or more records of the first set of records from the sub-segment to the base segment. During the migration, the system converts the one or more records from the first state to a second state and stores the one or more records, in their second state, in the base segment.

Automatic, online and space efficient clustering and compression of very large database objects

A computer program product, system, and computer implemented method for automatic, repetitive, and gradual shrink of very large database object structures. The approach disclosed herein generally comprises an iterative best efforts approach to automatically shrink a large database object structure(s). The process may comprise identifying a large object (LOB) structure shrink event and LOB structure or LOB structure segment to be shrunk. And executing a shrink operation on the LOB structure or LOB structure segment without blocking access to other extents in the LOB structure or LOB segment structure. This process allows an extent to be shrunk and deallocated. As a result, the process can operate to continually clean a LOB structure or LOB structure segment to decrease fragmentation within the extents of the LOB structure or LOB segment structure.

Automatic, repetitive, and gradual shrink of very large database object structures

Techniques are provided for optimizing storage of database data records in segments using sub-segments. A segment may be comprised of a plurality of extents that contain data blocks and a plurality of references to sub-segments. Sub-segments are containers that contain other extents. A request to insert a set of records into a segment may be received by a database management system. Upon receiving the request, a particular sub-segment is selected to temporarily store the set of records. The set of records are inserted into data blocks belonging to an extent of the sub-segment. Access frequency for the set of records is monitored to determine whether the access frequency is less than a first threshold. When the access frequency of a subset of records is less than a first threshold, the subset of records is compressed and inserted into a particular extent of a particular segment.

Self-optimizing database objects using virtual segments

Described are improved systems, computer program products, and methods for obtaining space usage information within a clustered database system. Some approaches provide an improved algorithm and structure that gives the ability to compute the latest and accurate space usage with only in-memory operations.

Efficient space usage cache on a database cluster

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

An approach is described to provide a method, a computer program product, and a computer system to implement hole punching. The described approach provides an automated way to free up space without requiring manual intervention by a DBA to manually reorganize database objects to free up space.

Automatic reclamation of storage space for database 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 described for querying on hybrid formats and storages by a DBMS to leverage faster mirror format (MF) data and completeness of persistent format (PF) data. In an embodiment, the DBMS receives a query that specifies both in-memory and disk-only columns. The DBMS identifies that a scan-based operation is referencing an in-memory column stored within both MF and PF data. The DBMS may initiate performing the operation on the in-memory column on one of the formats of data. While doing so, the DBMS may determine that the actual improvement metric for the operation on the selected format data is not achieving the desired improvement. If so, the DBMS may dynamically switch the performing of the scan-based operation to the other format of the same data.

Querying on hybrid formats and storages

Row locking is performed at the row level of granularity for database data stored in columnar form. Row level locking entails use of a lock vector that is stored in a compression unit in a data block, the compression unit storing rows in columnar-major format. On an as needed basis, the lock vector is expanded to identify more transactions affecting the rows in the compression unit.

Row level locking for columnar data

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 hybrid parallelization of in-memory table scans. Work for an in-memory scan is divided into granules based on a degree of parallelism. The granules are assigned to one or more processes. The work for each granule is further parallelized by dividing the work granule into one or more tasks. The tasks are assigned to one or more threads, the number of which can be dynamically adjusted.

Efficient hybrid parallelization for in-memory scans

In an approach, the database server generates a request for data representing rows of a table, the request identifying one or more data blocks stored on a storage system. The database server then generates metadata describing one or more filtering conditions to be applied to the rows and sends the metadata along with the request to the storage system. The storage system, when applying filtering conditions to a column containing a LOB, determines whether the LOB is stored in-line or out-of-line. If the column contains an out-of-line LOB, the storage system skips the filtering conditions on the column. If the column contains an in-line LOB, the storage system applies the filtering conditions to the column. Upon obtaining the filtered data from the storage system, the database server retrieves the data blocks for out-of-line LOBs and applies the skipped filtering conditions to create a final result set.

Predicate offload of large objects

Techniques are provided for maintaining data persistently in one format, but making that data available to a database server in more than one format. For example, one of the formats in which the data is made available for query processing is based on the on-disk format, while another of the formats in which the data is made available for query processing is independent of the on-disk format. Data that is in the format that is independent of the disk format may be maintained exclusively in volatile memory to reduce the overhead associated with keeping the data in sync with the on-disk format copies of the data.

Mirroring, in memory, data from disk to improve query performance

Growing community of inventors

Belmont, CA, United States of America

Teck Hua Lee