摘要:
A method and apparatus for approximating a database statistic, such as the number of distinct values (NDV) is provided. To approximate the NDV for a portion of a table, a synopsis of distinct values is constructed. Each value in the portion is mapped to a domain of values. The mapping function is implemented with a uniform hash function, in one embodiment. If the resultant domain value does not exist in the synopsis, the domain value is added to the synopsis. If the synopsis reaches its capacity, a portion of the domain values are discarded from the synopsis. The statistic is approximated based on the number (N) of domain values in the synopsis and the portion of the domain that is represented in the synopsis relative to the size of the domain.
摘要:
A method and apparatus for merging synopses to determine a database statistic, e.g., a number of distinct values (NDV), is disclosed. The merging can be used to determine an initial database statistic or to perform incremental statistics maintenance. For example, each synopsis can pertain to a different partition, such that merging the synopses generates a global statistic. When performing incremental maintenance, only those synopses whose partitions have changed need to be updated. Each synopsis contains domain values that summarize the statistic. However, the synopses may initially contain domain values that are not compatible with each other. Prior to merging the synopses the domain values in each synopsis is made compatible with the domain values in the other synopses. The adjustment is made such that each synopsis represents the same range of domain values, in one embodiment. After “compatible synopses” are formed, the synopses are merged by taking the union of the compatible synopses.
摘要:
A method and apparatus for approximating a database statistic, such as the number of distinct values (NDV) is provided. To approximate the NDV for a portion of a table, a synopsis of distinct values is constructed. Each value in the portion is mapped to a domain of values. The mapping function is implemented with a uniform hash function, in one embodiment. If the resultant domain value does not exist in the synopsis, the domain value is added to the synopsis. If the synopsis reaches its capacity, a portion of the domain values are discarded from the synopsis. The statistic is approximated based on the number (N) of domain values in the synopsis and the portion of the domain that is represented in the synopsis relative to the size of the domain.
摘要:
A method and apparatus for merging synopses to determine a database statistic, e.g., a number of distinct values (NDV), is disclosed. The merging can be used to determine an initial database statistic or to perform incremental statistics maintenance. For example, each synopsis can pertain to a different partition, such that merging the synopses generates a global statistic. When performing incremental maintenance, only those synopses whose partitions have changed need to be updated. Each synopsis contains domain values that summarize the statistic. However, the synopses may initially contain domain values that are not compatible with each other. Prior to merging the synopses the domain values in each synopsis is made compatible with the domain values in the other synopses. The adjustment is made such that each synopsis represents the same range of domain values, in one embodiment. After “compatible synopses” are formed, the synopses are merged by taking the union of the compatible synopses.
摘要:
Techniques are provided for performing a parallel aggregation operation on data that resides in a container, such as a relational table. During generation of the execution plan for the operation, it is determined whether partition-wise aggregation should be performed, based on the grouping keys involved in the aggregation and the partition keys used to partition the container. If partition-wise aggregation is to be performed, then the assignments given to the slave processes that are assigned to scan a container are made on a partition-wise basis. The scan slaves themselves may perform full or partial aggregation (depending on whether they are the only scan slaves assigned to the partition). If the scan slaves perform no aggregation, or only partial aggregation, then the scan slaves redistribute the data items to aggregation slaves that are local to the scan slaves.
摘要:
Techniques are provided for performing a parallel aggregation operation on data that resides in a container, such as a relational table. During generation of the execution plan for the operation, it is determined whether partition-wise aggregation should be performed, based on the grouping keys involved in the aggregation and the partition keys used to partition the container. If partition-wise aggregation is to be performed, then the assignments given to the slave processes that are assigned to scan a container are made on a partition-wise basis. The scan slaves themselves may perform full or partial aggregation (depending on whether they are the only scan slaves assigned to the partition). If the scan slaves perform no aggregation, or only partial aggregation, then the scan slaves redistribute the data items to aggregation slaves that are local to the scan slaves.
摘要:
Techniques for eliminating one or more portions of a data object from any join step of an operation that joins multiple data objects include determining that an operation joins a first data object and a second data object. The second data object includes multiple portions. Each of multiple data units of the first data object is scanned. Based on data in the data units of the first data object, information is generated. The information indicates a portion of the second data object for exclusion. The indicated portion is excluded from an output of the operation. Only one or more portions of the second data object that are not indicated for exclusion in the information are included in a particular join step involving the second data object. By pruning a large second table, such as a fact table, the computational resources consumed by the joins are substantially reduced.
摘要:
Under a type of query transformation referred to herein as join factorization, the branches of an UNION/UNION ALL query that join a common table are combined to reduce accesses to the common table. The transformation can be expressed as (T1 join T2) union all (T1 join T3)=T1 join (T2 union all T3), where T1, T2 and T3 are three tables. A given query may be rewritten in many alternate ways using join factorization. Evaluating each alternative can be expensive. Therefore, the alternatives are generated and evaluated in a way that minimizes the cost of evaluating the alternatives.
摘要翻译:在这里称为连接因式分解的一种类型的查询变换中,加入公共表的UNION / UNION ALL查询的分支被组合以减少对公共表的访问。 转换可以表示为(T1连接T2)联合全部(T1连接T3)= T1连接(T2联合全T3),其中T1,T2和T3是三个表。 给定的查询可以使用连接因式分解以许多替代方式重写。 评估每个替代品可能是昂贵的。 因此,以最小化评估替代品的成本的方式生成和评估替代方案。
摘要:
Under a type of query transformation referred to herein as join factorization, the branches of an UNION/UNION ALL query that join a common table are combined to reduce accesses to the common table. The transformation can be expressed as (T1 join T2) union all (T1 join T3)=T1 join (T2 union all T3), where T1, T2 and T3 are three tables. A given query may be rewritten in many alternate ways using join factorization. Evaluating each alternative can be expensive. Therefore, the alternatives are generated and evaluated in a way that minimizes the cost of evaluating the alternatives.
摘要翻译:在这里称为连接因式分解的一种类型的查询变换中,加入公共表的UNION / UNION ALL查询的分支被组合以减少对公共表的访问。 转换可以表示为(T1连接T2)联合全部(T1连接T3)= T1连接(T2联合全T3),其中T1,T2和T3是三个表。 给定的查询可以使用连接因式分解以许多替代方式重写。 评估每个替代品可能是昂贵的。 因此,以最小化评估替代品的成本的方式生成和评估替代方案。
摘要:
Techniques are described herein for estimating selectivities of query predicates that reference more than one column and predicates that include column expressions. Virtual columns are defined based on column groups and column expressions. Statistics are gathered on the virtual columns and are used to estimate the selectivities of query predicates that include column groups or expressions. Query predicates that include column groups are mapped to virtual columns on which statistics are gathered, based on similarities between the column groups in the query predicates and the column groups on which the virtual columns are defined. Virtual columns are defined for column groups and expressions are specified by users or a workload analyzer that analyzes query predicates.