Why Inner, left, right, and full outer joins in Apache Spark? - Purpose & Use Cases

The Big Idea

What if you could instantly find who's in both your friend lists without checking each name yourself?

The Scenario

Imagine you have two lists of friends from different events, and you want to find who attended both, only one, or either event. Doing this by hand means checking each name one by one, which is tiring and confusing.

The Problem

Manually comparing lists is slow and easy to mess up. You might miss names, repeat them, or forget who belongs where. It's hard to keep track when lists get big or change often.

The Solution

Using joins in data science lets you quickly and correctly combine these lists based on common names. You can find who is in both, only in one, or in either list with simple commands, saving time and avoiding mistakes.

Before vs After

✗ Before

for friend1 in list1:
    for friend2 in list2:
        if friend1 == friend2:
            print(friend1)

✓ After

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

What It Enables

Joins let you easily mix and match data from different sources to uncover connections and insights that are hard to see otherwise.

Real Life Example

A store wants to know which customers bought products online and in-store. Using joins, they combine online and in-store purchase records to see who bought where, helping them tailor offers.

Key Takeaways

Manual matching is slow and error-prone.

Joins automate combining data based on shared keys.

Different join types show different relationships between datasets.

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