Teradata Us, Inc.

Philip Jason Benton

Donald Raymond Pederson

Wayne R Boyle

Paul Laurence Sinclair

Louis Martin Burger

Gary Lee Boggs

Steven Bart Cohen

Frederick Stuart Kaufmann

Eric M Shank

Albert O Lam

Bhashyam Ramesh

Tirupathi Prabhu Bellapukonda

Rajendran Kolandasamy

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

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

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.

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San Diego, CA, United States of America

Philip Jason Benton