International Business Machines Corporation

Globalfoundries Inc.

Liping Zhang

Vijayshankar Raman

Ronald Jason Barber

Gopi K Attaluri

Naresh K Chainani

Vincent Kulandaisamy

Richard Sefton Sidle

Sam Sampson Lightstone

Ippokratis Pandis

Guy Maring Lohman

Kiran Chinta

David C Sharpe

Wayne Jonleon Young

Gregory R Stager

Calisto Paul Zuzarte

Ian R Finlay

Wenbin Ma

Vincent Kulandai Samy

Yuan Su

Dikran S Meliksetian

Qi Chen

Raghupathi Keshava Murthy

Sitaram Vemulapalli

Lynette D Adyilamuriyil

Joshua D Gross

Embodiments of the invention relate to processing data records, and for a multi-phase partitioned data reduction. The first phase relates to processing data records and partitioning the records into a first partition of records having a common characteristic and a second partition of records that are not members of the first partition. The data records in each partition are subject to intra-partition data reduction responsive to a resource constraint. The data records in each partition are also subject to an inter-partition data reduction, also referred to as an aggregation to reduce a footprint for storing the records. Partitions and/or individual records are logically aggregated and a data reduction operation for the logical aggregation of records takes place in response to available resources.

Accurate partition sizing for memory efficient reduction operations

Embodiments described herein provide a solution for optimizing a generating of query search results. A filtering search term (e.g., a search term that is used in a query to perform filtering aggregations of the query search results) is identified. A filtering bitmap that has a plurality of mapped locations corresponding to data values for the filtering search term is created. As a data value in the filtering search term is encountered during a scan of the query search results, the corresponding mapped location is updated. Each mapped location in the filtering bitmap is read to determine whether the value corresponding to the mapped location satisfies the filtering aggregation. The filtering aggregation can then be performed (e.g., prior to any grouping aggregation) by removing any of the query search results determined, based on the filtering bitmap, as having data values for which the filtering aggregation is not satisfied.

Grouping aggregation with filtering aggregation query processing

Provided herein are techniques for processing a database query aggregating data. Data tuples of a database object each including a grouping element and a data element are analyzed to determine a length of the data element for each data tuple. A plurality of tables each accommodate a successively greater length for the data element. A corresponding table of the plurality of tables to store each data tuple is determined based on the length of the data element of that data tuple relative to the accommodated lengths of the plurality of tables. Each data tuple in the determined corresponding table is stored to group the data tuples within each of the plurality of tables based on the grouping element, and an indication of corresponding tables containing members for each group is provided. The groups are combined across the plurality of tables to aggregate the data tuples for a database query.

Efficient database query aggregation of variable length data

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.

Efficient multiple aggregation distinct processing

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.

Memory management in multi-processor environments based on memory efficiency

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.

On-the-fly encoding method for efficient grouping and aggregation

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.

Efficient aggregation in a parallel system

A computer-implemented method includes receiving a query plan. The method includes identifying a plurality of qualified relevant rows and one or more encoding candidate payload columns. The method includes analyzing the relevant rows in the encoding candidate payload columns to yield a count of distinct contents and a payload column width. The method includes estimating a cost and determining whether the cost is larger than an amount of available memory for on-the-fly encoding all of the plurality of encoding candidate payload columns. The method is responsive to the estimated cost being less than the amount of available memory, by on-the-fly encoding the encoding candidates and responsive to the estimated cost being greater than the amount of available memory by on-the-fly encoding fewer than all of the encoding candidates so as not to exceed the available memory, and leaving alone one or more remaining encoding candidate payload columns unencoded.

Query plan optimization for large payload columns

Embodiments relate to payload storage format for storing data in support of an aggregation function. As an input is subject to aggregation, the input is evaluated to ascertain a payload format for the aggregation. It is understood that there is more than one payload format. An evaluation of the aggregation key is a factor in the initial payload format. If the key is an addition to an existing aggregation, the evaluation considers changing the format of the payload to address processing and/or memory efficiency for the aggregation. The evaluation and the format change takes place dynamically so that the aggregation may continue.

Adaptive payload management

A first query is detected. The first query is for a storage device, and pulls a result set from the storage device using a set of predicates. The first query satisfies a size criterion. A new set of predicates of the result set is identified. The new set of predicates does not include any predicates of the original set of predicates. A second query which is the same as the first query is detected. The new set of predicates is used to determine the result set.

Managing complex queries with predicates

According to one embodiment of the present invention, a method of transferring filters for operations in a distributed system comprises generating at each source node a first filter of a first size. The first filter indicates elements of objects that need not be processed for an operation. Each source node generates one more second filters of a second size less than the first size. Each generated second filter is based on a prior generated filter with each location representing a plurality of locations from the prior generated filter. A second filter is transferred from each source node to each of destination node. The elements of the objects to remove for the operation are determined based on the transferred second filters. Embodiments of the present invention further include a system and computer program product for transferring filters in a distributed system in substantially the same manners described above.

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Portland, OR, United States of America

Liping Zhang