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.