A JOIN and a UNION are both SQL operations used to combine data, but they serve different purposes and work in distinct ways. A JOIN merges columns from two or more tables based on a related column (like a foreign key), allowing you to combine data horizontally. For example, if you have a users table and an orders table, a JOIN could link each user to their orders by matching a user_id column. In contrast, a UNION stacks rows from two or more queries vertically, combining results that share the same structure. For instance, if you have two tables with identical columns (e.g., employees_2022 and employees_2023), a UNION would combine all rows into a single result set.
JOINs are ideal for querying related data across tables. Common types include INNER JOIN (returns matching rows), LEFT JOIN (returns all rows from the left table and matches from the right), and FULL OUTER JOIN (returns all rows from both tables). For example, an INNER JOIN between users and orders would exclude users with no orders, while a LEFT JOIN would include all users, even those without orders. JOINs rely on keys to establish relationships, making them essential for normalized databases where data is spread across tables. On the other hand, UNION requires the queries to have the same number of columns, with compatible data types, and typically removes duplicate rows unless UNION ALL is used. For example, combining sales data from two regions stored in separate tables with identical schemas would be a good use case for UNION.
The key difference lies in their use cases and output structure. JOINs expand the dataset horizontally by adding columns from related tables, while UNIONs expand vertically by adding rows from similar datasets. Use a JOIN when you need to combine columns (e.g., merging user details with their orders) and a UNION when aggregating rows from similar sources (e.g., combining monthly sales reports). A common mistake is using a UNION to combine unrelated tables, which fails if their structures don’t align. Similarly, misapplying a JOIN when a UNION is needed can lead to incomplete or incorrect results. Understanding these distinctions ensures efficient querying and accurate data retrieval.