Abstract: Aspects of the invention are provided for accessing a plurality of data elements. A page of column data is stored in a format that includes compressed and/or non-compressed elements, with the format including a plurality of arrays and a vector. Each of the arrays stores elements with common characteristics, with the vector functioning as a mapping to the stored data elements. The vector is leveraged to identify an array and determine an offset to support access to one or more of the data elements.

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: 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: A query processing method intersects two or more unsorted lists based on a conjunction of predicates. Each list comprises a union of multiple sorted segments. The method performs lazy segment merging and an adaptive n-ary intersecting process. The lazy segment merging comprises starting with each list being a union of completely unmerged segments, such that lookups into a given list involve separate lookups into each segment of the given list. The method intersects the lists according to the predicates while performing the lazy segment merging, such that the lazy segment merging reads in only those portions of each segment that are needed for the intersecting. As the intersecting proceeds and the lookups are performed, the intersecting selectively merges the segments together, based on a cost-benefit analysis of the cost of merging compared to the benefit produced by reducing a number of lookups.

Abstract: A system joins predicate evaluated column bitmaps having varying lengths. The system includes a column unifier for querying column values with a predicate and generating an indicator bit for each of the column values that is then joined with the respective column value. The system also includes a bitmap generator for creating a column-major linear bitmap from the column values and indicator bits. The column unifier also determines an offset between adjacent indicator bits. The system also includes a converter for multiplying the column-major linear bitmap with a multiplier to shift the indicator bits into consecutive positions in the linear bitmap.

Abstract: System, method and computer program products for storing data by computing a plurality of hash functions of data values in a data item, and determining a corresponding memory location for one of the plurality of hash functions of data values in the data item. Each memory location is of a cacheline size wherein a data item is stored in a memory location. Each memory location can store a plurality of data items. A key portion of all data items is contiguously stored within the memory location, and a payload portion is contiguously stored within the memory location. Payload portions are packed as bit-aligned in a fixed-sized memory location, comprising a bucket in a bucketized hash table, each bucket sized to store multiple key portions and payload portions that are packed as bit-aligned in a fixed-sized bucket. Corresponding key portions are stored as compressed keys in said fixed-sized bucket.

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: Embodiments of the present invention provide a database processing system for efficient partitioning of a database table with column-major layout for executing one or more join operations. One embodiment comprises a method for partitioning a database table with column-major layout, partitioning only the join-columns by limiting the partitions by size and number, executing one or more join operations for joining the partitioned columns, and optionally de-partitioning the join result to the original order by sequentially writing and randomly reading table values using P cursors.

Abstract: Embodiments of the present invention provide a database processing system for efficient partitioning of a database table with column-major layout for executing one or more join operations. One embodiment comprises a method for partitioning a database table with column-major layout, partitioning only the join-columns by limiting the partitions by size and number, executing one or more join operations for joining the partitioned columns, and optionally de-partitioning the join result to the original order by sequentially writing and randomly reading table values using P cursors.

Abstract: A method for storing database information includes storing a table having data values in a column major order. The data values are stored in a list of blocks. The method also includes assigning a tuple sequence number (TSN) to each data value in each column of the table according to a sequence order in the table. The data values that correspond to each other across a plurality of columns of the table have equivalent TSNs. The method also includes assigning each data value to a partition based on a representation of the data value. The method also includes assigning a tuple map value to each data value. The tuple map value identifies the partition in which each data value is located.

Abstract: A method for storing database information, including: storing a table having data values in a column major order, wherein the data values are stored in a list of blocks, assigning a tuple sequence number (TSN) to each data value in each column of the table according to a sequence order in the table, wherein data values that correspond to each other across a plurality of columns of the table have equivalent TSNs; assigning each data value to a partition based on a representation of the data value; and assigning a tuple map value to each data value, wherein the tuple map value identifies the partition in which each data value is located.

Abstract: A common interface to manage heterogeneous databases and develop enterprise class applications is provided. In particular, it is shown that a client-based system and method can: (a) provide a uniform interface for the DBA or the application developer to use across all the database deployments, (b) provide flexibility in the number and kinds of scenarios it can be used, and finally (c) reduce the total cost of ownership for the enterprise.

Abstract: Described herein are methods, systems, apparatuses and products for multithreaded data merging for multi-core central and graphical processing units. An aspect provides for executing a plurality of threads on at least one central processing unit comprising a plurality of cores, each thread comprising an input data set (IDS) and being executed on one of the plurality of cores; initializing at least one local data set (LDS) comprising a size and a threshold; inserting IDS data elements into the at least one LDS such that each inserted IDS data element increases the size of the at least one LDS; and merging the at least one LDS into a global data set (GDS) responsive to the size of the at least one LDS being greater than the threshold. Other aspects are disclosed herein.

Abstract: A computer-implemented method for scan sharing across multiple cores in a business intelligence (BI) query. The method includes receiving a plurality of BI queries, storing a block of data in a first cache, scanning the block of data in the first cache against a first batch of queries on a first processor core, and scanning the block of data against a second batch of queries on a second processor core. The first cache is associated with a first processor core. The block of data includes a subset of data stored in an in-memory database (IMDB). The first batch of queries includes two or more of the BI queries. The second batch of queries includes one or more of the BI queries that are not included in the first batch of queries.

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: A predicate over a single column of a table is converted into at least one IN-list, wherein the IN-list is generated for a set of tuples of the column, and the generation is done over a data structure representing a set of distinct values of the column where the predicate applies and having a smaller cardinality than the table. The generated IN-list is evaluated over the set of tuples and the results of the evaluation are outputted as an evaluation of the predicate.

Abstract: A novel method is described for applying various hash methods used in conjunction with a query with a Group By clause. A plurality of drawers are identified, wherein each of the drawers is made up of a collection of cells from a single partition of a Group By column and each of the drawers being defined for a specific query. A separate hash table is independently computed for each of the drawers and a hashing scheme (picked from among a plurality of hashing schemes) is independently applied for each of the drawers.

Abstract: A computer-implemented method for scan sharing across multiple cores in a business intelligence (BI) query. The method includes receiving a plurality of BI queries, storing a block of data in a first cache, scanning the block of data in the first cache against a first batch of queries on a first processor core, and scanning the block of data against a second batch of queries on a second processor core. The first cache is associated with a first processor core. The block of data includes a subset of data stored in an in-memory database (IMDB). The first batch of queries includes two or more of the BI queries. The second batch of queries includes one or more of the BI queries that are not included in the first batch of queries.