Methods for collecting query workload based statistics on column

Details Methods for collecting query workload based statistics on column Methods for collecting query workload based statistics on column

1997-02-10

1999-05-04

Black, Thomas G.

Data processing: database and file management or data structures

Database design

Data structure types

707 3, G06F 1730

Patent

active

058999868

ABSTRACT:
Methods for collecting query workload based statistics within a relational database management system (RDBMS) and for identifying columns for which statistics collection is to be performed. The novel system collects workload statistics that are dependent on multiple columns, rather than merely single columns. Multi-column statistic generation provides more accurate results for columns having correlated data, and therefore leads to better estimated cost analysis by an RDBMS optimizer. In one embodiment, a column duplicity factor is based on an analysis of distinct data rows, e.g., combinations of values within multiple columns, rather than rows of single columns. The novel system also collects separate statistics regarding the presence of null data within the rows of a column group. Separate null data statistics improve the determined result carnality used by the RDBMS optimizer because the cardinality of a relational operation's result is generally determined by the number of input rows with non-null data. The novel system includes an RDBMS optimizer that automatically identifies column groups and column groups on which workload statistics are to be generated. The parameters within a query (e.g., equi-joins, equi-selections, and projections) are analyzed by the optimizer to automatically identify the column groups. The identified columns are then registered within in a system catalog. The registered column groups are read by statistics generation procedures to identify those column groups for which workload statistics are to be collected.

REFERENCES:
patent: 5598559 (1997-01-01), Chaudhuri
"Performing Group-By before Join," Yan et al., Proceedings of the 10th International Conference on Data Engineering, Houston, TX, USA, pp. 89-100, Feb. 14-18, 1994.

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