Inner, left, right, and full outer joins in Apache Spark - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is an inner join in Apache Spark?

An inner join returns only the rows that have matching keys in both DataFrames. It is like finding common friends between two groups.

beginner

Explain a left outer join in simple terms.

A left outer join returns all rows from the left DataFrame and the matching rows from the right DataFrame. If there is no match, the right side will have null values.

beginner

What does a right outer join do?

A right outer join returns all rows from the right DataFrame and the matching rows from the left DataFrame. If no match exists, the left side will have null values.

beginner

Describe a full outer join.

A full outer join returns all rows from both DataFrames. When there is no match, the missing side will have null values. It combines left and right joins.

intermediate

What is the difference between inner join and full outer join?

Inner join returns only matching rows from both DataFrames. Full outer join returns all rows from both DataFrames, filling nulls where there is no match.

Which join returns only rows with matching keys in both DataFrames?

A Full outer join

B Left outer join

C Right outer join

D Inner join

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

In a left outer join, what happens if a row in the left DataFrame has no match in the right DataFrame?

A The row appears with nulls for right DataFrame columns

B The row is dropped

C The row appears with nulls for left DataFrame columns

D An error occurs

Left outer join keeps all left rows, filling nulls for unmatched right rows.

Which join returns all rows from the right DataFrame and matching rows from the left?

A Inner join

B Left outer join

C Right outer join

D Full outer join

Right outer join returns all right rows and matches from left.

What does a full outer join return?

A All rows from both DataFrames, with nulls where no match

B All rows from left only

C All rows from right only

D Only matching rows

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

If you want to keep only rows present in both DataFrames, which join do you use?

A Right outer join

B Inner join

C Left outer join

D Full outer join

Inner join keeps only rows with keys in both DataFrames.

Explain the difference between inner join, left outer join, right outer join, and full outer join in Apache Spark.

Think about which side's rows are always kept and how unmatched rows are handled.

Describe a real-life example where you would use a left outer join versus a full outer join.

Consider situations where you want to keep all from one list or all from both.

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