Abstract: Embodiments include a method, system, and computer program product for creating an array table. In one embodiment the method includes identifying keys associated with values in a database and identifying bits common between the plurality of keys using logical functions and removing the common hits to form condensed keys. The method also includes modulating the condensed keys using identified common bits to create transformed keys and populating the plurality of array tables using the transformed keys and associated values.

Abstract: Embodiments relate to data compression using dictionary encoding. An aspect includes subdividing a table of uncompressed data into a first block and a second block of complete rows. Another aspect includes determining information about a frequency of occurrence of different values for each column of the first block. Another aspect includes selecting a row of the first block to be removed out of the first block using frequency of occurrence-information. Another aspect includes removing the a row out of the first block to form an updated first block and determining information about a frequency of occurrence of different values for each column of the updated first block. Another aspect includes deriving a dictionary containing code-words for encoding the values of the updated first block. Another aspect includes encoding the values of the updated first block based on the code-words. Another aspect includes adding the removed row to the second block.

Abstract: Embodiments include method, system, and computer program product for providing aggregation hierarchy that is related memory hierarchies. In one embodiment, the method includes determining capacity of a first level memory of a memory hierarchy for processing data relating to completion of an aggregation process and generating a per thread local look-up table in said first level memory upon determining said capacity. Upon the first level memory reaching capacity, a plurality of per thread partitions to store remaining data to complete the aggregation process in a second level memory of the memory hierarchy is generated such that each of said per-thread partitions includes an identical amount of data portion on each thread. The method also includes storing the per thread partitions in said second level memory and providing a single global look up table for each of the identical data portions.

Abstract: Embodiments include a method and computer program product for encoding data while it is being processed as part of a query is provided. The method includes receiving a query request and determining a set of values associated with data to be encoded for completing the query request. The method also includes encoding those values such that any subsequent processing operations can be performed on the encoded values to complete the requested query. After performing the subsequent processing operations to complete the requested query, each value is decoded back to its original value.

Abstract: Collisions in hash tables are reduced by removing each empty bucket from a hash table and compacting the non-empty buckets, generating a map of the hash table indicating a status of the buckets of the hash table, and accessing data in the hash table by applying a hash key to the generated map to determine a corresponding bucket containing the data.

Abstract: Partitioned join operations are performed between a first database object and a second database object by determining an agent group for an agent in response to the agent receiving rows of the second database object to process; partitioning the rows to determine a target hash table for each row and adding the partitioned rows to work to be performed by the agent group; and distributing the work for the group to agents of the group by assigning to a single agent all the rows associated with a particular hash table to perform a join operation on the assigned rows. Each partition is assigned a first counter value indicating an upper bound of a task id range that is most recently assigned to an agent in the agent group for processing, and a second counter value indicating the highest task id that has been processed for that partition.

Abstract: Embodiments include a method, system, and computer program product for creating an array table. In one embodiment the method includes identifying keys associated with values in a database and identifying bits common between the plurality of keys using logical functions and removing the common bits to form condensed keys. The method also includes modulating the condensed keys using identified common bits to create transformed keys and populating the plurality of array tables using the transformed keys and associated values.

Abstract: Embodiments include a system for encoding data while it is being processed. The system includes a processor, an encoder and a decoder. The processor is configured to process a query request by determining a set of values. The encoder is configured for encoding the set of values, such that a subsequent processing operation can be performed on the encoded values. The processor performs the subsequent processing operations. The decoder is configured for decoding each value back to its value prior to being encoded upon completion of the processor completing the requested query.

Abstract: Embodiments of the present invention provide query processing for column stores by accumulating table record attributes during application of query plan operators on a table. The attributes and associated attribute values are compacted when said attribute values are to be consumed for an operation in the query plan, during the execution of the query plan. Table column record values are materialized late in query plan execution.

Abstract: Embodiments of the present invention provide query processing for column stores by accumulating table record attributes during application of query plan operators on a table. The attributes and associated attribute values are compacted when said attribute values are to be consumed for an operation in the query plan, during the execution of the query plan. Table column record values are materialized late in query plan execution.

Abstract: An approach is provided in which a processor receives a scan request to scan data included in a data table. The processor selects a column in the data table corresponding to the scan request and retrieves column data entries from the selected column. In addition, the processor identifies the width of the selected column and selects a scan algorithm based upon the identified column width. In turn, the processor loads the column data entries into column data vectors and computes scan results from the column data vectors using the selected scan algorithm.

Abstract: An approach is provided in which a processor receives a scan request to scan data included in a data table. The processor selects a column in the data table corresponding to the scan request and retrieves column data entries from the selected column. In addition, the processor identifies the width of the selected column and selects a scan algorithm based upon the identified column width. In turn, the processor loads the column data entries into column data vectors and computes scan results from the column data vectors using the selected scan algorithm.

Abstract: A method for approximating a validity range for a domain of cardinalities of input to an optimal query plan is provided. Such a validity range is iteratively approximated using a modified Newton-Raphson method to find roots of cost functions for optimal and alternative query plans, respectively. The Newton-Raphson method is combined with a method of incrementing roots of cost functions, known as input cardinalities, such that discontinuous and non-differentiable points in cost functions are avoided. In this manner, input cardinalities remain within a domain for which a valid range can be specified. Additionally, a robustness measure is determined by a sensitivity analysis performed on an approximated validity range. Using a robustness measure provided by a sensitivity analysis and resultant validity range and, query plan sub-optimality detection is simplified, re-optimization is selectively triggered, and robustness information is provided to a system or user performing corrective actions.

Abstract: Collisions in hash tables are reduced by removing each empty bucket from a hash table and compacting the non-empty buckets, generating a map of the hash table indicating a status of the buckets of the hash table, and accessing data in the hash table by applying a hash key to the generated map to determine a corresponding bucket containing the data.

Abstract: Embodiments of the invention relate to processing queries that utilize fact and/or dimension tables. In one aspect, a pre-join filtering phase precedes a star join. The necessary conditions for the pre-join filtering are considered for a given SQL query, including an estimated size of the hash table exceeding a threshold and presence of a local predicate either on the fact table or one or more dimension tables that is not a large dimension table. Once the necessary conditions are satisfied, the execution of the query exploits the pre-join filtering to build a pre-join output filter from columns of a reduced fact table that joins with each large dimension table. Thereafter, all the dimension tables and the fact table are joined in a star join while exploiting each pre-join filter. Accordingly, the order of when joins occur is changed in order to reduce the size of the fact table and to work from the fact table to reduce the size of large dimension tables.

Abstract: Providing database support. A first group of data are received, the first group of data being expressed in a first format, and a second group of data are received, the second group of data being expressed in a second format, the second format being different from the first format. The first and second groups of data are merged, and are represented in at least one common column. Such representing includes: maintaining the first and second formats; and providing a tuple map which provides reference to the first and second formats.

Abstract: Embodiments of the invention relate to processing queries. A query operation to be performed on a table of data is translated into a series of bit level logical operations using expansion and/or saturation operations. A mask is created from the series of bit level logical operations. This mask is then simultaneously applied to multiple rows from the table of data.

Abstract: Partitioned join operations are performed between a first database object and a second database object by determining an agent group for an agent in response to the agent receiving rows of the second database object to process; partitioning the rows to determine a target hash table for each row and adding the partitioned rows to work to be performed by the agent group; and distributing the work for the group to agents of the group by assigning to a single agent all the rows associated with a particular hash table to perform a join operation on the assigned rows. Each partition is assigned a first counter value indicating an upper bound of a task id range that is most recently assigned to an agent in the agent group for processing, and a second counter value indicating the highest task id that has been processed for that partition.

Abstract: A system is described for creating compact aggregation working areas for efficient grouping and aggregation using multi-core CPUs. The system implements operations including computing a running aggregate for a group within a business intelligence (BI) query, and identifying a location to store running aggregate information within an aggregation working area of a cache. The aggregation working area includes first and second data structures. The first data structure stores running aggregate information that is associated with a group that is accessed frequently relative to a threshold. The second data structure stores running aggregate information that is associated with a group that is accessed infrequently relative to the threshold. The operations also include storing the running aggregate information in either the first or second data structure of the aggregation working area based on a characterization of the group as a frequently or infrequently accessed group.

Abstract: Embodiments relate to data compression using dictionary encoding. An aspect includes subdividing a table of uncompressed data into a first block and a second block of complete rows. Another aspect includes determining information about a frequency of occurrence of different values for each column of the first block. Another aspect includes selecting a row of the first block to be removed out of the first block using frequency of occurrence-information. Another aspect includes removing the a row out of the first block to form an updated first block and determining information about a frequency of occurrence of different values for each column of the updated first block. Another aspect includes deriving a dictionary containing code-words for encoding the values of the updated first block. Another aspect includes encoding the values of the updated first block based on the code-words. Another aspect includes adding the removed row to the second block.