Join order and performance impact in SQL - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to select all columns from the two tables joined on the customer ID.

SQL

SELECT * FROM customers [1] orders ON customers.customer_id = orders.customer_id;

Drag options to blanks, or click blank then click option'

AJOIN

BWHERE

CGROUP BY

DORDER BY

Attempts:

3 left

💡 Hint

Common Mistakes

Using WHERE instead of JOIN to combine tables.

Using GROUP BY or ORDER BY which are for grouping or sorting, not joining.

The JOIN keyword is used to combine rows from two or more tables based on a related column.

2fill in blank

medium

Complete the code to perform an inner join between products and sales on product_id.

SQL

SELECT products.name, sales.quantity FROM products [1] JOIN sales ON products.product_id = sales.product_id;

Drag options to blanks, or click blank then click option'

ALEFT

BRIGHT

CFULL

DINNER

Attempts:

3 left

💡 Hint

Common Mistakes

Using LEFT or RIGHT join which include unmatched rows from one table.

Using FULL join which includes unmatched rows from both tables.

An INNER JOIN returns only the rows where there is a match in both tables.

3fill in blank

hard

Fix the error in the join condition to correctly join employees and departments on department_id.

SQL

SELECT employees.name, departments.name FROM employees JOIN departments ON employees.[1] = departments.department_id;

Drag options to blanks, or click blank then click option'

Adept_id

Bdepartment

Cdepartment_id

Ddept

Attempts:

3 left

💡 Hint

Common Mistakes

Using a wrong or incomplete column name in the join condition.

Using a column that does not exist in the employees table.

The join condition must use the exact column name department_id from employees to match departments.

4fill in blank

hard

Fill both blanks to write a query that joins orders and customers, selecting order_id and customer name, filtering only orders with amount greater than 100.

SQL

SELECT orders.order_id, customers.name FROM orders [1] JOIN customers ON orders.customer_id = customers.customer_id WHERE orders.amount [2] 100;

Drag options to blanks, or click blank then click option'

AINNER

B>

C<

DLEFT

Attempts:

3 left

💡 Hint

Common Mistakes

Using LEFT join which includes unmatched rows.

Using < instead of > in the WHERE clause.

Use INNER JOIN to get matching rows and > to filter amounts greater than 100.

5fill in blank

hard

Fill all three blanks to write a query that joins three tables: sales, products, and categories. Select product name and category name for sales with quantity less than 50.

SQL

SELECT products.name, categories.name FROM sales [1] JOIN products ON sales.product_id = products.product_id [2] JOIN categories ON products.category_id = categories.category_id WHERE sales.quantity [3] 50;

Drag options to blanks, or click blank then click option'

AINNER

B<

DLEFT

Attempts:

3 left

💡 Hint

Common Mistakes

Using LEFT join which includes unmatched rows.

Using > instead of < in the WHERE clause.

Use INNER JOIN for both joins to get matching rows only, and < to filter quantity less than 50.

Practice

1. Which statement best describes the impact of join order on SQL query results?

easy

A. Join order affects query speed but not the final result data.

B. Join order changes the final result data returned by the query.

C. Join order always causes syntax errors if incorrect.

D. Join order determines the number of columns in the result.

Solution

1. Step 1: Understand join order effect on data

   Join order does not change the rows or columns returned if the joins are correct and conditions are the same.

2. Step 2: Understand join order effect on performance

   Join order can affect how fast the database processes the query but not the actual data returned.

3. Final Answer:

   Join order affects query speed but not the final result data. -> Option A

4. Quick Check:

   Join order impacts speed, not data [OK]

Hint: Join order changes speed, not output data [OK]

Common Mistakes:

• Thinking join order changes the result rows
• Confusing join order with join type
• Assuming join order causes syntax errors

2. Which SQL join syntax is correct for joining two tables employees and departments on department_id?

easy

A. SELECT * FROM employees JOIN departments USING employees.department_id = departments.department_id;

B. SELECT * FROM employees JOIN departments WHERE employees.department_id = departments.department_id;

C. SELECT * FROM employees, departments ON employees.department_id = departments.department_id;

D. SELECT * FROM employees JOIN departments ON employees.department_id = departments.department_id;

Solution

1. Step 1: Identify correct JOIN syntax

   The correct syntax uses JOIN ... ON condition to specify join keys.

2. Step 2: Check each option

   SELECT * FROM employees JOIN departments ON employees.department_id = departments.department_id; uses JOIN ... ON correctly. USING with a full equality condition is invalid as USING expects column names only. JOIN ... WHERE is invalid syntax for explicit joins. Comma-separated tables with ON is invalid.

3. Final Answer:

   SELECT * FROM employees JOIN departments ON employees.department_id = departments.department_id; -> Option D

4. Quick Check:

   JOIN ... ON is correct syntax [OK]

Hint: Use JOIN ... ON for correct join syntax [OK]

Common Mistakes:

• Using WHERE instead of ON for join condition
• Mixing comma joins with ON clause
• Incorrect USING clause syntax

3. Given tables orders (1000 rows) and customers (10 rows), which join order is likely faster?
Query 1: SELECT * FROM orders JOIN customers ON orders.customer_id = customers.id;
Query 2: SELECT * FROM customers JOIN orders ON customers.id = orders.customer_id;

medium

A. Query 1 is faster because orders is first.

B. Query 2 is faster because customers is first and smaller.

C. Both queries have the same speed always.

D. Query 2 will cause an error due to join order.

Solution

1. Step 1: Analyze table sizes and join order

   Joining smaller tables first often helps performance because fewer rows are processed early.

2. Step 2: Compare queries

   Query 2 starts with the smaller customers table (10 rows), likely reducing intermediate data size and speeding up join.

3. Final Answer:

   Query 2 is faster because customers is first and smaller. -> Option B

4. Quick Check:

   Smaller table first improves speed [OK]

Hint: Join smaller tables first for better speed [OK]

Common Mistakes:

• Assuming join order never affects speed
• Thinking larger table first is always better
• Believing join order causes errors

4. Consider this SQL query:
SELECT * FROM A JOIN B ON A.id = B.a_id JOIN C ON B.id = C.b_id;
It runs very slowly. Which fix can improve performance by changing join order?

medium

A. Remove the join with table C.

B. Add WHERE A.id = B.a_id instead of ON clause.

C. Rewrite as SELECT * FROM C JOIN B ON B.id = C.b_id JOIN A ON A.id = B.a_id;

D. Use CROSS JOIN instead of JOIN.

Solution

1. Step 1: Understand join order impact on performance

   Changing join order to start with smaller or more selective tables can speed up query execution.

2. Step 2: Evaluate options

   Rewriting as SELECT * FROM C JOIN B ON B.id = C.b_id JOIN A ON A.id = B.a_id; changes join order to start with C, possibly smaller or more filtered, improving speed. Replacing ON with WHERE breaks join syntax. Removing a join loses data. CROSS JOIN explodes row count without filters.

3. Final Answer:

   Rewrite as SELECT * FROM C JOIN B ON B.id = C.b_id JOIN A ON A.id = B.a_id; -> Option C

4. Quick Check:

   Changing join order can improve speed [OK]

Hint: Reorder joins to start with smaller tables [OK]

Common Mistakes:

• Replacing ON with WHERE for joins
• Removing necessary joins
• Using CROSS JOIN without filtering

5. You have three tables: sales (1 million rows), products (1000 rows), and categories (50 rows). To optimize a query joining all three, which join order is best for performance?
Options:
A) sales JOIN products JOIN categories
B) products JOIN sales JOIN categories
C) categories JOIN products JOIN sales
D) sales JOIN categories JOIN products

hard

A. Join categories first, then products, then sales.

B. Join products first, then sales, then categories.

C. Join sales first, then products, then categories.

D. Join sales first, then categories, then products.

Solution

1. Step 1: Analyze table sizes and join order impact

   Joining smaller tables first reduces intermediate result size and speeds up query.

2. Step 2: Evaluate options based on table sizes

   Categories (50 rows) is smallest, then products (1000 rows), then sales (1 million rows). Joining in order categories -> products -> sales is best.

3. Final Answer:

   Join categories first, then products, then sales. -> Option A

4. Quick Check:

   Smallest to largest join order improves speed [OK]

Hint: Join tables from smallest to largest for best speed [OK]

Common Mistakes:

• Joining largest table first slows query
• Ignoring table size in join order
• Assuming join order doesn't affect performance