SQL queries on DataFrames in Apache Spark - Mini Project: Build & Apply

SQL Queries on DataFrames with Apache Spark

📖 Scenario: You work at a small bookstore. You have a list of books with their titles, authors, and prices. You want to use SQL queries on this data to find useful information.

🎯 Goal: Learn how to create a Spark DataFrame, register it as a SQL temporary view, run SQL queries on it, and display the results.

📋 What You'll Learn

Create a Spark DataFrame from a list of book data

Register the DataFrame as a temporary SQL view

Write and run SQL queries on the view

Display the query results

💡 Why This Matters

🌍 Real World

Bookstores and many businesses use SQL queries on data tables to find useful information quickly.

💼 Career

Data analysts and data scientists often use Spark SQL to analyze big data efficiently.

1

Create the initial DataFrame

Create a list called books_data with these exact tuples: ("The Alchemist", "Paulo Coelho", 10.99), ("1984", "George Orwell", 8.99), ("To Kill a Mockingbird", "Harper Lee", 7.99). Then create a Spark DataFrame called books_df from books_data with columns "title", "author", and "price".

Apache Spark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("Bookstore").getOrCreate()
# Create the list books_data and the DataFrame books_df below
# Your code here

Hint

Use spark.createDataFrame() with your list and column names.

2

Register the DataFrame as a SQL temporary view

Use the DataFrame books_df and register it as a temporary SQL view called books_view using the method createOrReplaceTempView.

Apache Spark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("Bookstore").getOrCreate()

books_data = [("The Alchemist", "Paulo Coelho", 10.99), ("1984", "George Orwell", 8.99), ("To Kill a Mockingbird", "Harper Lee", 7.99)]
books_df = spark.createDataFrame(books_data, ["title", "author", "price"])
# Register books_df as a temporary view named books_view
# Your code here

Hint

Use books_df.createOrReplaceTempView("books_view") to register the view.

3

Write and run a SQL query to select books priced above 8

Write a SQL query string called query that selects all columns from books_view where the price is greater than 8. Then run this query using spark.sql(query) and save the result in a DataFrame called expensive_books_df.

Apache Spark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("Bookstore").getOrCreate()

books_data = [("The Alchemist", "Paulo Coelho", 10.99), ("1984", "George Orwell", 8.99), ("To Kill a Mockingbird", "Harper Lee", 7.99)]
books_df = spark.createDataFrame(books_data, ["title", "author", "price"])

books_df.createOrReplaceTempView("books_view")
# Write the SQL query string query to select books with price > 8
# Run the query with spark.sql(query) and save to expensive_books_df
# Your code here

Hint

Write the SQL query as a string and run it with spark.sql().

4

Display the result of the SQL query

Use the DataFrame expensive_books_df and call the show() method to display the books with price greater than 8.

Apache Spark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("Bookstore").getOrCreate()

books_data = [("The Alchemist", "Paulo Coelho", 10.99), ("1984", "George Orwell", 8.99), ("To Kill a Mockingbird", "Harper Lee", 7.99)]
books_df = spark.createDataFrame(books_data, ["title", "author", "price"])

books_df.createOrReplaceTempView("books_view")

query = "SELECT * FROM books_view WHERE price > 8"
expensive_books_df = spark.sql(query)
# Display the results using show()
# Your code here

Hint

Call expensive_books_df.show() to print the results.

Practice

1. What does the createOrReplaceTempView method do in Apache Spark?

easy

A. It registers a DataFrame as a temporary SQL table for querying.

B. It permanently saves the DataFrame to disk as a table.

C. It converts a SQL query into a DataFrame.

D. It deletes a temporary view from Spark.

Solution

1. Step 1: Understand the purpose of createOrReplaceTempView

   This method registers a DataFrame as a temporary table in Spark's SQL context.

2. Step 2: Recognize its use in SQL queries

   Once registered, you can run SQL queries on the DataFrame using spark.sql().

3. Final Answer:

   It registers a DataFrame as a temporary SQL table for querying. -> Option A

4. Quick Check:

   Temporary view = SQL table for queries [OK]

Hint: Temp view means temporary SQL table for queries [OK]

Common Mistakes:

• Thinking it saves data permanently
• Confusing it with running SQL queries
• Assuming it deletes tables

2. Which of the following is the correct syntax to run a SQL query on a DataFrame registered as a temp view named people?

easy

A. spark.execute("SELECT * FROM people")

B. spark.query("SELECT * FROM people")

C. spark.sql("SELECT * FROM people")

D. spark.runSQL("SELECT * FROM people")

Solution

1. Step 1: Identify the correct method to run SQL queries

   The method to run SQL queries on Spark is spark.sql().

2. Step 2: Check the syntax for querying the temp view

   The query string should be passed inside spark.sql() as shown in spark.sql("SELECT * FROM people").

3. Final Answer:

   spark.sql("SELECT * FROM people") -> Option C

4. Quick Check:

   Use spark.sql() to run SQL queries [OK]

Hint: Use spark.sql() to run SQL queries on temp views [OK]

Common Mistakes:

• Using incorrect method names like spark.query
• Forgetting to use quotes around SQL query
• Confusing method names with other APIs

3. Given the following code, what will be the output of the SQL query?

data = [(1, "Alice", 29), (2, "Bob", 31), (3, "Cathy", 25)]
columns = ["id", "name", "age"]
df = spark.createDataFrame(data, columns)
df.createOrReplaceTempView("people")
result = spark.sql("SELECT name FROM people WHERE age > 28 ORDER BY age DESC")
result.show()

medium

A. [["Bob"], ["Cathy"]]

B. [["Alice"], ["Bob"]]

C. [["Cathy"]]

D. [["Bob"], ["Alice"]]

Solution

1. Step 1: Analyze the data and filter condition

   Rows with age > 28 are Alice (29) and Bob (31).

2. Step 2: Apply ORDER BY age DESC

   Ordering by age descending gives Bob (31) first, then Alice (29).

3. Final Answer:

   [["Bob"], ["Alice"]] -> Option D

4. Quick Check:

   Filter age > 28 and order desc = Bob, Alice [OK]

Hint: Filter then order by age descending to get correct order [OK]

Common Mistakes:

• Mixing ascending and descending order
• Including rows with age <= 28
• Confusing column names in SELECT

4. Identify the error in the following code snippet:

df.createOrReplaceTempView("employees")
result = spark.sql("SELECT * FROM employee WHERE salary > 50000")
result.show()

medium

A. The DataFrame was not created before creating temp view.

B. The temp view name is 'employees' but query uses 'employee'.

C. The SQL query syntax is incorrect.

D. The show() method cannot be used on SQL query results.

Solution

1. Step 1: Check temp view name and SQL query table name

   The temp view is named "employees" but the query uses "employee" (missing 's').

2. Step 2: Understand impact of name mismatch

   This causes a runtime error because the table "employee" does not exist.

3. Final Answer:

   The temp view name is 'employees' but query uses 'employee'. -> Option B

4. Quick Check:

   Temp view and query table names must match exactly [OK]

Hint: Check temp view and query table names match exactly [OK]

Common Mistakes:

• Assuming SQL syntax error instead of name mismatch
• Thinking show() can't be used on results
• Ignoring case sensitivity in table names

5. You have a DataFrame sales with columns product, region, and amount. You want to find the total sales amount per region but only for regions where total sales exceed 1000. Which SQL query on the temp view sales will achieve this?

hard

A. SELECT region, SUM(amount) AS total_sales FROM sales GROUP BY region HAVING total_sales > 1000

B. SELECT region, SUM(amount) AS total_sales FROM sales WHERE total_sales > 1000 GROUP BY region

C. SELECT region, SUM(amount) FROM sales GROUP BY region WHERE SUM(amount) > 1000

D. SELECT region, SUM(amount) AS total_sales FROM sales GROUP BY region WHERE total_sales > 1000

Solution

1. Step 1: Understand filtering on aggregated data

   Filtering on aggregated results requires the HAVING clause, not WHERE.

2. Step 2: Check correct use of HAVING with alias

   Using HAVING total_sales > 1000 after GROUP BY is correct as in SELECT region, SUM(amount) AS total_sales FROM sales GROUP BY region HAVING total_sales > 1000.

3. Final Answer:

   SELECT region, SUM(amount) AS total_sales FROM sales GROUP BY region HAVING total_sales > 1000 -> Option A

4. Quick Check:

   Use HAVING to filter grouped aggregates [OK]

Hint: Use HAVING, not WHERE, to filter grouped results [OK]

Common Mistakes:

• Using WHERE instead of HAVING for aggregates
• Placing WHERE after GROUP BY
• Not aliasing SUM(amount) before HAVING