SQL Joins

In SQL, a join is used to combine data from two or more tables based on a common column between them. A join is performed by matching the values in the specified columns of the tables being joined, and creating a new result set that includes data from all of the tables. There are several types of joins available in SQL, including inner join, left join, right join, and full outer join.

Here is an example of an inner join between two tables, Orders and Customers, based on a common column, CustomerID:

SELECT *
FROM Orders
INNER JOIN Customers
ON Orders.CustomerID = Customers.CustomerID;

In this example, the INNER JOIN keyword is used to combine data from the Orders and Customers tables based on the CustomerID column. The ON keyword specifies the condition that must be met for a record to be included in the result set. This query returns all columns from both tables where the CustomerID values match.

Another type of join is a left join, which includes all records from the left table (Orders) and matching records from the right table (Customers). If there is no matching record in the right table, the result will contain NULL values for the right table's columns. Here is an example:

SELECT *
FROM Orders
LEFT JOIN Customers
ON Orders.CustomerID = Customers.CustomerID;
 

In this example, the LEFT JOIN keyword is used to combine data from the Orders and Customers tables. This query returns all columns from both tables, with all records from the Orders table and matching records from the Customers table based on the CustomerID column. If there is no matching record in the Customers table, the result will contain NULL values for the Customers table's columns.

There are also right join and full outer join, which work similarly to left join but include all records from the right table or both tables, respectively.

Joins are powerful tools in SQL that allow for the combination of data from multiple tables based on a common column. They are often used to create more meaningful data sets and enable data analysts and database administrators to work with larger and more complex data sets.