Inner, left, right, and full outer joins in Apache Spark - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to perform an inner join between two DataFrames df1 and df2 on the column 'id'.

Apache Spark

joined_df = df1.join(df2, on='id', how='[1]')

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

Ainner

Bouter

Cright

Dleft

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'left' or 'right' instead of 'inner' will include unmatched rows from one side.

Using 'outer' will include all rows from both DataFrames.

The inner join returns only the rows with matching keys in both DataFrames.

2fill in blank

medium

Complete the code to perform a left join between df1 and df2 on the column 'user_id'.

Apache Spark

joined_df = df1.join(df2, on='user_id', how='[1]')

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

Aleft

Bright

Cinner

Dfull

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'inner' will exclude unmatched left rows.

Using 'right' will keep all right rows instead.

The left join returns all rows from the left DataFrame (df1) and matched rows from the right DataFrame (df2). Unmatched right rows are null.

3fill in blank

hard

Fix the error in the code to perform a right join between dfA and dfB on 'key'.

Apache Spark

result = dfA.join(dfB, on='key', how='[1]')

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

Ainner

Bleft

Cright

Douter

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'left' will keep all left rows, not right.

Using 'inner' excludes unmatched rows.

The right join keeps all rows from the right DataFrame (dfB) and matched rows from the left DataFrame (dfA). Unmatched left rows are null.

4fill in blank

hard

Fill both blanks to perform a full outer join between dfX and dfY on 'id' and select only rows where 'id' is not null.

Apache Spark

joined = dfX.join(dfY, on='id', how='[1]')
filtered = joined.filter(joined.id [2] None)

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

Aouter

B==

C!=

Dleft

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'left' or 'inner' will exclude some rows.

Filtering with '== None' keeps only null ids, which is usually unwanted.

A full outer join keeps all rows from both DataFrames. Filtering with != None keeps rows where 'id' exists (not null).

5fill in blank

hard

Fill all three blanks to create a dictionary of user names and their ages from dfUsers where age is greater than 20.

Apache Spark

user_dict = {row['[1]']: row['[2]'] for row in dfUsers.collect() if row['[3]'] > 20}

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

Aname

Bage

Did

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'id' instead of 'name' for keys.

Filtering on 'name' instead of 'age'.

This dictionary comprehension uses 'name' as keys and 'age' as values, filtering rows where 'age' is greater than 20.

Practice

1. Which type of join in Apache Spark returns only the rows that have matching keys in both DataFrames?

easy

A. Full outer join

B. Inner join

C. Right outer join

D. Left outer join

Solution

1. Step 1: Understand join types

   Inner join returns rows where keys match in both tables only.

2. Step 2: Compare with other joins

   Left, right, and full outer joins include unmatched rows from one or both tables.

3. Final Answer:

   Inner join -> Option B

4. Quick Check:

   Inner join = matching rows only [OK]

Hint: Inner join keeps only matching rows from both tables [OK]

Common Mistakes:

• Confusing inner join with left or right join
• Thinking full outer join returns only matches
• Assuming left join excludes unmatched rows

2. Which of the following is the correct syntax to perform a left outer join between two DataFrames df1 and df2 on column id in Apache Spark?

easy

A. df1.join(df2, df1.id == df2.id, 'inner')

B. df1.join(df2, df1.id == df2.id, 'full')

C. df1.join(df2, df1.id == df2.id, 'right_outer')

D. df1.join(df2, df1.id == df2.id, 'left')

Solution

1. Step 1: Identify join type keyword

   Left outer join uses 'left' as the join type string in Spark.

2. Step 2: Match syntax format

   The syntax is df1.join(df2, condition, 'left').

3. Final Answer:

   df1.join(df2, df1.id == df2.id, 'left') -> Option D

4. Quick Check:

   Left join keyword = 'left' [OK]

Hint: Use 'left' string for left outer join in join() method [OK]

Common Mistakes:

• Using 'left_outer' instead of 'left'
• Confusing join type strings like 'right_outer' for left join
• Omitting join condition or using wrong syntax

3. Given two DataFrames:
df1 = spark.createDataFrame([(1, 'A'), (2, 'B')], ['id', 'val1'])
df2 = spark.createDataFrame([(2, 'X'), (3, 'Y')], ['id', 'val2'])
What will be the result of df1.join(df2, 'id', 'right')?

medium

A. [ (1, 'A', null), (2, 'B', 'X'), (3, null, 'Y') ]

B. [ (1, 'A', null), (2, 'B', 'X') ]

C. [ (2, 'B', 'X'), (3, null, 'Y') ]

D. [ (2, 'B', 'X') ]

Solution

1. Step 1: Understand right join behavior

   Right join keeps all rows from right DataFrame (df2) and matches from left (df1).

2. Step 2: Match rows by 'id'

   Id=2 matches in both, id=3 only in df2, so df1 columns are null for id=3.

3. Final Answer:

   [ (1, 'A', null), (2, 'B', 'X'), (3, null, 'Y') ] -> Option A

4. Quick Check:

   Right join keeps all right rows [OK]

Hint: Right join keeps all right table rows, fills left with nulls if no match [OK]

Common Mistakes:

• Expecting left table rows to appear in right join
• Confusing right join with full outer join
• Misunderstanding null placement for unmatched rows

4. You wrote this Spark join code:
df1.join(df2, df1.id == df2.id, 'full_outerjoin')
But it throws an error. What is the likely cause?

medium

A. The join type string should be 'full' instead of 'full_outerjoin'

B. The join condition must use a string column name, not an expression

C. You must use 'outer' instead of 'full_outerjoin' as join type

D. Spark does not support full outer joins

Solution

1. Step 1: Check valid join type strings in Spark

   Spark expects 'full' or 'fullouter' but not 'full_outerjoin'.

2. Step 2: Identify correct string for full outer join

   The correct string is 'full', so 'full_outerjoin' causes error.

3. Final Answer:

   The join type string should be 'full' instead of 'full_outerjoin' -> Option A

4. Quick Check:

   Use 'full' for full outer join [OK]

Hint: Use 'full' not 'full_outerjoin' for full outer join string [OK]

Common Mistakes:

• Using 'full_outerjoin' instead of 'full'
• Thinking join condition must be string only
• Believing Spark lacks full outer join support

5. You have two DataFrames:
df1 = spark.createDataFrame([(1, 'A'), (2, 'B'), (4, 'D')], ['id', 'val1'])
df2 = spark.createDataFrame([(2, 'X'), (3, 'Y'), (4, 'Z')], ['id', 'val2'])
You want a DataFrame that shows all ids from both tables, with values from both, and null where no match exists. Which join should you use and what will be the output?

hard

A. Use left join; output: [(1, 'A', null), (2, 'B', 'X'), (4, 'D', 'Z')]

B. Use inner join; output: [(2, 'B', 'X'), (4, 'D', 'Z')]

C. Use full outer join; output: [(1, 'A', null), (2, 'B', 'X'), (3, null, 'Y'), (4, 'D', 'Z')]

D. Use right join; output: [(2, 'B', 'X'), (3, null, 'Y'), (4, 'D', 'Z')]

Solution

1. Step 1: Identify join type for all rows from both tables

   Full outer join returns all rows from both DataFrames, filling nulls where no match.

2. Step 2: Match rows and fill nulls

   Ids 1 and 3 appear only in one table, so their unmatched columns are null.

3. Final Answer:

   Use full outer join; output: [(1, 'A', null), (2, 'B', 'X'), (3, null, 'Y'), (4, 'D', 'Z')] -> Option C

4. Quick Check:

   Full outer join = all rows, nulls for no match [OK]

Hint: Full outer join keeps all rows from both tables with nulls [OK]

Common Mistakes:

• Using inner join and missing unmatched rows
• Confusing left or right join with full outer join
• Expecting no nulls in full outer join result