Multi-column joins in Apache Spark - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of multi-column join with inner join type

Given two Spark DataFrames df1 and df2 joined on columns id and category using inner join, what is the output of the following code?

Apache Spark

from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
data1 = [(1, 'A', 100), (2, 'B', 200), (3, 'A', 300)]
data2 = [(1, 'A', 'X'), (2, 'B', 'Y'), (2, 'C', 'Z')]
df1 = spark.createDataFrame(data1, ['id', 'category', 'value1'])
df2 = spark.createDataFrame(data2, ['id', 'category', 'value2'])
joined_df = df1.join(df2, on=['id', 'category'], how='inner')
result = joined_df.collect()

A [Row(id=1, category='A', value1=100, value2='X'), Row(id=2, category='B', value1=200, value2='Y'), Row(id=2, category='C', value1=None, value2='Z')]

B [Row(id=1, category='A', value1=100, value2='X'), Row(id=2, category='B', value1=200, value2='Y')]

C [Row(id=1, category='A', value1=100, value2='X'), Row(id=3, category='A', value1=300, value2=None)]

D [Row(id=2, category='C', value1=None, value2='Z')]

💡 Hint

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

An inner join on columns 'id' and 'category' returns rows where both DataFrames have the same values in these columns. Rows with id=2 and category='C' are only in df2, so they are excluded.

❓ data_output

intermediate

Number of rows after left join on multiple columns

If you perform a left join of df1 with df2 on columns id and category, how many rows will the resulting DataFrame have?

Apache Spark

from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
data1 = [(1, 'A', 100), (2, 'B', 200), (3, 'A', 300)]
data2 = [(1, 'A', 'X'), (2, 'B', 'Y'), (2, 'C', 'Z')]
df1 = spark.createDataFrame(data1, ['id', 'category', 'value1'])
df2 = spark.createDataFrame(data2, ['id', 'category', 'value2'])
joined_df = df1.join(df2, on=['id', 'category'], how='left')
count = joined_df.count()

A 2

B 4

C 3

D 5

💡 Hint

Left join keeps all rows from the left DataFrame.

Left join keeps all rows from df1 (3 rows). It adds matching rows from df2 or nulls if no match. So total rows remain 3.

🔧 Debug

advanced

Identify the error in multi-column join code

What error will this Spark code raise when trying to join two DataFrames on multiple columns?

Apache Spark

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

A AnalysisException: cannot resolve 'id,category' given input columns

B SyntaxError: invalid syntax

C No error, join works correctly

D TypeError: join() argument 'on' must be a string or a list of strings

💡 Hint

The 'on' parameter expects a list of column names for multiple columns.

Passing a single string with comma-separated column names is not valid. Spark treats it as one column name, which does not exist, causing AnalysisException.

🚀 Application

advanced

Choosing join type for multi-column join scenario

You have two DataFrames: sales with columns store_id, date, sales_amount and targets with columns store_id, date, target_amount. You want to keep all sales records and add target amounts where available. Which join type should you use on columns store_id and date?

A Full outer join

B Inner join

C Right join

D Left join

💡 Hint

You want to keep all sales records even if no target exists.

Left join keeps all rows from the left DataFrame (sales) and adds matching target data or null if no match.

🧠 Conceptual

expert

Effect of join keys order in multi-column join

In Spark DataFrame join, does the order of columns in the on list (e.g., ['id', 'category'] vs ['category', 'id']) affect the join result?

A No, the order of join keys does not affect the join result

B No, but it affects the performance of the join

C Yes, the order changes the join keys and result rows

D Yes, but only for outer joins, not inner joins

💡 Hint

Join keys are matched as a set, order does not matter for equality.

Spark matches rows where all join keys are equal regardless of order in the list. The join result is the same.

Practice

1.

What does a multi-column join in Apache Spark do?

Choose the best description.

easy

A. It sorts a DataFrame by multiple columns.

B. It merges two DataFrames by adding columns without any condition.

C. It filters rows from a single DataFrame based on multiple columns.

D. It joins two DataFrames using more than one column to match rows.

Solution

1. Step 1: Understand join basics

   A join combines rows from two tables based on matching column values.

2. Step 2: Recognize multi-column join

   Multi-column join uses more than one column to find matching rows, improving accuracy.

3. Final Answer:

   It joins two DataFrames using more than one column to match rows. -> Option D

4. Quick Check:

   Multi-column join = match on multiple columns [OK]

Hint: Multi-column join matches rows on several columns at once [OK]

Common Mistakes:

• Confusing join with filtering
• Thinking join adds columns without condition
• Mixing join with sorting

2.

Which of the following is the correct syntax to perform a multi-column inner join between df1 and df2 on columns "id" and "date"?

df1.join(df2, _____, how="inner")

easy

A. ["id", "date"]

B. "id, date"

C. df1["id"] == df2["id"] and df1["date"] == df2["date"]

D. on=("id", "date")

Solution

1. Step 1: Check join parameter for columns

   The on parameter accepts a list of column names for multi-column join.

2. Step 2: Identify correct list syntax

   Using ["id", "date"] correctly passes both columns as a list.

3. Final Answer:

   ["id", "date"] -> Option A

4. Quick Check:

   List of columns = ["id", "date"] [OK]

Hint: Use a list of column names for multi-column join [OK]

Common Mistakes:

• Passing columns as a single string
• Using boolean expressions instead of list
• Incorrect tuple syntax for columns

3.

Given the following two DataFrames:

df1 = spark.createDataFrame([
  (1, "2023-01-01", "A"),
  (2, "2023-01-02", "B"),
  (3, "2023-01-03", "C")
], ["id", "date", "value"])

df2 = spark.createDataFrame([
  (1, "2023-01-01", "X"),
  (2, "2023-01-04", "Y"),
  (3, "2023-01-03", "Z")
], ["id", "date", "value2"])

result = df1.join(df2, ["id", "date"], how="inner").select("id", "date", "value", "value2")
result.show()

What will be the output?

medium

A. +---+----------+-----+-------+
|id |date |value|value2 |
+---+----------+-----+-------+
|1 |2023-01-01|A |X |
|2 |2023-01-04|B |Y |
|3 |2023-01-03|C |Z |
+---+----------+-----+-------+

B. +---+----------+-----+-------+
|id |date |value|value2 |
+---+----------+-----+-------+
|2 |2023-01-02|B |Y |
+---+----------+-----+-------+

C. +---+----------+-----+-------+
|id |date |value|value2 |
+---+----------+-----+-------+
|1 |2023-01-01|A |X |
|3 |2023-01-03|C |Z |
+---+----------+-----+-------+

D. +---+----------+-----+-------+
|id |date |value|value2 |
+---+----------+-----+-------+
|1 |2023-01-01|A |null |
|2 |2023-01-02|B |null |
|3 |2023-01-03|C |Z |
+---+----------+-----+-------+

Solution

1. Step 1: Understand inner join on multiple columns

   Inner join keeps rows where both id and date match in both DataFrames.

2. Step 2: Identify matching rows

   Rows with (1, "2023-01-01") and (3, "2023-01-03") match in both DataFrames; (2, "2023-01-02") and (2, "2023-01-04") do not.

3. Final Answer:

   Rows with id 1 and 3 with matching dates and values -> Option C

4. Quick Check:

   Inner join = only matching rows on all columns [OK]

Hint: Inner join keeps only rows matching all join columns [OK]

Common Mistakes:

• Including rows with partial matches
• Confusing inner join with outer join
• Ignoring the multi-column condition

4.

Consider this code snippet:

result = df1.join(df2, on=["id", "date"], how="inner")

When running this, you get an error: TypeError: join() got an unexpected keyword argument 'on'. What is the likely cause?

medium

A. The how parameter must be a list, not a string.

B. The on parameter should be passed as the second positional argument, not as a keyword.

C. The join method does not support multi-column joins.

D. The DataFrames must have the same schema before joining.

Solution

1. Step 1: Check join method signature

   In Spark, join() expects the join columns as the second positional argument, not as a keyword on.

2. Step 2: Correct usage

   Use df1.join(df2, ["id", "date"], how="inner") without on=.

3. Final Answer:

   The on parameter should be passed as the second positional argument, not as a keyword. -> Option B

4. Quick Check:

   Join columns = positional argument [OK]

Hint: Pass join columns as positional argument, not 'on=' keyword [OK]

Common Mistakes:

• Using 'on=' keyword in join
• Passing 'how' as a list
• Assuming join needs identical schemas

5.

You have two DataFrames, orders and customers. You want to join them on customer_id and region, but some customers have missing region values in one DataFrame. You want to keep all orders and add customer info where possible. Which join type and approach should you use?

hard

A. Use a left join on customer_id only, then filter or fill missing region values separately.

B. Use an inner join on both columns; only rows with matching customer_id and region appear.

C. Use a left join on both columns; missing region values will exclude some matches.

D. Use a full outer join on both columns to keep all rows from both DataFrames.

Solution

1. Step 1: Understand join requirements

   You want all orders (left DataFrame) and customer info where available, but region may be missing in one DataFrame.

2. Step 2: Choose join columns carefully

   Joining on both columns will exclude rows with missing region. Joining only on customer_id keeps all orders.

3. Step 3: Handle missing region values separately

   After join, fill or filter missing region values as needed.

4. Final Answer:

   Use a left join on customer_id only, then handle missing region values separately. -> Option A

5. Quick Check:

   Left join on key column + handle missing data [OK]

Hint: Join on reliable keys first, handle missing columns after [OK]

Common Mistakes:

• Joining on columns with missing values directly
• Using inner join and losing rows
• Using full outer join when not needed