Services or capabilities described in Amazon Web Services documentation might vary by Region. To see the differences applicable to the China Regions, see Getting Started with Amazon Web Services in China (PDF).

Table JOIN

SQL Server Usage

ANSI JOIN

SQL Server supports the standard ANSI join types.

APPLY

SQL Server also supports the APPLY operator, which is somewhat similar to a join. However, APPLY operators enable the creation of a correlation between <Set A> and <Set B> such that <Set B> may consist of a sub query, a VALUES row value constructor, or a table valued function that is evaluated for each row of <Set A> where the <Set B> query can reference columns from the current row in <Set A>. This functionality isn’t possible with any type of standard JOIN operator.

There are two APPLY types:

ANSI SQL 89 JOIN

Up until version 2008R2, SQL Server also supported the old-style JOIN syntax including LEFT and RIGHT OUTER JOIN.

The ANSI syntax for a CROSS JOIN operator was to list the sets in the FROM clause using commas as separators.

SELECT * FROM Table1,
  Table2,
  Table3...

To perform an INNER JOIN, you only needed to add the JOIN predicate as part of the WHERE clause.

SELECT * FROM Table1,
  Table2
WHERE Table1.Column1 = Table2.Column1

Although the ANSI standard didn’t specify outer joins at the time, most RDBMS supported them in one way or another. T-SQL supported outer joins by adding an asterisk to the left or the right of equality sign of the join predicate to designate the reserved table.

SELECT * FROM Table1,
  Table2
WHERE Table1.Column1 *= Table2.Column1

To perform a FULL OUTER JOIN, asterisks were placed on both sides of the equality sign of the join predicate.

As of SQL Server 2008R2, outer joins using this syntax have been deprecated. For more information, see Deprecated Database Engine Features in SQL Server 2008 R2 in the SQL Server documentation.

Syntax

CROSS JOIN

FROM <Table Source 1>
  CROSS JOIN
  <Table Source 2>

-- ANSI 89
FROM <Table Source 1>,
  <Table Source 2>

INNER / OUTER JOIN

FROM <Table Source 1>
  [ { INNER | { { LEFT | RIGHT | FULL } [ OUTER ] } }] JOIN
  <Table Source 2>
  ON <JOIN Predicate>

-- ANSI 89
FROM <Table Source 1>,
  <Table Source 2>
WHERE <Join Predicate>
<Join Predicate>:: <Table Source 1 Expression> | = | *= | =* | *=* <Table Source 2 Expression>

APPLY

FROM <Table Source 1>
  { CROSS | OUTER } APPLY
  <Table Source 2>
<Table Source 2>:: <SELECT sub-query> | <Table Valued UDF> | <VALUES clause>

Examples

Create the Orders and Items tables.

CREATE TABLE Items
(
  Item VARCHAR(20) NOT NULL
  PRIMARY KEY
  Category VARCHAR(20) NOT NULL,
  Material VARCHAR(20) NOT NULL
);

INSERT INTO Items (Item, Category, Material)
VALUES
('M8 Bolt', 'Metric Bolts', 'Stainless Steel'),
('M8 Nut', 'Metric Nuts', 'Stainless Steel'),
('M8 Washer', 'Metric Washers', 'Stainless Steel'),
('3/8" Bolt', 'Imperial Bolts', 'Brass')

CREATE TABLE OrderItems
(
  OrderID INT NOT NULL,
  Item VARCHAR(20) NOT NULL
  REFERENCES Items(Item),
  Quantity SMALLINT NOT NULL,
  PRIMARY KEY(OrderID, Item)
);

INSERT INTO OrderItems (OrderID, Item, Quantity)
VALUES
(1, 'M8 Bolt', 100),
(2, 'M8 Nut', 100),
(3, 'M8 Washer', 200)

INNER JOIN

SELECT *
FROM Items AS I
  INNER JOIN
  OrderItems AS OI
  ON I.Item = OI.Item;
-- ANSI SQL 89
SELECT *
FROM Items AS I,
  OrderItems AS OI
WHERE I.Item = OI.Item;

LEFT OUTER JOIN

Find Items that were never ordered.

SELECT I.Item
FROM Items AS I
  LEFT OUTER JOIN
  OrderItems AS OI
  ON I.Item = OI.Item
WHERE OI.OrderID IS NULL;

-- ANSI SQL 89
SELECT Item
FROM
(
  SELECT I.Item, O.OrderID
  FROM Items AS I,
    OrderItems AS OI
  WHERE I.Item *= OI.Item
) AS LeftJoined
WHERE LeftJoined.OrderID IS NULL;

FULL OUTER JOIN

CREATE TABLE T1(Col1 INT, COl2 CHAR(2));
CREATE TABLE T2(Col1 INT, COl2 CHAR(2));

INSERT INTO T1 (Col1, Col2)
VALUES (1, 'A'), (2,'B');

INSERT INTO T2 (Col1, Col2)
VALUES (2,'BB'), (3,'CC');

SELECT *
FROM T1
  FULL OUTER JOIN
  T2
  ON T1.Col1 = T2.Col1;

The preceding example produces the following results.

Col1  COl2  Col1  COl2
1     A     NULL  NULL
2     B     2     BB
NULL  NULL  3     CC

For more information, see FROM clause plus JOIN, APPLY, PIVOT (Transact-SQL) in the SQL Server documentation.

PostgreSQL Usage

Amazon Aurora PostgreSQL-Compatible Edition (Aurora PostgreSQL) supports all types of joins in the same way as SQL Server.

PostgreSQL doesn’t support APPLY options. You can replace them with INNER JOIN LATERAL and LEFT JOIN LATERAL.

Syntax

FROM
    <Table Source 1> CROSS JOIN <Table Source 2>
  | <Table Source 1> INNER JOIN <Table Source 2>
    ON <Join Predicate>
  | <Table Source 1> {LEFT|RIGHT|FULL} [OUTER] JOIN <Table Source 2>
    ON <Join Predicate>

Migration Considerations

For most JOIN statements, the syntax should be equivalent and no rewrites should be needed. Find the differences following.

Examples

Create the Orders and Items tables.

CREATE TABLE Items
(
  Item VARCHAR(20) NOT NULL
  PRIMARY KEY
  Category VARCHAR(20) NOT NULL,
  Material VARCHAR(20) NOT NULL
);

INSERT INTO Items (Item, Category, Material)
VALUES
('M8 Bolt', 'Metric Bolts', 'Stainless Steel'),
('M8 Nut', 'Metric Nuts', 'Stainless Steel'),
('M8 Washer', 'Metric Washers', 'Stainless Steel'),
('3/8" Bolt', 'Imperial Bolts', 'Brass')

CREATE TABLE OrderItems
(
  OrderID INT NOT NULL,
  Item VARCHAR(20) NOT NULL
  REFERENCES Items(Item),
  Quantity SMALLINT NOT NULL,
  PRIMARY KEY(OrderID, Item)
);

INSERT INTO OrderItems (OrderID, Item, Quantity)
VALUES
(1, 'M8 Bolt', 100),
(2, 'M8 Nut', 100),
(3, 'M8 Washer', 200)

INNER JOIN

SELECT *
FROM Items AS I
  INNER JOIN
  OrderItems AS OI
  ON I.Item = OI.Item;

LEFT OUTER JOIN

Find Items that were never ordered.

SELECT Item
FROM Items AS I
  LEFT OUTER JOIN
  OrderItems AS OI
  ON I.Item = OI.Item
WHERE OI.OrderID IS NULL;

FULL OUTER JOIN

CREATE TABLE T1(Col1 INT, COl2 CHAR(2));
CREATE TABLE T2(Col1 INT, COl2 CHAR(2));

INSERT INTO T1 (Col1, Col2)
VALUES (1, 'A'), (2,'B');

INSERT INTO T2 (Col1, Col2)
VALUES (2,'BB'), (3,'CC');

SELECT *
FROM T1
FULL OUTER JOIN
T2
ON T1.Col1 = T2.Col1;

The preceding example produces the following results.

Col1  COl2  Col1  COl2
1     A     NULL  NULL
2     B     2     BB
NULL  NULL  3     CC

Summary

The following table shows similarities, differences, and key migration considerations.

For more information, see Controlling the Planner with Explicit JOIN Clauses and Joins Between Tables in the PostgreSQL documentation.