Reading CSV files with options in Apache Spark - Mini Project: Build & Apply

Reading CSV files with options

📖 Scenario: You have a CSV file containing sales data from a store. The file has a header row, and some fields might be missing. You want to read this file into a Spark DataFrame correctly by specifying options.

🎯 Goal: Learn how to read a CSV file in Apache Spark using options like header and inferSchema to get a DataFrame with correct column names and data types.

📋 What You'll Learn

Create a Spark session

Read a CSV file with header and schema inference options

Show the DataFrame content

💡 Why This Matters

🌍 Real World

Reading CSV files with options is common when working with data exported from spreadsheets or databases. It helps ensure data is read correctly with proper column names and types.

💼 Career

Data scientists and data engineers often need to read CSV files with different formats and options to prepare data for analysis or machine learning.

1

Create Spark session and specify CSV file path

Create a Spark session called spark and create a variable csv_file_path with the value "sales_data.csv".

Apache Spark

# Create Spark session and set CSV file path
# Your code here

Hint

Use SparkSession.builder.appName(...).getOrCreate() to create the Spark session.

2

Set options for reading CSV file

Create a variable csv_options as a dictionary with keys header set to "true" and inferSchema set to "true".

Apache Spark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("CSVReader").getOrCreate()
csv_file_path = "sales_data.csv"

# Create csv_options dictionary with header and inferSchema set to "true"
# Your code here

Hint

Use a Python dictionary with keys header and inferSchema both set to string "true".

3

Read the CSV file into a DataFrame using options

Use spark.read.options(**csv_options).csv(csv_file_path) to read the CSV file into a DataFrame called df.

Apache Spark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("CSVReader").getOrCreate()
csv_file_path = "sales_data.csv"
csv_options = {"header": "true", "inferSchema": "true"}

# Read CSV file into DataFrame df using csv_options
# Your code here

Hint

Use spark.read.options(**csv_options).csv(csv_file_path) to read the CSV file.

4

Show the DataFrame content

Call df.show() to display the DataFrame content.

Apache Spark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("CSVReader").getOrCreate()
csv_file_path = "sales_data.csv"
csv_options = {"header": "true", "inferSchema": "true"}
df = spark.read.options(**csv_options).csv(csv_file_path)

# Show the DataFrame content
# Your code here

Hint

Use df.show() to display the DataFrame rows in the console.

Practice

1. What does the header option do when reading a CSV file in Apache Spark?

easy

A. It tells Spark to treat the first line as column names.

B. It specifies the character used to separate columns.

C. It infers the data types of each column automatically.

D. It sets the file reading mode to permissive.

Solution

1. Step 1: Understand the header option

   The header option tells Spark whether the first line of the CSV file contains column names or data.

2. Step 2: Identify its effect

   If header is set to true, Spark uses the first line as column names; otherwise, it treats all lines as data.

3. Final Answer:

   It tells Spark to treat the first line as column names. -> Option A

4. Quick Check:

   header = first line as columns [OK]

Hint: Remember: header=true means first line is column names [OK]

Common Mistakes:

• Confusing header with separator option
• Thinking header infers data types
• Assuming header controls error handling

2. Which of the following is the correct way to read a CSV file with a semicolon separator and header in Spark?

easy

A. spark.read.csv(options={'sep': ';', 'header': 'true'}, 'file.csv')

B. spark.read.csv(sep=';', 'file.csv', header=True)

C. spark.read.option('sep', ';').option('header', 'true').csv('file.csv')

D. spark.read.csv(header=True, sep=';', 'file.csv')

Solution

1. Step 1: Recall Spark CSV reading syntax

   Spark uses option() method to set options before calling csv().

2. Step 2: Check each option usage

   spark.read.option('sep', ';').option('header', 'true').csv('file.csv') correctly chains option('sep', ';') and option('header', 'true') before reading the file.

3. Final Answer:

   spark.read.option('sep', ';').option('header', 'true').csv('file.csv') -> Option C

4. Quick Check:

   Use option() chaining for CSV options [OK]

Hint: Use .option() chaining to set CSV read options [OK]

Common Mistakes:

• Passing options directly inside csv() as kwargs
• Using Python booleans instead of strings for options
• Incorrect method chaining order

3. Given the code below, what will be the schema of the DataFrame?

df = spark.read.option('header', 'true').option('inferSchema', 'true').csv('data.csv')

Assuming data.csv has columns: id (integer), name (string), age (integer).

medium

A. All columns will be read as strings.

B. id and age as integers, name as string.

C. All columns will be inferred as integers.

D. Schema will be empty because no schema is provided.

Solution

1. Step 1: Understand inferSchema option

   When inferSchema is true, Spark tries to detect the data type of each column automatically.

2. Step 2: Apply to given columns

   Since id and age contain integers, they will be inferred as integer types; name remains string.

3. Final Answer:

   id and age as integers, name as string. -> Option B

4. Quick Check:

   inferSchema=true means detect types [OK]

Hint: inferSchema=true detects column data types automatically [OK]

Common Mistakes:

• Assuming all columns are strings by default
• Thinking inferSchema converts all to integers
• Believing schema is empty without explicit definition

4. What is wrong with the following code snippet for reading a CSV file with a header and comma separator?

df = spark.read.csv(header=True, sep=',', 'file.csv')

medium

A. The header option must be set to 'false' to read headers.

B. The separator should be a semicolon, not a comma.

C. The file path must be an absolute path.

D. The options should be set using the option() method, not as parameters in csv().

Solution

1. Step 1: Check how options are passed in Spark CSV reading

   Spark requires options like header and sep to be set using option() method before calling csv().

2. Step 2: Identify the error in the code

   The code incorrectly passes options as parameters inside csv(), which is not supported and causes an error.

3. Final Answer:

   The options should be set using the option() method, not as parameters in csv(). -> Option D

4. Quick Check:

   Use .option() for CSV options, not csv() params [OK]

Hint: Set CSV options with .option(), not inside csv() call [OK]

Common Mistakes:

• Passing options directly inside csv()
• Using Python booleans instead of strings for options
• Confusing separator character

5. You want to read a CSV file that may have some corrupted rows. Which option should you use to skip corrupted rows and still read the rest of the data?

hard

A. mode='dropmalformed'

B. mode='failfast'

C. mode='permissive'

D. mode='ignore'

Solution

1. Step 1: Understand CSV reading modes in Spark

   Spark supports modes like 'permissive', 'failfast', and 'dropmalformed' to handle corrupted rows.

2. Step 2: Identify mode to skip corrupted rows

   The 'dropmalformed' mode skips corrupted rows and reads the rest without error.

3. Final Answer:

   mode='dropmalformed' -> Option A

4. Quick Check:

   Use dropmalformed to skip bad rows [OK]

Hint: Use mode='dropmalformed' to skip bad rows safely [OK]

Common Mistakes:

• Using failfast which stops on error
• Assuming permissive skips corrupted rows
• Using ignore which is not a valid mode