Join (SQL)

An SQL join clause combines records from two or more tables in a database. It creates a set that can be saved as a table or used as is. A JOIN is a means for combining fields from two tables by using values common to each. ANSI standard SQL specifies four types of JOINs: INNER, OUTER, LEFT, and RIGHT. As a special case, a table (base table, view, or joined table) can JOIN to itself in a self-join.

A programmer writes a JOIN predicate to identify the records for joining. If the evaluated predicate is true, the combined record is then produced in the expected format, a record set or a temporary table.

Sample tables

Relational databases are often normalized to eliminate duplication of information when objects may have one-to-many relationships. For example, a Department may be associated with many different Employees. Joining two tables effectively creates another table which combines information from both tables. This is at some expense in terms of the time it takes to compute the join. While it is also possible to simply maintain such a table if speed is important, duplicate information may take extra space, and add the expense and complexity of maintaining data integrity if data which is duplicated later changes.

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. In the following tables the DepartmentID column of the Department table (which can be designated as Department.DepartmentID) is the primary key, while Employee.DepartmentID is a foreign key.

Note: The "Marketing" Department currently has no listed employees. Also, employee "John" has not been assigned to any Department yet.

Inner join

An inner join is the most common join operation used in applications and can be regarded as 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 examples, 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.

Thus the result of the execution of either of the two queries above will be:

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

Notice that the employee "John" and the department "Marketing" do not appear in the query execution results. Neither of these has any matching records in the respective other table: "John" has no associated department, and no employee has the department ID 35. Thus, no information on John or on Marketing appears in the joined table. Depending on the desired results, this behavior may be a subtle bug. Outer joins may be used to avoid it.

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

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 
       JOIN department 
          ON employee.DepartmentID = department.DepartmentID;

If columns in an equijoin have the same name, SQL/92 provides an optional shorthand notation for expressing equi-joins, by way of the USING construct^[1]:

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 not supported by SQL Server and Sybase.

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-names in the joined tables. The resulting joined table contains only one column for each pair of equally-named columns.

Most experts agree that NATURAL JOINs are dangerous and therefore strongly discourage their use.^[2] The danger comes from inadvertently adding a new column, named the same as another column in the other table. An existing natural join might then "naturally" use the new column for comparisons, making comparisons/matches using different criteria (from different columns) than before. Thus an existing query would (or could) produce different results, even though the data in the tables have not been changed, but only augmented.

The above sample query for inner joins can be expressed as a natural join in the following way:

SELECT *
FROM   employee NATURAL JOIN department;

As with the explicit USING clause, only one DepartmentID column occurs in the joined table, with no qualifier:

PostgreSQL, MySQL and Oracle support natural joins, but not Microsoft T-SQL. The columns used in the join are implicit so the join code does not show which columns are expected, and a change in column names may change the results. An INNER JOIN performed on 2 tables having the same field name has the same effect. ^[3]

Cross join

CROSS JOIN returns the Cartesian product of rows from tables in the join. In other words, it will produce rows which combine each row from the first table with each row from the second table. ^[4]

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. 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 right table returns one row and the left table returns more than one matching row for it, the values in the right table will be repeated for each distinct row on the left table. From Oracle 9i onwards the LEFT OUTER JOIN statement can be used as well as (+).^[5]

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;

Oracle supports the alternate syntax:

SELECT * FROM employee, department WHERE employee.DepartmentID = department.DepartmentID(+)

Sybase supports the alternate syntax:

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

Right outer join

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. Below is shown an example of right outer join, with the additional result row italicized:

SELECT * 
FROM   employee RIGHT OUTER JOIN department 
          ON employee.DepartmentID = department.DepartmentID;

Oracle supports the alternate syntax:

SELECT * FROM employee, department WHERE employee.DepartmentID(+) = department.DepartmentID

Right and left outer joins are functionally equivalent. Neither provides any functionality that the other does not, so right and left outer joins may replace each other as long as the table order is switched. The above result set may also be produced with a left outer join:

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

Full outer join

Conceptually, a full outer join combines the effect of applying both left and right outer joins. Where records in the FULL OUTER JOINed tables do not match, the result set will have NULL values for every column of the table that lacks a matching row. For those records that do match, a single row will be produced in the result set (containing fields populated from both tables).

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;

Some database systems do not support the full outer join functionality directly, but they can emulate it through the use of an inner join and UNION ALL selects of the "single table rows" from left and right tables respectively. The same example can appear as follows:

SELECT employee.LastName, employee.DepartmentID, department.DepartmentName, department.DepartmentID
FROM   employee INNER JOIN department
          ON employee.DepartmentID = department.DepartmentID
UNION ALL
SELECT employee.LastName, employee.DepartmentID, cast(NULL AS varchar(20)), cast(NULL AS integer)
FROM   employee
    WHERE NOT EXISTS (SELECT * FROM department WHERE employee.DepartmentID = department.DepartmentID)
UNION ALL
SELECT cast(NULL AS varchar(20)), cast(NULL AS integer), department.DepartmentName, department.DepartmentID
FROM   department
    WHERE NOT EXISTS (SELECT * FROM employee WHERE employee.DepartmentID = department.DepartmentID)

Self-join

A self-join is joining a table to itself.^[6]

Example

A query to find all pairings of two employees in the same country is desired. If there were 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, a normal join operation could be used to find the answer table. However, all the employee information is contained within a single large table.^[7]

Consider a modified Employee table such as the following:

An example solution query could be as follows:

SELECT F.EmployeeID, F.LastName, S.EmployeeID, S.LastName, F.Country
FROM Employee F
INNER JOIN Employee S ON F.Country = S.Country
WHERE F.EmployeeID < S.EmployeeID
ORDER BY F.EmployeeID, S.EmployeeID;

Which results in the following table being generated.

For this example:

• F and S are aliases for the first and second copies of the employee table.
• The condition F.Country = S.Country excludes pairings between employees in different countries. The example question only wanted pairs of employees in the same country.
• The condition F.EmployeeID < S.EmployeeID excludes pairings where the EmployeeID of the first employee is greater than or equal to the EmployeeID of the second employee. In other words, the effect of this condition is to exclude duplicate pairings and self-pairings. Without it, the following less useful table would be generated (the table below displays only the "Germany" portion of the result):

Only one of the two middle pairings is needed to satisfy the original question, and the topmost and bottommost are of no interest at all in this example.

Merge rows

To be able to do a select so as to merge multiple rows into 1 row : "group_concat notation".

MySQL uses the group_concat keyword to achieve that goal, and PostgreSQL 9.0 has the string_agg function. Versions before 9.0 required the use of something like

array_to_string(array_agg(value),', ')

or the creation of an aggregate function.

MySQL

SELECT DepartmentID, group_concat(LastName) AS LastNames
FROM   employee 
GROUP BY DepartmentID;

PostgreSQL

First the function _group_concat and aggregate group_concat need to be created before that query can be possible.

CREATE OR REPLACE FUNCTION _group_concat(text, text)  
RETURNS text AS $$  
SELECT CASE  
WHEN $2 IS NULL THEN $1  
WHEN $1 IS NULL THEN $2  
ELSE $1 operator(pg_catalog.||) ', ' operator(pg_catalog.||) $2  
END  
$$ IMMUTABLE LANGUAGE SQL;
 
error// JOIN SQL
CREATE AGGREGATE group_concat (
BASETYPE = text,
SFUNC = _group_concat,
STYPE = text
);
 
SELECT DepartmentID, group_concat(LastName) AS LastNames
FROM   employee 
GROUP BY DepartmentID;

As for version 9.0:

SELECT DepartmentID, string_agg(LastName, ', ') AS LastNames
FROM   employee 
GROUP BY DepartmentID;

Microsoft T-SQL

For versions prior to Microsoft SQL Server 2005, the function group_concat must be created as a user-defined aggregate function before that query can be possible, shown here in C#.

using System;
using System.Collections.Generic;
using System.Data.SqlTypes;
using System.IO;
using Microsoft.SqlServer.Server;
 
[Serializable]
[SqlUserDefinedAggregate(Format.UserDefined,  MaxByteSize=8000)]
public struct group_concat : IBinarySerialize{
        private List values;
 
        public void Init()    {
            this.values = new List();
        }
 
        public void Accumulate(SqlString value)    {
            this.values.Add(value.Value);
        }
 
        public void Merge(strconcat value)    {
            this.values.AddRange(value.values.ToArray());
        }
 
        public SqlString Terminate()    {
            return new SqlString(string.Join(", ", this.values.ToArray()));
        }
 
        public void Read(BinaryReader r)    {
            int itemCount = r.ReadInt32();
            this.values = new List(itemCount);
            for (int i = 0; i < itemCount; i++)    {
                this.values.Add(r.ReadString());
            }
        }
 
        public void Write(BinaryWriter w)    {
            w.Write(this.values.Count);
            foreach (string s in this.values)      {
                w.Write(s);
            }
        }
}

Then you can use the following query:

SELECT DepartmentID, dbo.group_concat(LastName) AS LastNames
FROM   employee 
GROUP BY DepartmentID;

From version 2005, one can accomplish this task using FOR XML PATH:

SELECT DepartmentID,
	STUFF(
		(SELECT
			',' + LastName
			FROM (
				SELECT LastName
				FROM employee e2
				WHERE e1.DepartmentID=e2.DepartmentID OR (e1.DepartmentID IS NULL AND e2.DepartmentID IS NULL)
			) t1
			ORDER BY LastName
			FOR XML PATH('') 
		)
		,1,1, ''
	) AS LastNames
FROM employee e1
GROUP BY DepartmentID

Alternatives

The effect of an outer join can also be obtained using a UNION ALL between an INNER JOIN and a SELECT of the rows in the "main" table that do not fulfill the join condition. 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, department.DepartmentName 
FROM   employee INNER JOIN department 
          ON employee.DepartmentID = department.DepartmentID
UNION ALL
SELECT employee.LastName, employee.DepartmentID, cast(NULL AS varchar(20))
FROM   employee 
    WHERE NOT EXISTS (SELECT * FROM department WHERE employee.DepartmentID = department.DepartmentID)

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 it joins functions 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:^[8]

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 for performing a join operation are known: Nested loop join, Sort-merge join and Hash join.

See also

• Join (relational algebra)

Notes

References

• Pratt, Phillip J (2005), A Guide To SQL, Seventh Edition, Thomson Course Technology, ISBN 9780619216740 
• Shah, Nilesh (2005) [2002], Database Systems Using Oracle - A Simplified Guide to SQL and PL/SQL Second Edition (International ed.), Pearson Education International, ISBN 0131911805 
• Yu, Clement T.; Meng, Weiyi (1998), Principles of Database Query Processing for Advanced Applications, Morgan Kaufmann, ISBN 9781558604346, http://books.google.com/?id=aBHRDhrrehYC, retrieved 2009-03-03 

External links

• Specific to products
  • Sybase ASE 15 Joins
  • MySQL 5.5 Joins
  • PostgreSQL Join with Query Explain
  • PostgreSQL 8.3 Joins
  • Joins in Microsoft SQL Server
  • Joins in MaxDB 7.6
  • Joins in Oracle 11g
• General
  • A Visual Explanation of SQL Joins
  • Another visual explanation of SQL joins, along with some set theory
  • SQL join types classified with examples
  • An alternative strategy to using FULL OUTER JOIN