join

A JOIN clause in SQL combines records from two tables in a relational database and results in a new (temporary) table, also called a "joined table". Structured Query Language (SQL:2003) specifies two types of joins: inner and outer.

A programmer writes a join predicate to identify the records for JOINing. If the predicate evaluates true, then the combined record inserts into the joined (temporary) table; otherwise, it does not contribute. Any predicate supported by SQL can become a join-predicate , for example, WHERE-clauses.

As a special case, a table (base table, view, or joined table) can join to itself in a self-join.

Mathematically, a join consists of a relation composition. It provides the fundamental operation in relational algebra and generalizes function composition.

Sample tables

All subsequent explanations on join types in this article make use of the following two tables. The rows in these tables serve to illustrate the effect of different types of joins and join-predicates.

Note: The "Marketing" Department currently has no listed employees. On the other hand, the employee "Jasper" has no link to any currently valid Department in the Department Table.

Inner join

An inner join essentially combines the records from two tables (A and B) based on a given join-predicate. The SQL-engine computes the cross-product of all records in the tables. Thus, processing combines each record in table A with every record in table B. Only those records in the joined table that satisfy the join predicate remain. This type of join occurs the most commonly in applications, and represents the default join-type.

SQL:2003 specifies two different syntactical ways to express joins. The first, called "explicit join notation", uses the keyword JOIN, whereas the second uses the "implicit join notation". The implicit join notation lists the tables for joining in the FROM clause of a SELECT statement, using commas to separate them. Thus it always computes a cross-join, and the WHERE clause may apply additional filter-predicates. Those filter-predicates function comparably to join-predicates in the explicit notation.

One can further classify inner joins as equi-joins, as natural joins, or as cross-joins.

Programmers should take special care when joining tables on columns that can contain NULL values, since NULL will never match any other value (or even NULL itself), unless the join condition uses explicitly the IS NULL or IS NOT NULL predicates.

As an example, the following query takes all the records from the Employee table and finds the matching record(s) in the Department table, based on the join predicate. The join predicate compares the values in the DepartmentID column in both tables. If it finds no match (i.e. the department-id of an employee does not match with the current department-id from the Department table) then the joined record remains outside the joined table, i.e. outside the (intermediate) result of the join.

Example of an explicit inner join:

SELECT *
FROM   employee 
       INNER JOIN department 
          ON employee.DepartmentID = department.DepartmentID

Example of an implicit inner join:

SELECT *  
FROM   employee, department 
WHERE  employee.DepartmentID = department.DepartmentID

Inner join result:

Notice that the employee "Jasper" and the department "Marketing" do not appear. Neither of these have any matching records in the respective other table: no department has the department ID 36 and no employee has the department ID 35. Thus, no information on Jasper or on Marketing appears in the joined table.

Types of inner joins

Equi-join

An equi-join (also known as an equijoin), a specific type of comparator-based join, or theta join, 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

The resulting joined table contains two columns named DepartmentID, one from table Employee and one from table Department.

SQL:2003 does not have a specific syntax to express equi-joins, but some database engines provide a shorthand syntax: for example, MySQL and PostgreSQL support USING(DepartmentID) in addition to the ON ... syntax.

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 natural join in the following way:

SELECT *
FROM   employee NATURAL JOIN department

The result appears slightly different, however, because only one DepartmentID column occurs in the joined table.

Using the NATURAL JOIN keyword to express joins can suffer from ambiguity at best, and leaves systems open to problems if schema changes occur in the database. For example, the removal, addition, or renaming of columns changes the semantics of a natural join. Thus the safer approach involves explicitly coding the join-condition using a regular inner join.

The Oracle database implementation of SQL selects the appropriate column in the naturally-joined table from which to gather data. An error-message such as "ORA-25155: column used in NATURAL join cannot have qualifier" may encourage checking and precisely specifying the columns named in the query.

Cross join

A cross join or cartesian join 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.

If A and B are two sets then cross join = A X 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;

The cross join does not apply any predicate to filter records from the joined table. Programmers can further filter the results of a cross join by using a WHERE clause.

Outer joins

An outer join does not require each record in the two joined tables to have a matching record in the other table. 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).

(For a table to qualify as left or right its name has to appear after the FROM or JOIN keyword, respectively.)

No implicit join-notation for outer joins exists in SQL:2003.

Left outer join

The result of a left outer join for tables 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.

A left outer join returns all the values from the left table, plus matched values from right table (or NULL in case of no matching join predicate).

For example, this allows us to find an employee's department, but still to show the employee even when their department does not exist (contrary to the inner-join example above, where employees in non-existent departments get filtered out).

Example of a left outer join:

SELECT *  
FROM   employee 
       LEFT OUTER JOIN department 
          ON employee.DepartmentID = department.DepartmentID

Right outer join

A right outer join closely resembles a left outer join, except with the tables reversed. Every record 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 A.

A right outer join returns all the values from right table and matched values from left table (or NULL in case of no matching join predicate).

Example right outer join :

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.

Example full outer join:

SELECT *  
FROM   employee 
       FULL OUTER JOIN department 
          ON employee.DepartmentID = department.DepartmentID

Some database systems 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:

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

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, 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:

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 article: Nested loop join

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) 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 the inner relation, but far fewer scans of the inner relation.

Merge join

If both join relations come in order, sorted by the join attribute(s), the system can perform the join trivially, thus:

1. For each tuple in the outer relation,
   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 of query-nodes — 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.

See also: Sort-Merge Join

Hash join

Main article: Hash join

A hash join algorithm can 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, not for other relationships.

See also

• Query optimizer
• Set

External links

• SQL Joins
• Using SQL join clause

• Java implementation of NestedLoop, Sort-Merge and Hash joins.
• MySQL 5.0 Joins
• PostgreSQL 8.2 Joins
• Joins in Microsoft SQL Server
• INNER JOIN in Microsoft Access
• Various join-algorithm implementations
• A Visual Explanation of SQL Joins

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