Left Join

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

Left join orders to users so unmatched orders remain. Treat this as a production helper: match the contracted return shape, including empty and duplicate inputs.

Sometimes you want to keep every row from your main table, even if the other table has no match. A left join does that: all rows of the left table stay, and the columns from the right table are empty (None) when no match is found. Join orders (left) to users on user_id with a left join. 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 the last line so result holds your joined table.

Requirements

• Use a left join with orders as the left table.
• Keep every order, even when its user does not exist.
• Select order_id, user_id, amount, name, email.
• Sort by order_id and store the DataFrame in result.

Constraints

• orders has 5 rows, so the result must have exactly 5 rows.
• Join with how="left" on user_id.
• An order with no matching user has None in name and email.

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] 104 | 9 | 40.0 | None | None 105 | 1 | 60.0 | Ada | [email protected] Order 104 survives with null name/email.

Topics: lakebench, pyspark, left.

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