Join (SQL)
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A join combines records from two or more tables in a relational database. In the Structured Query Language (SQL), there are three types of joins: inner, outer, and cross. Outer joins are subdivided further into left outer joins, right outer joins, and full outer joins.
Mathematically, join is relation composition, the fundamental operation in relational algebra, and generalizing function composition.
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[edit] Example Tables
The join examples below use the following two tables:
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For illustrative purposes, notice that the DepartmentID of employee Jasper has no matching row in the department table, and the Marketing department has no matching row in the employee table.
[edit] Join forms
[edit] Inner join
An inner join essentially finds the intersection between the two tables. This is the most common type of join used, and is considered the default join type. The join example below takes all the records from table A (in this case, employee) and finds the matching record(s) from table B (department). If no match is found, the record from A is not included in the results. If multiple results are found in B that match the predicate then one row will be returned for each (the values from A will be repeated).
Special care must be taken when joining tables on columns that can be NULL since NULL values will never match each other. See Left Outer Join or Right Outer Join for a solution.
Example inner join (ANSI 92 standard syntax):
SELECT *
FROM employee
INNER JOIN department
ON employee.DepartmentID = department.DepartmentID
Example inner join (non-standard syntax):
SELECT * FROM employee, department WHERE employee.DepartmentID = department.DepartmentID
Inner join result :
+-----------+--------------+----------------+--------------+ | LastName | DepartmentID | DepartmentName | DepartmentID | +-----------+--------------+----------------+--------------+ | Smith | 34 | Clerical | 34 | | Jones | 33 | Engineering | 33 | | Robinson | 34 | Clerical | 34 | | Steinberg | 33 | Engineering | 33 | | Rafferty | 31 | Sales | 31 | +-----------+--------------+----------------+--------------+
Notice that employee Jasper and department Marketing do not appear. Neither of these records have accompanying rows in their associative tables, and are thus omitted from the inner join result.
[edit] Left outer join
A left outer join is very different from an inner join. Instead of limiting results to those in both tables, it limits results to those in the "left" table (A). This means that if the ON clause matches 0 records in B, a row in the result will still be returned—but with NULL values for each column from B.
It returns all the values from left table + only matched values from right table.
For example, this allows us to find the employee's departments, but still show the employee even when their department is NULL or does not exist. The example above would have ignored employees in non-existent departments.
Example left outer join (ANSI 92 standard syntax):
SELECT distinct *
FROM employee
LEFT OUTER JOIN
department
ON employee.DepartmentID = department.DepartmentID
Example left outer join (non-standard syntax):
SELECT *
FROM employee, department
WHERE employee.DepartmentID = department.DepartmentID(+)
+-----------+--------------+----------------+--------------+ | LastName | DepartmentID | DepartmentName | DepartmentID | +-----------+--------------+----------------+--------------+ | Smith | 34 | Clerical | 34 | | Jones | 33 | Engineering | 33 | | Robinson | 34 | Clerical | 34 | | Jasper | 36 | NULL | NULL | | Steinberg | 33 | Engineering | 33 | | Rafferty | 31 | Sales | 31 | +-----------+--------------+----------------+--------------+
In the non-standard syntax, the (+) indicates the department table should be expanded (with NULLs) where no matching rows exist in the employee table.
[edit] Right outer join
A right outer join is much like a left outer join, except that the tables are reversed. Every record from the right side, B or department, will be returned, and NULL values will be returned for those that have no matching record in A.
It returns all the values from right table + only matched values from left table
Example right outer join (ANSI 92 standard syntax):
SELECT *
FROM employee
RIGHT OUTER JOIN
department
ON employee.DepartmentID = department.DepartmentID
Example right outer join (non-standard syntax):
SELECT *
FROM employee, department
WHERE employee.DepartmentID(+) = department.DepartmentID
+-----------+--------------+----------------+--------------+ | LastName | DepartmentID | DepartmentName | DepartmentID | +-----------+--------------+----------------+--------------+ | Smith | 34 | Clerical | 34 | | Jones | 33 | Engineering | 33 | | Robinson | 34 | Clerical | 34 | | Steinberg | 33 | Engineering | 33 | | Rafferty | 31 | Sales | 31 | | NULL | NULL | Marketing | 35 | +-----------+--------------+----------------+--------------+
[edit] Full outer join
A full outer join combines the results of both left and right outer joins. These joins will show records from both tables, and fill in NULLs for missing matches on either side.
Some database systems do not support this functionality, but it can be emulated through the use of left and right outer joins and unions (see below).
Example full outer join (ANSI 92 standard syntax):
SELECT *
FROM employee
FULL OUTER JOIN
department
ON employee.DepartmentID = department.DepartmentID
+-----------+--------------+----------------+--------------+ | LastName | DepartmentID | DepartmentName | DepartmentID | +-----------+--------------+----------------+--------------+ | Smith | 34 | Clerical | 34 | | Jones | 33 | Engineering | 33 | | Robinson | 34 | Clerical | 34 | | Jasper | 36 | NULL | NULL | | Steinberg | 33 | Engineering | 33 | | Rafferty | 31 | Sales | 31 | | NULL | NULL | Marketing | 35 | +-----------+--------------+----------------+--------------+
The same example, for use on databases that do not support FULL OUTER JOIN:
SELECT
employee.LastName,
employee.DepartmentID,
department.DepartmentName,
department.DepartmentID
FROM employee
LEFT JOIN
department
ON employee.DepartmentID = department.DepartmentID
UNION
SELECT
employee.LastName,
employee.DepartmentID,
department.DepartmentName,
department.DepartmentID
FROM employee
RIGHT JOIN
department
ON employee.DepartmentID = department.DepartmentID
WHERE employee.DepartmentID IS NULL
[edit] Cross join
While not used very commonly, a cross join is the foundation upon which inner joins are built. A cross join returns the cartesian product of the sets of rows from the joined tables.
The SQL code for a cross join lists the tables to be joined (FROM), but does not include any filtering predicate (WHERE).
Example cross join (ANSI 92 standard syntax):
SELECT *
FROM employee CROSS JOIN
department;
Example cross join (alternative syntax):
SELECT * FROM employee, department;
+-----------+---------------+----------------+--------------+ | LastName | DepartmentID | DepartmentName | DepartmentID | +-----------+---------------+----------------+--------------+ | Smith | 34 | Sales | 31 | | Smith | 34 | Engineering | 33 | | Smith | 34 | Clerical | 34 | | Smith | 34 | Marketing | 35 | | Jones | 33 | Sales | 31 | | Jones | 33 | Engineering | 33 | | Jones | 33 | Clerical | 34 | | Jones | 33 | Marketing | 35 | | Robinson | 34 | Sales | 31 | | Robinson | 34 | Engineering | 33 | | Robinson | 34 | Clerical | 34 | | Robinson | 34 | Marketing | 35 | | Jasper | 36 | Sales | 31 | | Jasper | 36 | Engineering | 33 | | Jasper | 36 | Clerical | 34 | | Jasper | 36 | Marketing | 35 | | Steinberg | 33 | Sales | 31 | | Steinberg | 33 | Engineering | 33 | | Steinberg | 33 | Clerical | 34 | | Steinberg | 33 | Marketing | 35 | | Rafferty | 31 | Sales | 31 | | Rafferty | 31 | Engineering | 33 | | Rafferty | 31 | Clerical | 34 | | Rafferty | 31 | Marketing | 35 | +-----------+---------------+----------------+--------------+
As you can see the cross join does no matching of like records. These joins are almost never used, except to generate all possible combinations of records from tables that do not share a common element. If A and B are two sets then cross join = A X B
[edit] Implementation
The efficient implementation of joins has been the goal of much work in database systems, because joins are both extremely common and difficult to execute efficiently. The difficulty results from the fact that (inner) joins are both commutative and associative. In practice, this means that the user merely supplies the list of tables to be joined and the join conditions to be used, and the database system has the task of determining the most efficient way to perform the operation. Determining how to execute a query containing joins is done by the query optimizer. It has two basic freedoms:
- Join order: because joins are commutative, the order in which tables are joined 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 is very important.
- Join method: given two tables and a join condition, there are multiple algorithms to produce the result set of the join. Which algorithm is most efficient 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. The inputs to a join are referred to as the outer and inner join operands, or left and right, respectively. In the case of nested loops, for example, the entire inner relation will be scanned for each row of the outer relation.
Query plans involving joins can be classified as:
- left-deep: the inner operand of each join in the plan is a base table (rather than another join).
- right-deep: the outer operand of each join in the plan is a base table.
- bushy: neither left-deep nor right-deep; both inputs to a join may be joins themselves.
These names are derived 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 is the convention).
[edit] Join algorithms
There are three fundamental algorithms to perform a join operation.
[edit] Nested loops
This is the simplest join algorithm. For each tuple in the outer join relation, the entire inner join relation is scanned, and any tuples that match the join condition are added to the result set. Naturally, this algorithm performs poorly if either the inner or outer join relation is very large. The performance though can be enhanced if the inner relation has an index on joining column.
A refinement to this technique is called "block nested loops" (BNL): for every block in the outer relation, the entire inner relation is scanned. For each match between the current inner tuple and one of the tuples in the current block of the outer relation, a tuple is added to the join result set. This variant means that more computation is done for each tuple of the inner relation, but far fewer scans of the inner relation are required.
[edit] Merge join
If both join relations are sorted by the join attribute, the join can be performed trivially:
- For each tuple in the outer relation,
- 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.
- For each matching tuple in the current inner group, add a tuple to the join result. Once the inner group has been exhausted, both the inner and outer scans can be advanced to the next group.
This is one reason why many optimizers keep track of the sort order of query nodes — if one or both input relations to a merge join is already sorted on the join attribute, an additional sort is not required. 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
[edit] Hash join
[edit] Join optimization
[edit] Semi join
A semi join is an optimization technique for joins on distributed databases. The join predicates are applied in multiple phases, starting with the earliest possible. This can reduce the size of the intermediate results that must be exchanged with remote nodes, thus reducing inter node network traffic. It can be improved with a Bloom filter (hashing).
- See also: Query optimizer
