The SQL JOIN clause is used in a
DELETE statement to combine rows from two or more tables based on a related column between them. In other words, it combines rows from two or more tables in a relational database based on the values of the matched column.
INNER JOIN: This is the most common type of JOIN. It returns only the rows that have matching values in both tables.
LEFT OUTER JOIN): This type of JOIN returns all rows from the left table and the matching rows from the right table. If there is no match,
NULLvalues will be returned for right table's columns.
RIGHT OUTER JOIN): This is similar to a
LEFT JOIN, but it returns all rows from the right table and the matching rows from the left table.
FULL OUTER JOIN): This type of JOIN returns all rows from both tables, along with the matching rows from either table. If there is no match,
NULLvalues will be returned for both tables' non-matching columns.
CROSS JOIN: This type of JOIN returns the Cartesian product of the two tables, i.e., it returns every possible combination of rows from the two tables. It is also known as a Cartesian product join.
Here is an example of a simple SQL INNER JOIN statement:
INNER JOIN customers
ON orders.customer_id = customers.id;
This statement retrieves all columns from the
orders table and the
customers table, and returns only the rows where the
customer_id column in the
orders table matches the
id column in the
Use the appropriate type of JOIN: Always use the correct type of JOIN based on your requirements.
Join on the right columns: Always make sure that you're joining on the appropriate columns. If you're joining on a column that has duplicate values, you'll end up with duplicate rows in the result.
Use aliases: When you join multiple tables, it's a best practice to use aliases to make the query more readable.
Use table prefixes: When you join multiple tables that have columns with the same name, use prefixes in the aliases for columns to avoid ambiguity
Watch out on performance: JOINs can be a performance bottleneck, especially when working with large tables. Try to limit the number of JOINs in a single query and avoid using sub-queries that contain JOINs.
The order of JOINs matters: The order of JOINs can also affect the performance. Try to join the smallest table first and then join it with the next largest table.