Teradata Us, Inc.

Ncr Corporation

Steven Bart Cohen

Donald Raymond Pederson

John R Catozzi

Paul Laurence Sinclair

Bhashyam Ramesh

Gary Lee Boggs

Philip Jason Benton

Eric M Shank

Wayne R Boyle

Albert O Lam

Gregory Howard Milby

Michael Warren Watzke

Olli Pekka Kostamaa

Yu Long

Sorana Rabinovici

Judy Wu

Guilian Wang

Gary Allen Roberts

Preeti Javaji

Richard H Hanson

Bo H Tan

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

Various embodiments herein each include at least one of systems, methods, and software for generating, storing, and using non-unique secondary indexes of semi-structured data in database tables. One method embodiment includes creating and storing a non-unique secondary index (NUSI) for a database table based on column data stored in rows of the database table. The NUSI in some such embodiments identifying an offset to specific data included in the column data of at least some rows of the database table where the specific data is located. Other embodiments include building a NUSI index that includes values embedded therein.

Non-unique secondary indexing of semi-structured data in databases

A database system may include a storage array that includes a plurality of storage devices configured to store a database. The database system may further include a processor in communication with the memory device. The processor may determine frequency of data values of a first set of data from the database. The frequency of data values are determined at a predetermined data granularity. The processor may also generate a data object to include information indicative of the frequency of data values. The processor may also store the data object in the storage array. A method and computer-readable medium may also be implemented.

Data-temperature-based mapping in a database system

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.

A database system may include a storage array including a plurality of storage devices configured to store database data. The database system may further include a processor in communication with the memory device. The processor may be further configured to determine usage frequency of the database data. The processor may be configured to select a subset of the database data for compression based on the usage frequency. The processor may be further configured to perform the compression of the selected subset of the database data. A method and computer-readable medium may also be implemented.

Data-temperature-based compression in a database system

A database system may implement compression management of tables in the database system. The compression management may include determination of a pattern of usage of various database tables in the database system. Based on this pattern of usage, the database tables may be selected as candidates for compression or decompression at the appropriate time. In one example, the pattern of usage may be based on the contents of a query log of the database system. The compression management may also include evaluation of various compression strategies to apply to a candidate database table. Each compression strategy may be evaluated to determine if application to a database table or a portion of the database table would be beneficial based on various conditions. The compression management may also include consideration of each available compression strategy to be applied solely or in combination with one another.

Selective data compression in a database system

A method for retrieving information from a database includes identifying a cylinder. The cylinder includes a plurality of data segments. The method also includes retrieving the plurality of data segments from the cylinder during a single retrieval operation. In addition, the method includes storing the plurality of data segments in a cache.

System and method for retrieving information from a database

Methods, computer programs, information handling systems, and state machines for performing an atomic write to a data block area are disclosed. The atomic write is an in-place write&#x3e; The method includes receiving one or more data blocks to write to the data block area; and for each data block received: writing the data block to the depot slot; and writing the data block to the data block area after the data block write to the depot slot is completed.

System and method for performing disk write operations by writing to a data depot prior to an in-place write

Implementations of a partitioned database system and a method of using a database system are disclosed. One system implementation includes storage facilities. Each storage facility includes data from database table rows. The database table rows in each storage facility that correspond to a specific database table are logically ordered according to a row identifier (row ID). The row ID includes a first value that is based on one or more columns of the table. The row ID also includes a second value that is based on one or more columns of the table, which may be different from or the same as those on which the first value is based. The first value of the row ID is predominate in determining the order of the rows in the storage facilities. The second value determines the order of those rows with identical first values.

Partitioned database system

A method, computer program, and computer system are disclosed for indexing database table rows. A first set of one or more columns of the database table is chosen. A second set of one or more columns of the database table is chosen. An index record is generated for each unique result of applying a second function to a value corresponding to the second set for the database table rows. In each index record, only a single copy of each unique result of applying a first function to a value corresponding to the first set for the database table rows having the result of applying the second function to a value corresponding to the second set for that index record is included. The first set is not identical to the second set. At least two rows of the database table rows have the same result of applying the second function to a value corresponding to the second set and have the same result of applying the first function to the value corresponding to the first set.

[object Object]

A method, computer system, and computer program are claimed for selecting rows from first and second tables each having rows containing values in columns. In at least the first table, the rows are divided into partitions at least one of which is populated by one or more rows. The method, computer system, and computer program include creating a plurality of partition contexts, each partition context corresponding to a populated partition and storing at least a first value associated with a row. The lowest first value stored by the partition context is determined. Rows with a particular first value are identified by reading the partition contexts, creating a temporary file context corresponding to one of the partition contexts, the temporary file context including location data for a data block of rows in memory, and reading rows in memory.

Row hash match scan join using summary contexts for a partitioned database system

A database system includes a plurality of storage modules, with a first storage module storing rows of a first table and a second storage module storing rows of a second table, and a plurality of access modules adapted to manage access of respective storage modules. A first access module corresponds to the first storage module, and a second access module corresponds to the second storage module. The first access module is adapted to distribute rows of the first table to the second access module in response to a join request, and the first access module is adapted to further distribute row identifiers with the distributed rows. The distributed rows contain one or more columns that are part of one or more join conditions of the join request, but does not contain one or more columns that are in a specified result list of the join request or that are part of a subsequent operation specified in the join request.

Performance of join operations in parallel database systems

A diagnosis system includes procedures to perform various operations, including diagnosis of problems, in a run-time environment recreated from dump information. The dump information is saved in response to a fault occurring in a node, which may be part of a parallel processing system. The diagnosis system may be separate from the parallel processing system or it may be one of the nodes of the parallel processing system. Setting up the run-time environment includes identifying memory sections associated with a task that caused the fault and filling the identified memory sections with data extracted from the dump information. Diagnosis procedures may then be run in the recreated run-time environment. The diagnosis may be the same diagnosis procedures used in a live run-time environment to save time and costs associated with writing such diagnosis procedures.

Growing community of inventors

Redondo Beach, CA, United States of America

Steven Bart Cohen