Teradata US, Inc.

Ncr Corporation

Teradota US, Inc.

Donald Raymond Pederson

Philip Jason Benton

Bhashyam Ramesh

Paul Laurence Sinclair

Steven Bart Cohen

Douglas Paul Brown

Frederick Stuart Kaufmann

Wayne R Boyle

John Mark Morris

Louis Martin Burger

Gary Lee Boggs

James P Chapman

Kuorong Chiang

Eric M Shank

Fred S Kaufmann

May Pederson

Albert O Lam

Anita Richards

Gregory Howard Milby

Awny Kayed Al-Omari

Michael Warren Watzke

Olli Pekka Kostamaa

Stephen Andrew Brobst

Todd A Walter

Manjula Koppuravuri

John Earl Merritt

Arthur Vargas Lopes

Gregory Paul Ferguson

Stephen Sheldon

Pradeep Sathyanarayan

David Tung

Mark Anthony Hodgens

Gary Allen Roberts

Tirunagari Venkata Rama Krishna

Srinath Boppudi

Jerry Lynn Klindt

Eric Thomas Potter

Louis Benton Landry

Allen N Diaz

Tirupathi Prabhu Bellapukonda

Jeremy Davis

Oliver Ratzesberger

Cuong Nhan

James Pierce

Sunanda Reddy

Rajendran Kolandasamy

Bill Huffman

Stanley S Chiu

Richard Pogue

Arthur Clark

In some examples, a system sends a transaction to a database server to cause storing of data of the transaction in a cache of the database server, where the data in the cache is for inclusion in a backup of data from the database server to a remote data store (e.g., the backup may be in a cloud and may be a snapshot). The system detects a failure associated with the database server, and in response to detecting the failure, requests, from the database server or a replacement database server, transaction information of at least one transaction that was successfully applied to the remote data store, the transaction information based on the backup of data. The system causes replay one or more transactions to recover data at the database server or the replacement database server, to perform recovery of the database server or the replacement database server to a current state.

Transaction recovery from a failure associated with a database server

An Application Programming Interface (API) processes object storage requests in an object storage format that are received from client applications. A mapper maps the object storage formatted requests into database statements and executes the statements within a database management system. Results returned from the statements are mapped by the mapper to object-storage constructs and provided to the API in object storage format. The object storage formatted requests are provided from the API to the client applications to satisfy the original client applications' requests.

Seamless integration between object-based environments and database environments

A query that is frequently processed to access an object storage is identified. Results from the query returned from the object storage is transformed into a relational database format as a materialized view. When the query is submitted a subsequent time, updated results are managed from the materialized view, other materialized views, and/or the object storage when needed.

Point in time consistent materialization for object storage environments

A data-changing request is received on a first data source system. Metadata for a data source object associated with the request is obtained. The request is executed on a second data source system and results are obtained. A selection between multiple replication processes is made based on the metadata and the results. The selected process is performed on each of the remaining data source systems to synchronize/replicate data changes on the remaining data source systems. In an embodiment, bidirectional and concurrent data connections are permitted to a source object for one data source system and a replicated version of the source object on a different source system. In an embodiment, each data source system includes an agent that establishes an out-of-band connection to a synchronization/replication server, such that synchronization/replication is processed by the server without an affirmative connection being made from the data source systems to the server.

Methods and system for optimal multi-environment data synchronization/replication

A database includes a Value List Compression (VLC) predicate evaluator. A table identified in a query that is being processed is identified as having compressed data values. The predicate evaluator compares a query predicate of the query against actual decompressed values noted in a dictionary for the table and the predicate evaluator maintains a bitmap for selective ones of the actual values that satisfy the query predicate. The matched bitmap positions are processed against an index maintained in the table for the actual values to provide selective decompressed table entries as results for the query.

Value list compression (VLC) aware qualification

Search spaces for obtaining query execution plans for a query are identified. The search spaces are subdivided into sub-search spaces. Searches are initiated within the sub search spaces and plan costs for competing query execution plans are noted along with search costs associated with continuing to search the sub-search spaces. A decision is made based on the plan costs and search costs for utilizing search resources as to when to terminate the searching and to select the then-existing lowest cost query execution plan as an optimal query execution plan for executing the query.

Query plan searching and optimization

Database processing engines of a single cluster are configured such that each engine is a primary engine and a dedicated fallback engine to one other engine of the cluster. In an embodiment, the cluster includes more than two processing engines.

Dedicated fallback processing for a distributed data warehouse

Data portions of a database can be grouped and ranked in order of priory for reassignment from one or more maps to another one or more maps. It should be noted that a first map can assign the data portions to a first configuration of processors for processing the data portions, and a second map assigns the data portions to a second configuration of processors, different than the first configuration, for processing the data portions in a database system and/or environment. The data portions are reassigned in groups during an available time (window) for reassignment by taking the first one of the groups can be reassigned ('moved') in the available, then the second one in the available reaming time, and so on, until no group of data portions can be moved in the remaining time or all of them have been moved.

Timing of reassignment of data to different configurations of processing units in database systems

An order (or priority) for reassigning data portions of a database from a first map to a second map can be determined. It should be noted that the first map assigns the data portions to a first configuration of processors for processing the data portions, and the second map assigns the data portions to a second configuration of processors, different than the first configuration, for processing the data portions in a database system and/or environment. The order can be determined by grouping the data in multiple groups and ordering the groups in accordance with a determined priority for reassignment. In doing so, one or more relationships between the data portions and/or cost of use for each one the groups can be considered.

Reassignment of data to different configurations of processing units in database systems

Data or one or more operations can be provided, based on one or more characteristics associated with the data and/or operations, to a designated computing group or pool of computing resources designated for handling the data and/or operations with the particular data characteristic(s). The designated computing group can, for example, be one of multiple computing groups in the same system or device. As such, all of the computing groups can still function together in the same system or device, for example, in parallel. However, each one of the multiple computing groups can, for example, be defined or predefined to include one or more computing resources that are more suitable for storing and/or processing data with one or more data characteristics or handle operations with one or more determined characteristics.

Designated computing groups or pools of resources for storing and processing data based on its characteristics

A combination of non-persistent-based and persistent-based schemes are used to effectively manage volatile storages which are conventionally managed solely by using non-persistent schemes (e.g., LRU schemes in cache memory). Generally, the persistent-based schemes can be based on persistent information associated with a non-volatile storage environment (e.g., persistent data temperatures associated with data stored in non-volatile storages). In this context, a persistent-based scheme can, for example, be effectively used in addition or combination with a conventional scheme provided for volatile memory. By way of example, a LRU scheme can be combined with a scheme based on persistent data temperatures in order to more effectively manage cache memory provided to enhance the performance of a system. As another example, a LRU, a LFU aging schemes can be combined with a scheme based on persistent data temperatures.

Management of data in multi-storage systems that can include non-volatile and volatile storages

Techniques for management of data in multi-storage systems allow managing storage of data in one of the multiple storages (e.g., a HDD) based on the information associated with the storage and/or access of data (e.g., logical hits of data in a cache memory) in another one of the storages (e.g., a cache memory) of the multi-storage system. In one aspect, information associated with storage and/or access of data in a volatile (or non-persistent) storage environment is provided for managing the storage of data in a non-volatile (or persistent) storage environment. By way of example, data access information associated with a volatile storage environment (e.g., logical hits of data in a cache) can be effectively communicated to a non-volatile storage environment, for example, as information that can be indicative of data temperatures. Moreover, the information obtained from a non-volatile storage environment can be persistently stored for and/or in the non-volatile environment (e.g., stored as persistent data temperatures), thereby preserving and allowing the information to be used to more effectively manage the storage of the data in the non-volatile storage itself and/or the multi-storage system including the (volatile storage) as a whole.

Techniques for management of data in multi-storage systems allow managing storage of data in a first storage of multiple storages in a multi-storage system based on the temperature of the data (or data temperature) obtained for a second storage of the multiple storages in a multi-storage system. For example, in a multi-storage system that includes at least one non-volatile storage (e.g., one or more HDDs) and at least one volatile storage (e.g., a cache memory device), the storage of a particular data portion (e.g., a storage unit of data) for the at least one volatile storage can be managed based on a temperature of the data determined for the non-volatile storage. By way of example, persistent temperatures used for management of data in a non-volatile storage system (e.g., a multi-tier storage of a database system) can be used to effectively manage the storage of the data in a cache memory that is typically provided for improving performance by keeping data that is likely to be used in the cache memory. As a result, overall performance of a system can be further enhanced by allowing persistent temperature measurements to be used in managing cache memory, instead of solely relying on conventional techniques for caching data which typically do not take into account persistent temperatures associated with a non-volatile storage environment.

Techniques for finding a column with column partitioning are provided. Metadata for a container row is expanded to include information for searching ranges of partitioned column values. The metadata identifies offsets to specific ranges and specific columns within a specific range. The offsets also identify where compressed data for a desired column resides. Thereby, permitting partitioned columns having compressed data to be located without being decompressed and decompressed on demand as needed.

Techniques for finding a column with column partitioning

A computer-implemented method, apparatus and article of manufacture for performing an automatic error recovery in a database system. Automatic error recovery is performed for a query execution plan, following errors, problems or failures that occur during execution, by automatically or manually deactivating and/or activating components, features or code paths, and then re-submitting the query execution plan for execution in the computer system.

Query execution plan revision for error recovery

Growing community of inventors

San Diego, CA, United States of America

Donald Raymond Pederson