SQL Join

SQL JOIN is a mechanism that allows you to combine data from two or more tables in a relational database based on a standard column between them. The basic idea of a join is to match the rows in one table with the rows in another based on a specified condition, usually, the values in a column or columns shared between the tables.

There are different types of SQL joins, but the most commonly used ones are:

INNER JOIN: Returns only the rows with matching values in both tables.

LEFT JOIN: Returns all the rows from the left table and the matching rows from the right table. If there are no matching rows in the right table, the result will contain NULL values for the correct columns.

RIGHT JOIN: Returns all the rows from the right table and the matching rows from the left table. If there are no matching rows in the left table, the result will contain NULL values for the left table columns.

FULL OUTER JOIN: Returns all the rows from both tables, with NULL values for the columns that do not have a match in the other table.

To perform a join in SQL, you need to specify the tables you want to join, the columns you want to match on, and the type of join you want to perform. For example, the syntax for an inner join looks like this:

SELECT column1, column2, …
FROM table1
INNER JOIN table2
ON table1.column = table2.column;

The above query will return all the rows from table1 and table2 where the values in table1.column and table2.column match based on the specified join condition.

The following query will find all the empty orders (orders with no associated line items), a WHERE clause can filter results where an order has no corresponding order line ID:

SELECT 
  orders.id, orders.customer_id, orders.date, …
FROM orders
LEFT JOIN order_lines ON order_lines.order_id = order.id
WHERE order_lines.id is NULL

Joining tables is a powerful feature of SQL that allows you to combine data from different tables to create more meaningful results. It is an essential tool for working with relational databases, especially when dealing with large datasets.