A 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. In special cases, 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.
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.
Script to create the tables
CREATE TABLE employee ( LastName varchar(25), DepartmentID int ); CREATE TABLE department ( DepartmentID int UNIQUE, DepartmentName varchar(25) ); ALTER TABLE employee ADD CONSTRAINT fk_employee_dept FOREIGN KEY (DepartmentID) REFERENCES department(DepartmentID);
Script to add data to the tables
INSERT INTO department VALUES (31,'Sales'); INSERT INTO department VALUES (33,'Engineering'); INSERT INTO department VALUES (34,'Clerical'); INSERT INTO department VALUES (35,'Marketing'); INSERT INTO employee VALUES ('Rafferty',31); INSERT INTO employee VALUES ('Jones',33); INSERT INTO employee VALUES ('Steinberg',33); INSERT INTO employee VALUES ('Robinson',34); INSERT INTO employee VALUES ('Smith',34); INSERT INTO employee VALUES ('John',NULL);
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)â€”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.
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 (or 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.
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 EQUI JOIN department ON employee.DepartmentID = department.DepartmentID;
SQL provides an optional shorthand notation for expressing equi-joins, by way of the USING construct:
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 2005.
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;
As with the explicit USING clause, only one DepartmentID column occurs in the joined table, with no qualifier:
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.
An outer join does not require each record in the two joined tables to have a matching record. The joined table retains each recordâ€”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 resultâ€”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.
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;
In practice, explicit right outer joins are rarely used, since they can always be replaced with left outer joins (with the table order switched) and provide no additional functionality. The result above is produced also 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 (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;
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;
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:
An example solution query could be as follows:
SELECT F.EmployeeID, F.LastName, S.EmployeeID, S.LastName, F.Country FROM Employee F, Employee S WHERE F.Country = S.Country AND F.EmployeeID < S.EmployeeID ORDER BY F.EmployeeID, S.EmployeeID;
Which results in the following table being generated.
For this example, note that:
- 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 EmployeeIDs are the same.
- F.EmployeeID < S.EmployeeID also excludes duplicate 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.
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 group_concat has to be manually added since it doesn't already exist. As in the following example:
SELECT DepartmentID, group_concat(LastName) AS LastNames FROM employee GROUP BY DepartmentID;
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;
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 department.DepartmentID = employee.DepartmentID ) AS DepartmentName FROM employee;
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:
- 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.
- 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:
- using a base table (rather than another join) as the inner operand of each join in the plan
- using a base table as the outer operand of each join in the plan
- 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).
Three fundamental algorithms exist for performing a join operation: Nested loop join, Sort-merge join and Hash join.
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