Exploring the Types of SQL Joins: CROSS JOIN and RIGHT JOIN

Relational databases can contain dozens of tables, each with its own unique data. To make full use of this information, SQL (Structured Query Language) provides various types of join operations to combine rows from two or more tables. Among these types, CROSS JOIN and RIGHT JOIN are valid ones which are the correct answers to our quiz question.

CROSS JOIN

CROSS JOIN in SQL is a join operation that produces the Cartesian product of the records from the tables that are participating in the join. In simpler terms, when you perform a CROSS JOIN on two tables, every row from the first table is combined with every row from the second table.

SELECT * FROM table1 CROSS JOIN table2;

In this example, each record from table1 would be combined with each record from table2. This type of join is generally not used in day-to-day operations as it can yield a vast amount of data. However, it's very useful at times for generating all possible combinations between two datasets.

RIGHT JOIN

On the other hand, RIGHT JOIN, also known as RIGHT OUTER JOIN, returns all the rows from the right table and the matching rows from the left table. If there is no match, NULL is returned.

SELECT table1.column1, table2.column2 FROM table1 RIGHT JOIN table2 ON table1.common_field = table2.common_field;

This will return all entries from table2 (right table) and any matching entries from table1 (left table). If no match is found in table1, then the result is NULL.

This type of join can be particularly useful when you want to include all the records from one table (right table) and only matching records from the other (left table).

Even though in the question OUTER JOIN was marked as incorrect, it's worth noting that both LEFT JOIN, RIGHT JOIN and also FULL JOIN are types of OUTER JOIN. What differentiates them is just the directionality of the join. OUTER JOIN is used to fetch data if present in either of the tables. Therefore, it is not entirely incorrect but perhaps not as specific as the question required.

As best practice, when using joins, ensure that the tables have proper indexing on the join keys to optimize for faster queries. This is especially important when working with large datasets. Always validate data after joining to ensure accuracy as well.