Abstract: A method includes scanning multiple incoming database transaction requests. Each transaction includes one or more operations. Operations are clustered into a set of combined operations based on type of operation constraints. Log records are prepared and written for re-performing operations upon system failures, and for undoing operations upon an operation or a transaction failing to be processed fully. Each set of combined operations are performed within a thread. Each update operation is marked for a transaction within which the update operation belongs. Recoverable update operations belonging to a plurality of transactions are performed within a single logical thread of execution.

Abstract: A method includes joining data between at least two data sets. Values of one or more join attributes of each of the data sets is represented in a compressed form, indicated by an encoding scheme. A compression scheme for the one or more join attributes is dynamically selected.

Abstract: A method includes logically organizing, by an object hierarchy processor, data objects in a first hierarchy. A portion of the data objects in the first hierarchy logically includes groupings of other data objects. The object hierarchy processor physically organizes the data objects across two or more types of memory in a second hierarchy. Another portion of the data objects in the second hierarchy physically includes groupings of other data objects. Groupings of the data objects in the second hierarchy are dynamically moved across the two or more types of memory. Levels of access of the data objects are tracked using a data structure that maps groupings of the data objects in the first hierarchy onto metadata information including combined access frequencies of the data objects, and current number of accessors to the data objects, in each grouping of the data objects.

Abstract: A method for building a hash table over a subset of data in a data set includes mapping keys in the data set to values in the data set using multiple parallel computation threads. Each thread scans a subset of the keys and values and partitioning the subset of the keys and values into multiple partitions. A cumulative count for keys and values in each partition is determined. A hash table with space reserved for each partition is formed based on the determined cumulative counts. Each thread selects one or more partitions and inserts keys and values belonging to the selected one or more partitions into the hash table in the reserved space for those partitions.

Abstract: Embodiments include a method, system, 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: 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: A database table is partitioned with column-major layout by limiting partitioning one or more columns containing join-column values for a join operator. Join operations are executed for joining the partitioned columns.

Abstract: A hardware accelerator includes a bitmap processor that processes a bitmap structure for multiple list population. A population count processor processes population counts for data and aggregates the population counts. The bitmap data structure includes bitmap bit fields interleaved with aggregated population count fields.

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 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 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: 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: A hybrid approach for performing a join in a database includes: obtaining a distribution of data values in a join column of an inner table; using the distribution, identifying one or more data ranges containing skew; for each data range identified as containing skew, performing, by the processor, the join operation using a skew specific join scheme; and for each data range not identified as containing skew, performing, by the processor, the join operation using a non-skew specific join scheme. One skew specific join scheme involves a compact array table, a highly populated array that represents the range of values that the inner table join column contains. One non-skew specific join scheme involves a compact hash table, an optimized hash table that allows high load factors with a small memory overhead. In combining multiple join techniques, joins may be performed more efficiently for skewed and non-skewed data.

Abstract: A hybrid approach for performing a join in a database includes: obtaining a distribution of data values in a join column of an inner table; using the distribution, identifying one or more data ranges containing skew; for each data range identified as containing skew, performing, by the processor, the join operation using a skew specific join scheme; and for each data range not identified as containing skew, performing, by the processor, the join operation using a non-skew specific join scheme. One skew specific join scheme involves a compact array table, a highly populated array that represents the range of values that the inner table join column contains. One non-skew specific join scheme involves a compact hash table, an optimized hash table that allows high load factors with a small memory overhead. In combining multiple join techniques, joins may be performed more efficiently for skewed and non-skewed 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: 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 relate to data shuffling by logically rotating processing nodes. The nodes are logically arranged in a two or three dimensional matrix. Every time two of the nodes in adjacent rows of the matrix are positionally aligned, these adjacent nodes exchange data. The positional alignment is a logical alignment of the nodes. The nodes are logically arranged and rotated, and data is exchanged in response to the logical rotation.

Abstract: A method of orchestrated shuffling of data in a non-uniform memory access device that includes a plurality of processing nodes that are connected by interconnects. The method includes running an application on a plurality of threads executing on the plurality of processing nodes. Data to be shuffled is identified from source threads running on source processing nodes among the processing nodes to target threads executing on target processing nodes among the processing nodes. The method further includes generating a plan for orchestrating the shuffling of the data among the all of the memory devices associated with the threads and for simultaneously transmitting data over different interconnects to a plurality of different target processing nodes from a plurality of different source processing nodes. The data is shuffled among all of the memory devices based on the plan.

Abstract: The embodiments described herein relate to accessing a plurality of data elements. A page of column data is compressed and stored in a format that includes a collection of data elements. A tuple map is stored, and the collection of data elements is indexed via the tuple map. A query is processed based on the compressed page by identifying a set of tuple identifiers mapping to stored data in support of the query. Each tuple identifier corresponds to a location of a respective tuple of the compressed page.

Abstract: A method of orchestrated shuffling of data in a non-uniform memory access device that includes a plurality of processing nodes includes running an application on a plurality of threads executing on the plurality of processing nodes and identifying data to be shuffled from source threads running on source processing nodes among the processing nodes to target threads executing on target processing nodes among the processing nodes. The method further includes generating a plan for orchestrating the shuffling of the data among the all of the memory devices associated with the threads and shuffling the data among all of the memory devices based on the plan.