Abstract: A computer-implemented method, system, and/or computer program product optimizes an order of execution of column join operations. A first partitioning of the first data column splits the first data column into first subsets of rows. A second partitioning of the second data column splits the second data column into a second subsets of rows. Cardinalities of sub-tables derived by a respective joining of the subsets of rows of the first and second data columns are estimated, based on the first and second value frequency information. An order of execution of multiple join operations is then optimized based on the estimated cardinalities of the sub-tables.

Abstract: A computer-implemented method, system, and/or computer program product optimizes an order of execution of column join operations. A first partitioning of the first data column splits the first data column into first subsets of rows. A second partitioning of the second data column splits the second data column into a second subsets of rows. Cardinalities of sub-tables derived by a respective joining of the subsets of rows of the first and second data columns are estimated, based on the first and second value frequency information. An order of execution of multiple join operations is then optimized based on the estimated cardinalities of the sub-tables.

Abstract: A computer-implemented method, system, and/or computer program product optimizes an order of execution of column join operations. A first partitioning of the first data column splits the first data column into first subsets of rows. A second partitioning of the second data column splits the second data column into a second subsets of rows. A first value frequency information indicates a frequency of attribute values within a subset of rows of the first data column processed. A second value frequency information indicates a frequency of attribute values within a subset of rows of the second data column. Cardinalities of sub-tables derived by a respective joining of the subsets of rows of the first and second data columns are estimated, based on the first and second value frequency information. An order of execution of multiple join operations is then optimized based on the estimated cardinalities of the sub-tables.

Abstract: A computer-implemented method for minimizing join operation processing time within a database system based on estimated joined table spread of the database system has been provided. The computer-implemented method includes, estimating value distribution of data in a joined table, wherein the joined table is a result of join operation between two instances of tables of a database system. The computer-implemented method further includes determining boundaries for partitioning at least one range of attributes of the estimated value distribution, wherein the boundaries for partitioning at least one range of attributes of the estimated value distribution corresponds to a same number of rows of the joined table. The computer-implemented method further includes determining at least one assignment of the determined partition of the at least one range of attributes to processing units of the database system.

Abstract: A computer-implemented method for minimizing join operation processing time within a database system based on estimated joined table spread of the database system has been provided. The computer-implemented method includes estimating value distribution of data in a joined table, wherein the joined table is a result of join operation between two instances of tables of a database system. The computer-implemented method further includes determining boundaries for partitioning at least one range of attributes of the estimated value distribution, wherein the boundaries for partitioning at least one range of attributes of the estimated value distribution corresponds to a same number of rows of the joined table. The computer-implemented method further includes determining at least one assignment of the determined partition of the at least one range of attributes to processing units of the database system.

Abstract: A method, system, and/or computer program product estimate a cardinality of a joined table (T) obtained by joining at least a first data column (R) and a second data column (S), where R and S each comprise attribute values. A first density distribution function f(x) describes a frequency of the attribute values of R. A second density distribution function (g(x)) describes the frequency of the attribute values of S. A first information on values in R is based on a sample of values of R. A second information on values in S is based on a sample of values of S. One or more processors then estimate a cardinality of a joined table (T) based on the first and second density distribution function (f(x), g(x)) and the first and second information on values.

Abstract: Database queries are optimized through the functionality of decomposition data skew in an asymmetric massively parallel processing database system. A table having data skew is restructured by (1) storing original data values of a distribution key in a special switch column added to the table, (2) replacing the original data values of the distribution key with modified data values such as randomly generated data values, and (3) partitioning the rows across the nodes of the asymmetric massively parallel processing database system based on the distribution key. The original data values that are stored and replaced may only comprise a subset of the original data values that cause data skew in the table. Data skew is reduced, which improves performance, yet the original data values remain available, which reduces the impact on collocated joins.

Abstract: A computer-implemented method for minimizing join operation processing time within a database system based on estimated joined table spread of the database system has been provided. The computer-implemented method includes, estimating value distribution of data in a joined table, wherein the joined table is a result of join operation between two instances of tables of a database system. The computer-implemented method further includes determining boundaries for partitioning at least one range of attributes of the estimated value distribution, wherein the boundaries for partitioning at least one range of attributes of the estimated value distribution corresponds to a same number of rows of the joined table. The computer-implemented method further includes determining at least one assignment of the determined partition of the at least one range of attributes to processing units of the database system.

Abstract: A computer-implemented method for minimizing join operation processing time within a database system based on estimated joined table spread of the database system has been provided. The computer-implemented method includes estimating value distribution of data in a joined table, wherein the joined table is a result of join operation between two instances of tables of a database system. The computer-implemented method further includes determining boundaries for partitioning at least one range of attributes of the estimated value distribution, wherein the boundaries for partitioning at least one range of attributes of the estimated value distribution corresponds to a same number of rows of the joined table. The computer-implemented method further includes determining at least one assignment of the determined partition of the at least one range of attributes to processing units of the database system.

Abstract: A computer-implemented method, system, and/or computer program product optimizes an order of execution of column join operations. A first partitioning of the first data column splits the first data column into first subsets of rows. A second partitioning of the second data column splits the second data column into a second subsets of rows. A first value frequency information indicates a frequency of attribute values within a subset of rows of the first data column processed. A second value frequency information indicates a frequency of attribute values within a subset of rows of the second data column. Cardinalities of sub-tables derived by a respective joining of the subsets of rows of the first and second data columns are estimated, based on the first and second value frequency information. An order of execution of multiple join operations is then optimized based on the estimated cardinalities of the sub-tables.

Abstract: A method, system, and/or computer program product estimate a cardinality of a joined table (T) obtained by joining at least a first data column (R) and a second data column (S), where R and S each comprise attribute values. A first density distribution function f(x) describes a frequency of the attribute values of R. A second density distribution function (g(x)) describes the frequency of the attribute values of S. A first information on values in R is based on a sample of values of R. A second information on values in S is based on a sample of values of S. One or more processors then estimate a cardinality of a joined table (T) based on the first and second density distribution function (f(x), g(x)) and the first and second information on values.

Abstract: Database queries are optimized through the functionality of decomposition data skew in an asymmetric massively parallel processing database system. A table having data skew is restructured by (1) storing original data values of a distribution key in a special switch column added to the table, (2) replacing the original data values of the distribution key with modified data values such as randomly generated data values, and (3) partitioning the rows across the nodes of the asymmetric massively parallel processing database system based on the distribution key. The original data values that are stored and replaced may only comprise a subset of the original data values that cause data skew in the table. Data skew is reduced, which improves performance, yet the original data values remain available, which reduces the impact on collocated joins.