The RIGHT JOIN keyword returns all records from the right table (table2), and the matched records from the left table (table1). The result is NULL from the left side, when there is no match.
Basically, it looks like this.
The highlighted green part from the image above is our query results when we apply RIGHT JOIN between 2 tables.
Syntax:
SELECT a.column2, b.column2
FROM table a
RIGHT JOIN table b
ON a.column = b.column;I am using alias as well in the syntax. You can use name of the table instead of alias. Alias makes it better to write the query. Learn more about alias in this post:
Column And Table Alias In Postgres
RIGHT JOIN Query Example 2 tables
I am using the database for all examples. It is available on my Github public repo
SELECT c.contact_name, o.order_date
FROM customers c
RIGHT JOIN orders o ON c.customer_id = o.customer_id
//Output
contact_name. order_date
....
....
....
"Guillermo Fernández" "1998-05-05"
"Jytte Petersen" "1998-05-06"
"Michael Holz" "1998-05-06"
"Laurence Lebihan" "1998-05-06"
"Paula Wilson" "1998-05-06"
...
830 rows
It’s the same example from this LEFT JOIN post: LEFT JOIN In Postgres
In LEFT JOIN we saw the rows with some null values in the right column because it was including all the columns from left and the matches from right. Over here, all the columns in the right table are already in the left table so we don’t see any null values in the results.
What will happen if we apply RIGHT JOIN to 3 tables?
RIGHT JOIN Query Example 3 tables
SELECT c.contact_name, o.order_date, od.product_id
FROM customers c
RIGHT JOIN orders o ON c.customer_id = o.customer_id
RIGHT JOIN order_details od ON od.order_id = o.order_id
//Output
contact_name. order_date. count
...
...
...
"Paula Wilson" "1998-05-06" 73
"Paula Wilson" "1998-05-06" 75
"Paula Wilson" "1998-05-06" 77
...
2155 rows
This example is also from this LEFT JOIN post: LEFT JOIN In Postgres
And same as the previous example, over here, all the columns in the right table are already in the left table so we don’t see any null values in the results.
We are seeing the same names in many rows because we haven’t used GROUP BY clause. If we want to filter this data more, we can add an aggregate function and add GROUP BY clause.
