An inner join is the most common join operation used in applications, and represents the default join-type. Inner join creates a new result table by combining column values of two tables (A and B) based upon the join-predicate. The query compares each row of A with each row of B to find all pairs of rows which satisfy the join-predicate. When the join-predicate is satisfied, column values for each matched pair of rows of A and B are combined into a result row. The result of the join can be defined as the outcome of first taking the Cartesian product (or cross-join) of all records in the tables (combining every record in table A with every record in table B) - then return all records which satisfy the join predicate. Actual SQL implementations normally use other approaches like a Hash join or a Sort-merge join where possible, since computing the Cartesian product is very inefficient.
SQL specifies two different syntactical ways to express joins: "explicit join notation" and "implicit join notation".
The "explicit join notation" uses the JOIN keyword to specify the table to join, and the ON keyword to specify the predicates for the join, as in the following example:
SELECT *
FROM employee
INNER JOIN department
ON employee.DepartmentID = department.DepartmentID
The "implicit join notation" simply lists the tables for joining (in the FROM clause of the SELECT statement), using commas to separate them. Thus, it specifies a cross-join, and the WHERE clause may apply additional filter-predicates (which function comparably to the join-predicates in the explicit notation).
The following example shows a query which is equivalent to the one from the previous example, but this time written using the implicit join notation:
SELECT *
FROM employee, department
WHERE employee.DepartmentID = department.DepartmentID
The queries given in the examples above will join the Employee and Department tables using the DepartmentID column of both tables. Where the DepartmentID of these tables match (i.e. the join-predicate is satisfied), the query will combine the LastName, DepartmentID and DepartmentName columns from the two tables into a result row. Where the DepartmentID does not match, no result row is generated
Equi-join
An equi-join, also known as an equijoin, is a specific type of comparator-based join, or theta join, that uses only equality comparisons in the join-predicate. Using other comparison operators (such as <) disqualifies a join as an equi-join. The query shown above has already provided an example of an equi-join: SELECT * FROM employee INNER JOIN department ON employee.DepartmentID = department.DepartmentID SQL provides an optional shorthand notation for expressing equi-joins, by way of the USING construct (Feature ID F402): SELECT * FROM employee INNER JOIN department USING (DepartmentID) The USING construct is more than mere syntactic sugar, however, since the result set differs from the result set of the version with the explicit predicate. Specifically, any columns mentioned in the USING list will appear only once, with an unqualified name, rather than once for each table in the join. In the above case, there will be a single DepartmentID column and no employee.DepartmentID or department.DepartmentID. The USING clause is supported by MySQL, Oracle, PostgreSQL, SQLite, DB2/400 and Firebird in version 2.1 or higher. Natural join
A natural join offers a further specialization of equi-joins. The join predicate arises implicitly by comparing all columns in both tables that have the same column-name in the joined tables. The resulting joined table contains only one column for each pair of equally-named columns....
The above sample query for inner joins can be expressed as a natural join in the following way:
SELECT *
FROM employee NATURAL JOIN department
Cross join
A cross join, cartesian join or product provides the foundation upon which all types of inner joins operate. A cross join returns the cartesian product of the sets of records from the two joined tables. Thus, it equates to an inner join where the join-condition always evaluates to True or where the join-condition is absent from the statement. In other words, a cross join combines every row in B with every row in A. The number of rows in the result set will be the number of rows in A times the number of rows in B.
Thus, if A and B are two sets, then the cross join is written as A × B.
The SQL code for a cross join lists the tables for joining (FROM), but does not include any filtering join-predicate.
Example of an explicit cross join:
SELECT *
FROM employee CROSS JOIN department
Example of an implicit cross join:
SELECT *
FROM employee, department;
Outer joins
An outer join does not require each record in the two joined tables to have a matching record. The joined table retains each record—even if no other matching record exists. Outer joins subdivide further into left outer joins, right outer joins, and full outer joins, depending on which table(s) one retains the rows from (left, right, or both).
(In this case left and right refer to the two sides of the JOIN keyword.)
No implicit join-notation for outer joins exists in standard SQL.
Left outer join
The result of a left outer join (or simply left join) for table A and B always contains all records of the "left" table (A), even if the join-condition does not find any matching record in the "right" table (B). This means that if the ON clause matches 0 (zero) records in B, the join will still return a row in the result—but with NULL in each column from B. This means that a left outer join returns all the values from the left table, plus matched values from the right table (or NULL in case of no matching join predicate). If the left table returns one row and the right table returns more than one matching row for it, the values in the left table will be repeated for each distinct row on the right table.
For example, this allows us to find an employee's department, but still shows the employee(s) even when they have not been assigned to a department (contrary to the inner-join example above, where unassigned employees are excluded from the result).
Example of a left outer join, with the additional result row italicized:
SELECT *
FROM employee LEFT OUTER JOIN department
ON employee.DepartmentID = department.DepartmentID
Right outer joins
A right outer join (or right join) closely resembles a left outer join, except with the treatment of the tables reversed. Every row from the "right" table (B) will appear in the joined table at least once. If no matching row from the "left" table (A) exists, NULL will appear in columns from A for those records that have no match in B.
A right outer join returns all the values from the right table and matched values from the left table (NULL in case of no matching join predicate).
For example, this allows us to find each employee and his or her department, but still show departments that have no employees.
Example right outer join, with the additional result row italicized:
SELECT *
FROM employee RIGHT OUTER JOIN department
ON employee.DepartmentID = department.DepartmentID
Full outer join
A full outer join combines the results of both left and right outer joins. The joined table will contain all records from both tables, and fill in NULLs for missing matches on either side.
For example, this allows us to see each employee who is in a department and each department that has an employee, but also see each employee who is not part of a department and each department which doesn't have an employee.
Example full outer join:
SELECT *
FROM employee
FULL OUTER JOIN department
ON employee.DepartmentID = department.DepartmentID
NULL NULL Marketing 35
Some database systems (like MySQL) do not support this functionality directly, but they can emulate it through the use of left and right outer joins and unions. The same example can appear as follows:
SELECT *
FROM employee
LEFT JOIN department
ON employee.DepartmentID = department.DepartmentID
UNION
SELECT *
FROM employee
RIGHT JOIN department
ON employee.DepartmentID = department.DepartmentID
WHERE employee.DepartmentID IS NULL
SQLite does not support right join, so outer join can be emulated as follows:
SELECT employee.*, department.*
FROM employee
LEFT JOIN department
ON employee.DepartmentID = department.DepartmentID
UNION ALL
SELECT employee.*, department.*
FROM department
LEFT JOIN employee
ON employee.DepartmentID = department.DepartmentID
WHERE employee.DepartmentID IS NULL
Self-join
A self-join is joining a table to itself. This is best illustrated by the following example.
A query to find all pairings of two employees in the same country is desired. If you had two separate tables for employees and a query which requested employees in the first table having the same country as employees in the second table, you could use a normal join operation to find the answer table. However, all the employee information is contained within a single large table.[
Considering a modified Employee table such as the following:
Alternatives
The effect of outer joins can also be obtained using correlated subqueries. For example
SELECT employee.LastName, employee.DepartmentID, department.DepartmentName
FROM employee LEFT OUTER JOIN department
ON employee.DepartmentID = department.DepartmentID
can also be written as
SELECT employee.LastName, employee.DepartmentID,
(SELECT department.DepartmentName
FROM department
WHERE employee.DepartmentID = department.DepartmentID )
FROM employee
Implementation
Much work in database-systems has aimed at efficient implementation of joins, because relational systems commonly call for joins, yet face difficulties in optimising their efficient execution. The problem arises because (inner) joins operate both commutatively and associatively. In practice, this means that the user merely supplies the list of tables for joining and the join conditions to use, and the database system has the task of determining the most efficient way to perform the operation. A query optimizer determines how to execute a query containing joins. A query optimizer has two basic freedoms:
1. Join order: Because joins function commutatively and associatively, the order in which the system joins tables does not change the final result-set of the query. However, join-order does have an enormous impact on the cost of the join operation, so choosing the best join order becomes very important.
2. Join method: Given two tables and a join condition, multiple algorithms can produce the result-set of the join. Which algorithm runs most efficiently depends on the sizes of the input tables, the number of rows from each table that match the join condition, and the operations required by the rest of the query.
Many join-algorithms treat their inputs differently. One can refer to the inputs to a join as the "outer" and "inner" join operands, or "left" and "right", respectively. In the case of nested loops, for example, the database system will scan the entire inner relation for each row of the outer relation.
One can classify query-plans involving joins as follows:
left-deep
using a base table (rather than another join) as the inner operand of each join in the plan
right-deep
using a base table as the outer operand of each join in the plan
bushy
neither left-deep nor right-deep; both inputs to a join may themselves result from joins
These names derive from the appearance of the query plan if drawn as a tree, with the outer join relation on the left and the inner relation on the right (as convention dictates).
Join algorithms
Three fundamental algorithms exist for performing a join operation.
Nested loops
Main articles: Nested loop join and block nested loop
Use of nested loops produces the simplest join-algorithm. For each tuple in the outer join relation, the system scans the entire inner-join relation and appends any tuples that match the join-condition to the result set. Naturally, this algorithm performs poorly with large join-relations: inner or outer or both. An index on columns in the inner relation in the join-predicate can enhance performance.
The block nested loops (BNL) approach offers a refinement to this technique: for every block in the outer relation, the system scans the entire inner relation. For each match between the current inner tuple and one of the tuples in the current block of the outer relation, the system adds a tuple to the join result-set. This variant means doing more computation for each tuple of
Merge join
If both join relations come in order, sorted by the join attribute(s), the system can perform the join trivially, thus:
1. Consider the current "group" of tuples from the inner relation; a group consists of a set of contiguous tuples in the inner relation with the same value in the join attribute.
2. For each matching tuple in the current inner group, add a tuple to the join result. Once the inner group has been exhausted, advance both the inner and outer scans to the next group.
Merge joins offer one reason why many optimizers keep track of the sort order produced by query plan operators—if one or both input relations to a merge join arrives already sorted on the join attribute, the system need not perform an additional sort. Otherwise, the DBMS will need to perform the sort, usually using an external sort to avoid consuming too much memory.
Hash join
Main article:
A hash join algorithm can only produce equi-joins. The database system pre-forms access to the tables concerned by building hash tables on the join-attributes. The lookup in hash tables operates much faster than through index trees. However, one can compare hashed values only for equality (or inequality), not for other relationships.
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