International Business Machines Corporation

Lance Christopher Amundsen

John Matthew Santosuosso

Robert J Bestgen

Richard Dale Hoffman

Daniel Virgil Toft

Kevin James Kathmann

Robert Douglas Driesch, Jr

Abdo Esmail Abdo

Harold Hershey Hall, Jr

Bruce Joseph Ryba

Scott E Highbarger

A method, system, and apparatus are disclosed for processing serialized transactions in parallel while preserving transaction integrity. The method includes receiving a transaction comprising at least two keys and accessing a serialization-independent key ('SI-Key') and a serialization-dependent key ('SD-Key') from the transaction. A value for the SI-Key identifies the transaction as independent of transactions having a different value for the SI-Key. Furthermore, a value for the SD-Key governs a transaction execution order for each transaction having a SI-Key value that matches the SI-Key value associated with the SD-Key value. The method also includes assigning the transaction to an execution group based on a value for the SI-Key. The method also includes scheduling the one or more transactions in the execution group in an order defined by the SD-Key. The execution group may execute in parallel with one or more additional execution groups.

System and method for assigning a transaction to a serialized execution group based on an execution group limit for parallel processing with other execution groups

Method, article of manufacture and apparatus for playing back recorded database activity in a graphical user interface. Chronologically ordered database event information is dynamically displayed in a user interface having user-selectable graphical elements associated with predefined playback controls.

Database monitor replay

A method and apparatus are provided for performing periodic virus scans on a computer system that mitigates utilization of system resources. All objects designated as non-recently used are placed in an archived format and rendered un-executable. The archived objects are omitted from periodic virus scans. At such time as a request for an archive object is received, or access to an archived object is otherwise required, the archived object is converted to a non-archived state. In addition, before the returned object is made available to a requester, the recently converted object is subject to a virus scan on-demand. Accordingly, the periodic virus scan omits all archived objects, which will only be subject to a virus scan upon receipt of request to the archived object.

On demand virus scan

A memory management system is disclosed having a first memory heap module that assigns newly created objects to a first memory category and assigns persistent objects to a second memory category. The memory management system further assigns infrequently accessed persistent objects to a third memory category. A garbage collecting module collects garbage on objects in the first and second memory categories, but does not access objects in the third memory category. An operating system pages inactive objects assigned to the third memory category to a stable store. A user can debug the system, checking how many objects are assigned to the third memory category, and how many objects assigned to the third memory category are not accessed after initial allocation. Objects can be assigned to the third memory category based on selected criteria including time since access, likelihood of future object access, object size, and object priority.

Apparatus, system, and method for improving system performance in a large memory heap environment

Query results and statistics regarding them are generated using a novel representation of an n-attribute relation as an order n relational tensor. Orders of the relational tensor respectively correspond to each of the attributes, and each coordinate along an order relates to a key value of the corresponding attribute. Numeric values are stored in the relational tensor, each numeric value representing a count of tuples having the attribute key values that correspond to the coordinate of the numeric value along the orders of the relational tensor. This storage representation is useful in a variety of contexts for enhancing the performance of a RDBMS system. Specifically, a data-representing relational tensor can be used to produce results for operations such as SQL restriction operations using IN and EXISTS criteria.

Generating in and exists queries using tensor representations

Query results and statistics regarding them are generated using a novel representation of an n-attribute relation as an order n relational tensor. Orders of the relational tensor respectively correspond to each of the attributes, and each coordinate along an order relates to a key value of the corresponding attribute. Numeric values are stored in the relational tensor, each numeric value representing a count of tuples having the attribute key values that correspond to the coordinate of the numeric value along the orders of the relational tensor. This storage representation is useful in a variety of contexts for enhancing the performance of a RDBMS system. Specifically, a data-representing relational tensor can be used to produce results for join operations such as the SQL operations JOIN, LEFT OUTER JOIN, RIGHT OUTER JOIN and FULL OUTER JOIN.

Generating join queries using tensor representations

Query results and statistics regarding them are generated using a novel representation of an n-attribute relation as an order n relational tensor. Orders of the relational tensor respectively correspond to each of the attributes, and each coordinate along an order relates to a key value of the corresponding attribute. Numeric values are stored in the relational tensor, each numeric value representing a count of tuples having the attribute key values that correspond to the coordinate of the numeric value along the orders of the relational tensor. This storage representation is useful in a variety of contexts for enhancing the performance of a RDBMS system. Specifically, a data-representing relational tensor can be used to produce results for tuple grouping operations such as the SQL operations COUNT . . . GROUP BY, SUM . . . GROUP BY, AVG . . . GROUP BY, MIN . . . GROUP BY and MAX . . . GROUP BY.

Generating grouping queries using tensor representations

Query results and statistics regarding them are generated using a novel representation of an n-attribute relation as an order n relational tensor. Orders of the relational tensor respectively correspond to each of the attributes, and each coordinate along an order relates to a key value of the corresponding attribute. Numeric values are stored in the relational tensor, each numeric value representing a count of tuples having the attribute key values that correspond to the coordinate of the numeric value along the orders of the relational tensor. This storage representation is useful in a variety of contexts for enhancing the performance of a RDBMS system. Specifically, in a first aspect of the invention, a tensor representation can be used to generate statistics for a user query so that the relational database system can determine, from among two candidate approaches, an approach to use in processing the user query based on the statistic. Also, a a data-representing relational tensor can be used to produce results for a restrict operation such as the SQL operations DISTINCT, PROJECTION, EQUALS, LESS THAN, LESS THAN OR EQUAL, GREATER THAN, GREATER THAN OR EQUAL and LIKE.

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Rochester, MN, United States of America

Lance Christopher Amundsen