Inner, left, right, and full outer joins in Apache Spark - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of Inner Join in Spark

What is the output of the following Spark code performing an inner join?

Apache Spark

from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
data1 = [(1, "Alice"), (2, "Bob"), (3, "Charlie")]
data2 = [(2, "Sales"), (3, "HR"), (4, "IT")]
df1 = spark.createDataFrame(data1, ["id", "name"])
df2 = spark.createDataFrame(data2, ["id", "dept"])
joined_df = df1.join(df2, on="id", how="inner")
result = joined_df.collect()

A [Row(id=2, name='Bob', dept='Sales'), Row(id=3, name='Charlie', dept='HR')]

B [Row(id=1, name='Alice', dept=None), Row(id=2, name='Bob', dept='Sales'), Row(id=3, name='Charlie', dept='HR')]

C [Row(id=2, name='Bob', dept='Sales'), Row(id=3, name='Charlie', dept='HR'), Row(id=4, name=None, dept='IT')]

D [Row(id=1, name='Alice', dept=None), Row(id=2, name='Bob', dept='Sales'), Row(id=3, name='Charlie', dept='HR'), Row(id=4, name=None, dept='IT')]

💡 Hint

Inner join returns only rows with matching keys in both tables.

An inner join keeps only rows where the 'id' exists in both dataframes. Here, ids 2 and 3 are common.

❓ Predict Output

intermediate

Output of Left Outer Join in Spark

What is the output of this Spark code performing a left outer join?

Apache Spark

from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
data1 = [(1, "Alice"), (2, "Bob"), (3, "Charlie")]
data2 = [(2, "Sales"), (3, "HR"), (4, "IT")]
df1 = spark.createDataFrame(data1, ["id", "name"])
df2 = spark.createDataFrame(data2, ["id", "dept"])
joined_df = df1.join(df2, on="id", how="left")
result = joined_df.collect()

A [Row(id=4, name=None, dept='IT')]

B [Row(id=2, name='Bob', dept='Sales'), Row(id=3, name='Charlie', dept='HR')]

C [Row(id=1, name='Alice', dept=None), Row(id=2, name='Bob', dept='Sales'), Row(id=3, name='Charlie', dept='HR')]

D [Row(id=1, name='Alice', dept=None), Row(id=2, name='Bob', dept='Sales'), Row(id=3, name='Charlie', dept='HR'), Row(id=4, name=None, dept='IT')]

💡 Hint

Left join keeps all rows from the left table, adding matching data from the right.

Left outer join keeps all rows from df1. For id=1, no matching dept exists, so dept is None.

❓ Predict Output

advanced

Output of Right Outer Join in Spark

What is the output of this Spark code performing a right outer join?

Apache Spark

from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
data1 = [(1, "Alice"), (2, "Bob"), (3, "Charlie")]
data2 = [(2, "Sales"), (3, "HR"), (4, "IT")]
df1 = spark.createDataFrame(data1, ["id", "name"])
df2 = spark.createDataFrame(data2, ["id", "dept"])
joined_df = df1.join(df2, on="id", how="right")
result = joined_df.collect()

A [Row(id=2, name='Bob', dept='Sales'), Row(id=3, name='Charlie', dept='HR')]

B [Row(id=1, name='Alice', dept=None), Row(id=2, name='Bob', dept='Sales'), Row(id=3, name='Charlie', dept='HR')]

C [Row(id=1, name='Alice', dept=None), Row(id=4, name=None, dept='IT')]

D [Row(id=2, name='Bob', dept='Sales'), Row(id=3, name='Charlie', dept='HR'), Row(id=4, name=None, dept='IT')]

💡 Hint

Right join keeps all rows from the right table, adding matching data from the left.

Right outer join keeps all rows from df2. For id=4, no matching name exists, so name is None.

❓ Predict Output

advanced

Output of Full Outer Join in Spark

What is the output of this Spark code performing a full outer join?

Apache Spark

from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
data1 = [(1, "Alice"), (2, "Bob"), (3, "Charlie")]
data2 = [(2, "Sales"), (3, "HR"), (4, "IT")]
df1 = spark.createDataFrame(data1, ["id", "name"])
df2 = spark.createDataFrame(data2, ["id", "dept"])
joined_df = df1.join(df2, on="id", how="outer")
result = joined_df.collect()

A [Row(id=2, name='Bob', dept='Sales'), Row(id=3, name='Charlie', dept='HR')]

B [Row(id=1, name='Alice', dept=None), Row(id=2, name='Bob', dept='Sales'), Row(id=3, name='Charlie', dept='HR'), Row(id=4, name=None, dept='IT')]

C [Row(id=1, name='Alice', dept=None), Row(id=4, name=None, dept='IT')]

D [Row(id=1, name='Alice', dept='Sales'), Row(id=2, name='Bob', dept='HR'), Row(id=3, name='Charlie', dept='IT')]

💡 Hint

Full outer join keeps all rows from both tables, filling missing values with None.

Full outer join returns all ids from both dataframes. Missing matches have None values.

🧠 Conceptual

expert

Understanding Join Types in Spark

Which join type would you use in Spark to keep all rows from the left dataframe and only matching rows from the right dataframe, filling unmatched right columns with nulls?

A Left outer join

B Inner join

C Right outer join

D Full outer join

💡 Hint

Think about which join keeps all left rows regardless of matches.

Left outer join keeps all rows from the left dataframe and fills unmatched right columns with nulls.

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