Abstract: A computer program product provides efficient multiple aggregation distinct processing. The computer program product including a computer readable storage medium having program instructions embodied therewith. The program instructions are executable by a processor to cause the processor to convert a query with multiple aggregation distincts into a two-stage group-by process using a nest operator. The two-stage group-by process including further program instructions to cause the processor to: perform a first stage group-by process including the nest operator taking a single input row, and concatenating a grouping key with a measure value for each aggregation distinct that forms multiple inputs to a single group-by table, and perform a second stage group-by process including the nest operator bringing together entries for each original group.

Abstract: A method implemented by at least one processing device, a processing device, and a computer program product are provided for adjusting hash partitions for a hash join operation. An amount of rows within each of an initial number of hash partitions is determined during assignment of respective rows to respective hash partitions. The initial number of hash partitions is adjusted to a final number of hash partitions based on the amount of rows within each of the initial number of hash partitions and an amount of available memory. The adjusting further includes determining the final number of hash partitions based on the amount of rows within each of the initial number of hash partitions and the amount of available memory, and assigning the rows to the final number of hash partitions. The hash join operation is then performed.

Abstract: A method of orchestrated shuffling of data in a non-uniform memory access device including a plurality of processing nodes that 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 and each processing node is capable of accessing data from first and second local memory devices.

Abstract: One embodiment provides for monotonic transactions in a multi-master database with loosely coupled nodes including performing, by a processor, a read transaction protocol including: performing, by the processor, a read transaction protocol including issuing read transactions at any one of the nodes; attempting to return rows that are known to be replicated by at least a read quorum of the nodes; and for timing-out of the attempting to return rows known to be replicated by at least the read quorum of the nodes based on the special-node being non-responsive: selecting a new special-node from the nodes; and repeating attempting to return rows that are known to be replicated by at least the read quorum of the nodes.

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: To manage memory in a multi-processing system, a memory budget is assigned to each of a number of agents within the multi-processing system. A portion of memory is allocated to each the agents within the memory budget. Metrics are collected for each agent during processing of data by the agents; the metrics include an amount of data processed and an amount of memory used for each agent. Memory efficiency is determined for each agent based on the collected metrics and another memory budget is determined based on the memory efficiency. The portion of the memory is reallocated to the agents within the other memory budget in response to data stored in the memory relative to the assigned memory budget meeting a criterion.

Abstract: One embodiment provides for monotonic transactions in a multi-master database with loosely coupled nodes including performing, by a processor, a write transaction protocol including: a client device issuing write transactions at any of the nodes, recording a write transaction locally at a node that issues a write transaction and asynchronously replicating the write transaction to at least one other node, and waiting for reception of an acknowledgment from at least a quorum of the nodes before returning a response to the client device. The quorum is any set of the nodes that includes a special node and at least one other node.

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: A system for sorting in a column organized relational database by sorting only key columns in the column organized relational database to reduce memory and CPU usage. The sort may be performed using an in memory sort. After sorting the key columns, the system orders a payload column according to an order indicated by the key columns. The payload column is associated with the key columns in the column organized relational database. The system assesses memory capacity prior to ordering the payload column. Prior to ordering a plurality of payload columns, the system refrains from automatically concatenating the plurality of payload columns, and refrains from automatically de-concatenating any concatenated payload columns after the ordering the plurality of payload columns. The system determines an amount of required memory necessary to order the payload column, and detects an amount of available memory accessible to order the payload column.

Abstract: A system for sorting in a column organized relational database by sorting only key columns in the column organized relational database to reduce memory and CPU usage. The sort may be performed using an in memory sort. After sorting the key columns, the system orders a payload column according to an order indicated by the key columns. The payload column is associated with the key columns in the column organized relational database. The system assesses memory capacity prior to ordering the payload column. Prior to ordering a plurality of payload columns, the system refrains from automatically concatenating the plurality of payload columns, and refrains from automatically de-concatenating any concatenated payload columns after the ordering the plurality of payload columns. The system determines an amount of required memory necessary to order the payload column, and detects an amount of available memory accessible to order the payload column.

Abstract: One embodiment provides for a method to track replication state and providing quorum visible retrievals. The method includes assigning, by a writer node performing write operations, a sequence number. The sequence number is replicated with a write operation to other replica nodes in an asynchronous fashion thru a first network channel. On a second network channel, a state of a local system is communicated by sending a latest sequence number available that has been hardened to the other replica nodes. A query is obtained by a reader node, and a sequence number limit is selected. Results up to the sequence number limit are returned.

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. The data includes operand data and operational state data of the source threads.

Abstract: A method of orchestrated shuffling of data in a non-uniform memory access device that includes a plurality of processing nodes 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. Shifting the data-shifting table includes rotating a first ring with respect to a second ring.

Abstract: A method includes storing an anchor row vector identification for an anchor row to a local memory. It is determined whether the anchor row vector identification is visible based on isolation requirements. The anchor row vector identification is accessed upon a determination that the anchor row vector identification is visible, and the row vector identification is re-read from the local memory. It is determined whether the anchor row vector identification has not changed since a start of the accessing. Upon a determination that the anchor row vector identification has not changed, read anchor row fields are returned. A first check history is performed on an anchor row history tuple sequence number (TSN) for the anchor row.

Abstract: One embodiment provides for a method including processing transactional operations on a key used to determine whether existing data is found for that key. A first time index is updated using unique keys and a start time field of a first appearance of each key from the transactional operations. A deferred update of prior versions of the key is performed for non-recent data upon determining that recent data in the transactional operations is found for the key.

Abstract: Embodiments relate to aggregation operations. More specifically, embodiments relate to translating a query involving a distinct aggregate(s) into a query that does not involve a distinct aggregate. The translation involves a transformation effectively rewriting the original query containing the distinct aggregate including removal of the distinct aggregate from the query. The translated query enables the query to perform a non-distinct aggregation that effectively provides the results of the removed distinct aggregate(s).

Abstract: A first table has more than one key column, and a hash array is built for the table, which includes computing hash indices for respective rows of the first table based on more than one key column. Building the hash array also includes writing in the hash array for each row at a respective location in the hash array, wherein each location is indicated by each row's respective hash index computed from the more than one key column. The hash array is probed responsive to the second table. In the building of the hash array for the first table, the writing in the hash array for each row at a respective location in the hash array writes only one of the key columns as a single key field in the hash array for each row.

Abstract: A method includes setting, by an update processor, a write latch in a first data structure associated with an object. The first data structure is copied to a storage structure. A history tuple sequence number (TSN) of the first data structure is set to point to a TSN of the copied first data structure. The version identifier is set to point to a transaction identification for the object. Data portions are updated for the first data structure. The version identifier is read from the first data structure. It is determined whether the version identifier of the first data structure is visible for a transaction including isolation requirements. If version identifier of the first data structure is visible, the first data structure is accessed and it is determined whether the version identifier of the first data structure changed since starting the transaction.

Abstract: A method, a system, and a computer program product are provided. A filter is created for each portion of a data set. The filter indicates which one or more characteristics are present among each of the portions. Each of the one or more characteristics comprises one or more groups defined by the data grouping operation. The filters for the portions of the data sets are transferred to one or more filter processors and combined within the one or more filter processors to indicate characteristics of data residing across multiple processing elements to produce a result for a data grouping operation, utilizing transfers based on a combined filter result. In various embodiments, the filter may be a Bloom filter.

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.