GROUP and JOIN operations in Hadoop - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to group data by the 'department' field.

Hadoop

grouped_data = data.[1]('department')

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

Aselect

BgroupBy

Cfilter

Djoin

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'join' instead of 'groupBy' to group data.

Using 'filter' which selects rows, not groups.

The groupBy function groups data by the specified column, here 'department'.

2fill in blank

medium

Complete the code to join two datasets on the 'employee_id' column.

Hadoop

joined_data = data1.[1](data2, 'employee_id')

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

Ajoin

Bselect

CgroupBy

Dfilter

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'groupBy' instead of 'join' to combine datasets.

Using 'filter' which only selects rows.

The join function combines two datasets based on a common column, here 'employee_id'.

3fill in blank

hard

Fix the error in the join operation by completing the code correctly.

Hadoop

result = data1.join(data2, data1.[1] == data2.employee_id)

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

Aemployee_id

Bdept_id

Cid

Dname

Attempts:

3 left

💡 Hint

Common Mistakes

Using a wrong column like 'id' or 'name' for join condition.

Not matching the same key columns in join.

The join condition must compare the same key columns. Here, data1's employee_id matches data2's employee_id.

4fill in blank

hard

Fill both blanks to create a dictionary of employee names and their project counts for employees with more than 2 projects.

Hadoop

result = {emp.name: emp.[1] for emp in employees if emp.[1] [2] 2}

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

Aproject_count

Bprojects

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'projects' which might be a list, not a count.

Using '<' instead of '>' to filter employees.

We use project_count to get the number of projects per employee and filter those with more than 2 projects using >.

5fill in blank

hard

Fill all three blanks to create a dictionary of department names and total salaries for departments with total salary above 100000.

Hadoop

dept_salaries = {dept.[1]: sum(emp.[2] for emp in dept.employees) for dept in departments if sum(emp.[2] for emp in dept.employees) [3] 100000}

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

Aname

Bsalary

C>

Ddepartment

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'department' instead of 'name' for department key.

Using '<' instead of '>' for filtering.

Using wrong attribute for salary.

We use name for department names, sum salary for total salaries, and filter departments with total salary > 100000.

Practice

1. What does the GROUP BY operation do in Hadoop data processing?

easy

A. It organizes data by keys to summarize or analyze groups.

B. It merges two datasets based on matching keys.

C. It deletes duplicate records from a dataset.

D. It sorts data in ascending order.

Solution

1. Step 1: Understand the purpose of GROUP BY

   The GROUP BY operation groups rows that have the same values in specified columns into summary rows.

2. Step 2: Differentiate from JOIN

   JOIN merges datasets based on keys, but GROUP BY organizes data within one dataset by keys.

3. Final Answer:

   It organizes data by keys to summarize or analyze groups. -> Option A

4. Quick Check:

   GROUP BY = Organize by keys [OK]

Hint: GROUP BY groups data by keys for summaries [OK]

Common Mistakes:

• Confusing GROUP BY with JOIN
• Thinking GROUP BY deletes duplicates
• Assuming GROUP BY sorts data

2. Which of the following is the correct syntax to join two datasets table1 and table2 on the key id in HiveQL (Hadoop SQL)?

easy

A. SELECT * FROM table1 GROUP BY table2.id;

B. SELECT * FROM table1 JOIN table2 ON table1.id = table2.id;

C. SELECT * FROM table1 WHERE table1.id = table2.id;

D. SELECT * FROM table1, table2 ON table1.id = table2.id;

Solution

1. Step 1: Identify correct JOIN syntax

   The standard JOIN syntax in HiveQL uses ON to specify the join condition: JOIN table2 ON table1.id = table2.id.

2. Step 2: Eliminate incorrect options

   SELECT * FROM table1 WHERE table1.id = table2.id; uses WHERE incorrectly for join; SELECT * FROM table1 GROUP BY table2.id; uses GROUP BY wrongly; SELECT * FROM table1, table2 ON table1.id = table2.id; incorrectly uses ON with comma-separated tables.

3. Final Answer:

   SELECT * FROM table1 JOIN table2 ON table1.id = table2.id; -> Option B

4. Quick Check:

   JOIN with ON condition = SELECT * FROM table1 JOIN table2 ON table1.id = table2.id; [OK]

Hint: Use JOIN ... ON for correct Hadoop SQL joins [OK]

Common Mistakes:

• Using WHERE instead of ON for JOIN
• Confusing GROUP BY with JOIN
• Using ON clause with old-style comma join

3. Given two datasets in Hive:
table1(id INT, value INT)
table2(id INT, category STRING)
What is the output of this query?

SELECT t2.category, SUM(t1.value) FROM table1 t1 JOIN table2 t2 ON t1.id = t2.id GROUP BY t2.category;

medium

A. Sum of values grouped by id from table1.

B. List of categories with no aggregation.

C. Sum of values grouped by category from table2.

D. Error due to missing GROUP BY column.

Solution

1. Step 1: Understand JOIN and GROUP BY

   The JOIN merges rows from table1 and table2 where ids match. Then GROUP BY groups results by category.

2. Step 2: Analyze aggregation

   SUM(t1.value) calculates total value per category from joined data.

3. Final Answer:

   Sum of values grouped by category from table2. -> Option C

4. Quick Check:

   JOIN + GROUP BY category = sum per category [OK]

Hint: JOIN then GROUP BY aggregates data by joined keys [OK]

Common Mistakes:

• Ignoring GROUP BY column in SELECT
• Confusing grouping by id vs category
• Expecting list without aggregation

4. You wrote this Hive query:

SELECT t1.id, COUNT(*) FROM table1 t1 JOIN table2 t2 ON t1.id = t2.id;

But it gives an error. What is the likely cause?

medium

A. Missing GROUP BY clause for aggregation.

B. Incorrect JOIN syntax.

C. COUNT(*) cannot be used with JOIN.

D. Table aliases are not allowed.

Solution

1. Step 1: Identify aggregation usage

   COUNT(*) is an aggregate function and requires GROUP BY for non-aggregated columns in SELECT.

2. Step 2: Check query structure

   Since t1.id is selected without aggregation, GROUP BY t1.id is needed.

3. Final Answer:

   Missing GROUP BY clause for aggregation. -> Option A

4. Quick Check:

   Aggregate needs GROUP BY [OK]

Hint: Use GROUP BY when selecting non-aggregated columns with COUNT [OK]

Common Mistakes:

• Forgetting GROUP BY with aggregate functions
• Thinking JOIN syntax is wrong
• Assuming COUNT(*) disallowed with JOIN

5. You have two large datasets:
sales(order_id, product_id, amount)
products(product_id, category)
You want to find total sales amount per category but only for categories with total sales above 1000. Which Hive query correctly achieves this?

hard

A. SELECT p.category, SUM(s.amount) FROM sales s JOIN products p ON s.product_id = p.product_id HAVING SUM(s.amount) > 1000;

B. SELECT p.category, SUM(s.amount) FROM sales s JOIN products p ON s.product_id = p.product_id WHERE SUM(s.amount) > 1000 GROUP BY p.category;

C. SELECT p.category, SUM(s.amount) FROM sales s JOIN products p ON s.product_id = p.product_id GROUP BY p.category WHERE SUM(s.amount) > 1000;

D. SELECT p.category, SUM(s.amount) FROM sales s JOIN products p ON s.product_id = p.product_id GROUP BY p.category HAVING SUM(s.amount) > 1000;

Solution

1. Step 1: Join sales and products on product_id

   This merges sales with product categories.

2. Step 2: Group by category and sum amounts

   GROUP BY p.category with SUM(s.amount) calculates total sales per category.

3. Step 3: Filter groups with HAVING

   HAVING filters groups after aggregation; WHERE cannot filter aggregated sums.

4. Final Answer:

   SELECT p.category, SUM(s.amount) FROM sales s JOIN products p ON s.product_id = p.product_id GROUP BY p.category HAVING SUM(s.amount) > 1000; -> Option D

5. Quick Check:

   Use HAVING to filter aggregated groups [OK]

Hint: Use HAVING to filter after GROUP BY aggregation [OK]

Common Mistakes:

• Using WHERE instead of HAVING for aggregation filter
• Omitting GROUP BY when aggregating
• Incorrect JOIN condition