GroupBy and aggregations in Apache Spark - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the groupBy() function do in Apache Spark?

It groups rows in a DataFrame based on one or more columns, so you can perform aggregations on each group separately.

beginner

Name three common aggregation functions used after groupBy() in Spark.

Common aggregation functions are count(), sum(), and avg() (average).

beginner

How do you calculate the average of a column named sales after grouping by region?

Use df.groupBy('region').avg('sales') to get the average sales per region.

intermediate

What is the difference between agg() and direct aggregation functions like sum() after groupBy()?

agg() lets you apply multiple aggregation functions at once, while direct functions like sum() apply a single aggregation.

beginner

Why is grouping and aggregation useful in real life?

It helps summarize large data sets, like finding total sales per store or average temperature per city, making data easier to understand.

What does df.groupBy('category').count() do?

A Counts the number of columns in the DataFrame

B Counts the total number of categories

C Counts the number of unique values in the DataFrame

D Counts the number of rows in each category group

The count() after groupBy() counts rows per group.

Which function calculates the sum of a column after grouping?

A sum()

B avg()

C count()

D max()

sum() adds up values in each group.

How do you apply multiple aggregations like sum and average together?

A Use multiple groupBy() calls

B Use agg() with a dictionary of functions

C Use count() twice

D Use select() only

The agg() function allows multiple aggregations at once.

What type of object does groupBy() return?

A GroupedData

B DataFrame

C RDD

D List

groupBy() returns a GroupedData object to apply aggregations.

Which of these is NOT an aggregation function in Spark?

A max()

B min()

C filter()

D avg()

filter() is for filtering rows, not aggregation.

Explain how you would use groupBy() and aggregation functions to find the total sales per product category.

Think about grouping by category and then summing sales.

Describe the difference between using agg() and direct aggregation functions after groupBy().

Consider when you want one vs many summary stats.

Practice

1. What does the groupBy function do in Apache Spark?

easy

A. It splits the data into groups based on column values.

B. It sorts the data in ascending order.

C. It filters rows based on a condition.

D. It joins two DataFrames together.

Solution

1. Step 1: Understand the purpose of groupBy

   The groupBy function is used to split data into groups based on one or more column values.

2. Step 2: Compare with other functions

   Sorting, filtering, and joining are different operations and do not split data into groups.

3. Final Answer:

   It splits the data into groups based on column values. -> Option A

4. Quick Check:

   groupBy = splits data into groups [OK]

Hint: Remember: groupBy groups rows by column values [OK]

Common Mistakes:

• Confusing groupBy with filter or sort
• Thinking groupBy changes row order
• Assuming groupBy merges DataFrames

2. Which of the following is the correct syntax to calculate the average of column sales after grouping by region in Spark DataFrame df?

easy

A. df.groupBy("region").sum("sales")

B. df.groupBy("region").agg(avg("sales"))

C. df.groupBy("region").filter(avg("sales"))

D. df.groupBy("region").select(avg("sales"))

Solution

1. Step 1: Identify correct aggregation syntax

   To calculate average after grouping, use groupBy followed by agg(avg(column)).

2. Step 2: Check each option

   df.groupBy("region").agg(avg("sales")) uses agg(avg("sales")) correctly. df.groupBy("region").sum("sales") uses sum, not average. df.groupBy("region").filter(avg("sales")) uses filter incorrectly. df.groupBy("region").select(avg("sales")) uses select which is not valid after groupBy for aggregation.

3. Final Answer:

   df.groupBy("region").agg(avg("sales")) -> Option B

4. Quick Check:

   groupBy + agg(avg) = df.groupBy("region").agg(avg("sales")) [OK]

Hint: Use agg() with avg() inside groupBy for averages [OK]

Common Mistakes:

• Using sum instead of avg for average
• Using filter instead of agg
• Using select after groupBy without agg

3. Given the following Spark code, what will be the output schema and result?

from pyspark.sql import SparkSession
from pyspark.sql.functions import sum

spark = SparkSession.builder.getOrCreate()
data = [("East", 100), ("West", 200), ("East", 150)]
df = spark.createDataFrame(data, ["region", "sales"])
result = df.groupBy("region").agg(sum("sales").alias("total_sales"))
result.show()

medium

A. region | total_sales
East | 250
West | 200

B. region | sales
East | 100
West | 200
East | 150

C. region | total_sales
East | 150
West | 200

D. region | total_sales
East | 100
West | 200

Solution

1. Step 1: Understand groupBy and sum aggregation

   The code groups rows by "region" and sums the "sales" values for each group.

2. Step 2: Calculate sums for each region

   For "East": 100 + 150 = 250; For "West": 200.

3. Final Answer:

   region | total_sales East | 250 West | 200 -> Option A

4. Quick Check:

   Sum sales by region = 250 (East), 200 (West) [OK]

Hint: Sum sales per group to get total_sales [OK]

Common Mistakes:

• Not summing all values per group
• Confusing alias name with original column
• Expecting original rows without aggregation

4. Identify the error in the following Spark code snippet:

df.groupBy("category").agg(sum("amount"), avg("amount"))

medium

A. Missing alias for aggregated columns causes error.

B. agg() requires a dictionary, not multiple arguments.

C. groupBy cannot be used with agg.

D. sum and avg functions are not imported.

Solution

1. Step 1: Check function imports

   The code uses sum and avg functions but does not show imports from pyspark.sql.functions.

2. Step 2: Validate other options

   agg() accepts multiple arguments; aliasing is optional; groupBy works with agg.

3. Final Answer:

   sum and avg functions are not imported. -> Option D

4. Quick Check:

   Missing imports cause NameError [OK]

Hint: Always import aggregation functions before use [OK]

Common Mistakes:

• Assuming agg needs dict, but it accepts multiple args
• Forgetting to import sum, avg from pyspark.sql.functions
• Thinking groupBy can't be chained with agg

5. You have a Spark DataFrame sales_df with columns store, product, and revenue. You want to find the maximum revenue per store and the average revenue per product across all stores in a single query. Which code snippet correctly achieves this?

hard

A. sales_df.groupBy("store").agg(max("revenue").alias("max_revenue"), avg("revenue").alias("avg_revenue"))

B. sales_df.agg(max("revenue").alias("max_revenue"), avg("revenue").alias("avg_revenue"))

C. sales_df.groupBy("store").agg(max("revenue").alias("max_revenue")).crossJoin(sales_df.groupBy("product").agg(avg("revenue").alias("avg_revenue")))

D. sales_df.groupBy("store").agg(max("revenue").alias("max_revenue")).groupBy("product").agg(avg("revenue").alias("avg_revenue"))

Solution

1. Step 1: Understand the requirements

   We need max revenue per store and average revenue per product in one result.

2. Step 2: Analyze options

   sales_df.groupBy("store").agg(max("revenue").alias("max_revenue"), avg("revenue").alias("avg_revenue")) aggregates both by store only, so avg revenue per product is incorrect. sales_df.agg(max("revenue").alias("max_revenue"), avg("revenue").alias("avg_revenue")) aggregates globally, losing grouping. sales_df.groupBy("store").agg(max("revenue").alias("max_revenue")).groupBy("product").agg(avg("revenue").alias("avg_revenue")) tries chaining groupBy but overwrites previous aggregation. sales_df.groupBy("store").agg(max("revenue").alias("max_revenue")).crossJoin(sales_df.groupBy("product").agg(avg("revenue").alias("avg_revenue"))) computes both groupings separately and joins results, correctly combining both summaries.

3. Final Answer:

   sales_df.groupBy("store").agg(max("revenue").alias("max_revenue")).crossJoin(sales_df.groupBy("product").agg(avg("revenue").alias("avg_revenue"))) -> Option C

4. Quick Check:

   Separate groupBy + crossJoin combines both aggregations [OK]

Hint: Use separate groupBy then join to combine different aggregations [OK]

Common Mistakes:

• Trying to aggregate different groupings in one groupBy
• Overwriting aggregation by chaining groupBy
• Forgetting to join separate aggregation results