Inner, left, right, and full outer joins in Apache Spark

Introduction

Joins help combine two tables based on matching data. This lets you see related information together.

You have customer info in one table and orders in another, and want to see orders with customer details.

You want to find all employees and their departments, even if some employees have no department yet.

You want to find all products and any sales they have, including products with no sales.

You want to combine two lists of events, showing all events from both lists, matched where possible.

Syntax

Apache Spark

df1.join(df2, on='key_column', how='join_type')

df1 and df2 are the two tables (DataFrames) to join.

on is the column name(s) to match rows.

how is the join type: 'inner', 'left', 'right', or 'full'.

Examples

Returns rows where id matches in both tables.

Apache Spark

df1.join(df2, on='id', how='inner')

Returns all rows from df1, with matching rows from df2 or null if no match.

Apache Spark

df1.join(df2, on='id', how='left')

Returns all rows from df2, with matching rows from df1 or null if no match.

Apache Spark

df1.join(df2, on='id', how='right')

Returns all rows from both tables, matching where possible, null where no match.

Apache Spark

df1.join(df2, on='id', how='full')

Sample Program

This code creates two small tables: one with people and one with departments. It shows how to join them in four ways and prints the results.

Apache Spark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName('JoinExample').getOrCreate()

# Create first DataFrame
data1 = [(1, 'Alice'), (2, 'Bob'), (3, 'Charlie')]
df1 = spark.createDataFrame(data1, ['id', 'name'])

# Create second DataFrame
data2 = [(2, 'Sales'), (3, 'HR'), (4, 'IT')]
df2 = spark.createDataFrame(data2, ['id', 'department'])

# Inner join
inner_join = df1.join(df2, on='id', how='inner')
print('Inner Join Result:')
inner_join.show()

# Left join
left_join = df1.join(df2, on='id', how='left')
print('Left Join Result:')
left_join.show()

# Right join
right_join = df1.join(df2, on='id', how='right')
print('Right Join Result:')
right_join.show()

# Full outer join
full_join = df1.join(df2, on='id', how='fullouter')
print('Full Outer Join Result:')
full_join.show()

spark.stop()

Important Notes

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

Left join keeps all rows from the first table, adding nulls if no match in second.

Right join keeps all rows from the second table, adding nulls if no match in first.

Full outer join keeps all rows from both tables, filling nulls where no match.

Summary

Joins combine tables based on matching columns.

Inner join shows only matching rows.

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

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