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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.
For example, think of A as Authors, and B as Books. An Author can write several Books, and a Book can be written by several Authors. In a relational database management system, such relationships are usually implemented by means of an associative table (also known as join table, junction table or cross-reference table), say, AB with two one-to-many relationships A → AB and B → AB.
An associative (or junction) table maps two or more tables together by referencing the primary keys (PK) of each data table. In effect, it contains a number of foreign keys (FK), each in a many-to-one relationship from the junction table to the individual data tables. The PK of the associative table is typically composed of the FK columns ...
The join operation defined for relational databases is often referred to as a natural join (⋈). In this type of join, two relations are connected by their common attributes. MySQL's approximation of a natural join is the Inner join operator. In SQL, an INNER JOIN prevents a cartesian product from occurring when there are two tables in a query.
The example below demonstrates a query of multiple tables, grouping, and aggregation, by returning a list of books and the number of authors associated with each book. SELECT Book . title AS Title , count ( * ) AS Authors FROM Book JOIN Book_author ON Book . isbn = Book_author . isbn GROUP BY Book . title ;
In database theory, a join dependency is a constraint on the set of legal relations over a database scheme. A table T {\displaystyle T} is subject to a join dependency if T {\displaystyle T} can always be recreated by joining multiple tables each having a subset of the attributes of T {\displaystyle T} .
Crosswalk tables are often employed within or in parallel to enterprise systems, especially when multiple systems are interfaced or when the system includes legacy system data. In the context of Interfaces, they function as an internal extract, transform, load (ETL) mechanism.
The entity–relationship model proposes a technique that produces entity–relationship diagrams (ERDs), which can be employed to capture information about data model entity types, relationships and cardinality. A Crow's foot shows a one-to-many relationship. Alternatively a single line represents a one-to-one relationship. [4]