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Self tuning database retrieval optimization using regression functions
| Details |
Inventors: Keller, Martin; Zuzarte, Calisto P.;
Assignee: International Business Machines Corporation (Armonk, NY)
Primary Examiner: Alam; Shahid
Assistant Examiner: Fleurantin; Jean Bolte
Attorney, Agent or Firm: Plow; Greg
A system and method for accessing a relational database and estimating the selectivity of a query (e.g., an SQL query) in order to better predict the number of qualifying records for simple and complex queries. A dataset is created based upon queries applied against the database by the user community. The dataset is populated with information related to query conditions and their respective combinations. A regression function reflecting correlations between query conditions is generated and used as a data mining model to calculate table-specific estimates for the cardinality of subsequent queries. An appropriate access method is then selected from a set of available access methods based upon the number of estimated query-qualifying records. By periodically updating the regression model with FIFO managed queries, a self-tuning mechanism is achieved resulting in better selectivity/result size estimates for use in selecting access methods used in compiling subsequent SQL queries applied against the database. |
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DETAILED DESCRIPTION OF THE PREFERED EMBODIMENT With general reference to the figures and with special reference now to the basic steps of sampling and training, an application of a preferred embodiment of the present invention method, as well as an overview of the physical structure thereof will be described in more detail.
In FIG.
1 illustrating sampling and training according to the present invention, a set 110 of SQL queries q.
sub.
j, j=1, .
.
.
N, is assumed to be given as they are intercepted by an interface provided by the present invention.
Such queries represent "daily life" accesses to a given database.
Each query q.
sub.
j has one or more conditions c.
sub.
jk, k=1, .
.
.
M.
sub.
j like "AGE>30".
In a step 120, the query is first checked in a filtering step, if it is usable for the purposes of the present invention.
If usable, the query does not comprise the following limitative subject matter: A complex expression having SQL keywords e.
g.
UNION, DIFFERENCE and INTERSECT cannot be addressed by the present invention concept, because such keywords are operations on a single attribute level and not on frequencies of attributes, for which the model is primarily trained by the present invention; Nor can subqueries (>all, >any, exist) be addressed, due to the difficulty of training a model for this type of prediction; Nor can computed expressions like "(income*tax_rate>average_tax)" be addressed, because in most cases there is no statistical evidence to forecast a new expression with the knowledge from an old expression, i.
e.
, the cardinality of (average_tax) is not correlated to (average_tax+10) At the end of filtering, a query that meets the above criteria is processed by the invention.
Then, in a step 130 the query command is opened for processing.
In step 140, basically any OR condition is removed from the query.
The original logical expression of the query is replaced by a BOOLEAN equivalent which does not contain any logical OR connector, but instead, which comprises AND connectors
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