An adaptive hash join algorithm for multiuser environments
Proceedings of the sixteenth international conference on Very large databases
Join processing in relational databases
ACM Computing Surveys (CSUR)
Query optimization for parallel execution
SIGMOD '92 Proceedings of the 1992 ACM SIGMOD international conference on Management of data
Query evaluation techniques for large databases
ACM Computing Surveys (CSUR)
SIGMOD '75 Proceedings of the 1975 ACM SIGMOD international conference on Management of data
Database Management Systems
A new way to compute the product and join of relations
SIGMOD '80 Proceedings of the 1980 ACM SIGMOD international conference on Management of data
High-Dimensional Similarity Joins
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
A Cost Model and Index Architecture for the Similarity Join
Proceedings of the 17th International Conference on Data Engineering
Hash-Partitioned Join Method Using Dynamic Destaging Strategy
VLDB '88 Proceedings of the 14th International Conference on Very Large Data Bases
What Happens During a Join? Dissecting CPU and Memory Optimization Effects
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Including Group-By in Query Optimization
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Improving Hash Join Performance through Prefetching
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Optimal top-down join enumeration
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
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Joins are statements that retrieve data from more than one table. A Join is characterized by multiple tables in the FROM clause, and the relationship between the tables is defined through the existence of a Join condition in the WHERE clause. In the case of Very large databases and highly normalized databases frequency of Join queries are high. To perform the Join Query much efficiently, the Join Method the optimizer selects are very vital. Nested Loop Join, Hash Join and Merge Sort Join are primary Join methods available to join tables. When the size of the result set is less than 10,000 the optimizer will perform Nested Loop join. But Nested loop Join steals must of the system resources. In Nested loop Join each record from the outer table will be compared with the inner table to find out a match. This paper proposes a Random Record Join method in which a random record will be picked from the inner table to find a match with the outer table. This method reduces the number of iteration required to Join the table. To perform the Random Record join the optimizer requires statistics of the table. The no. of records containing the distinct Join key attributes value should be maintained by the data dictionary.