Inner Join

PySpark data engineering interview problem. Difficulty: beginner. Pattern: Joins. About 12 minutes. Free to practice.

Inner join orders to users on user_id. Treat this as a production helper: match the contracted return shape, including empty and duplicate inputs.

You have 2 tables. orders has order_id, user_id and amount. users has user_id, name and email. You want each order together with the name and email of the person who placed it. Use an inner join on user_id. An inner join keeps only the rows that have a match on both sides, so an order with a user that is not in users is left out. Select order_id, user_id, amount, name and email, and sort by order_id. The editor already creates orders and users and ends with result = orders. Change that last line so result holds your joined table.

Requirements

• Join orders and users on user_id with how="inner".
• Select order_id, user_id, amount, name, email.
• Sort by order_id.
• Store the final DataFrame in result.

Constraints

• orders has 5 rows and users has 4 rows.
• Join with orders.join(users, on="user_id", how="inner"), which keeps one user_id column.
• Only orders whose user_id exists in users appear in the result.

Examples

Input: orders + users Output: order_id | user_id | amount | name | email 101 | 1 | 50.0 | Ada | [email protected] 102 | 2 | 75.0 | Alan | [email protected] 103 | 3 | 20.0 | Grace | [email protected] 105 | 1 | 60.0 | Ada | [email protected] Order 104 has user_id 9 with no user row, so it drops.

Topics: lakebench, pyspark, inner.

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