Distributed Query Optimization by Index Tuning

Abstract

Query language access a data from databases. When data grows exponentially, the optimization techniques must to be adopt for better results. Query performance tuning and optimization can be achieved by query reformation and/or index selection. A database index is a way of sorting a number of records on multiple fields. Creating an index on a field in a table creates another data structure which holds the field value, and a pointer to the record it relates to. The creation of index in database adds more complexity in data storage, memory consumptions as well as computational complexity. It causes the significant impact on data manipulation operation. On distributed database the query execution optimization is more complex due to various sites are responsible to execute a query and also data is not stored in single site but is distributed to the network. Hence it is needed to define the indexes in more efficient way to reduce the complexities. The proposed method is to reduce the overhead of the data manipulation operation due to index creation; the exploration of multiple column index to identify the minimum number of index creation for optimum level of the database storage consumption and efficient query execution for most frequently used query.