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An inner join (or join) requires each row in the two joined tables to have matching column values, and is a commonly used join operation in applications but should not be assumed to be the best choice in all situations. Inner join creates a new result table by combining column values of two tables (A and B) based upon the join-predicate.
A block-nested loop (BNL) is an algorithm used to join two relations in a relational database. [ 1 ] This algorithm [ 2 ] is a variation of the simple nested loop join and joins two relations R {\displaystyle R} and S {\displaystyle S} (the "outer" and "inner" join operands, respectively).
algorithm nested_loop_join is for each tuple r in R do for each tuple s in S do if r and s satisfy the join condition then yield tuple <r,s> This algorithm will involve n r *b s + b r block transfers and n r +b r seeks, where b r and b s are number of blocks in relations R and S respectively, and n r is the number of tuples in relation R.
In addition, this approach requires high engineering efforts to implement flattening algorithms into a database engine. A general computational approach is to directly execute the nested loop by iterating all tuples of the correlated columns from the outer query block and executing the subquery as many times as the number of outer-loop tuples. [9]
Starting with version 3.1, JDBC has been developed under the Java Community Process. JSR 54 specifies JDBC 3.0 (included in J2SE 1.4), JSR 114 specifies the JDBC Rowset additions, and JSR 221 is the specification of JDBC 4.0 (included in Java SE 6). [2] JDBC 4.1, is specified by a maintenance release 1 of JSR 221 [3] and is included in Java SE ...
The hash join is an example of a join algorithm and is used in the implementation of a relational database management system.All variants of hash join algorithms involve building hash tables from the tuples of one or both of the joined relations, and subsequently probing those tables so that only tuples with the same hash code need to be compared for equality in equijoins.
Join and meet are dual to one another with respect to order inversion. A partially ordered set in which all pairs have a join is a join-semilattice. Dually, a partially ordered set in which all pairs have a meet is a meet-semilattice. A partially ordered set that is both a join-semilattice and a meet-semilattice is a lattice.
The recursive join is an operation used in relational databases, also sometimes called a "fixed-point join". It is a compound operation that involves repeating the join operation, typically accumulating more records each time, until a repetition makes no change to the results (as compared to the results of the previous iteration).