Creating DataFrames from files (CSV, JSON, Parquet) in Apache Spark - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to read a CSV file into a DataFrame.

Apache Spark

df = spark.read.format([1]).load("data/employees.csv")

Drag options to blanks, or click blank then click option'

A"csv"

B"text"

C"parquet"

D"json"

Attempts:

3 left

💡 Hint

Common Mistakes

Using "json" or "parquet" format for CSV files.

Forgetting to specify the format before loading.

The format method specifies the file type. For CSV files, use "csv".

2fill in blank

medium

Complete the code to read a JSON file into a DataFrame.

Apache Spark

df = spark.read.[1]("data/employees.json")

Drag options to blanks, or click blank then click option'

Acsv

Bparquet

Cjson

Dtext

Attempts:

3 left

💡 Hint

Common Mistakes

Using read.csv() or read.parquet() for JSON files.

Using read.text() which reads raw text lines.

The read.json() method reads JSON files directly into a DataFrame.

3fill in blank

hard

Fix the error in the code to read a Parquet file into a DataFrame.

Apache Spark

df = spark.read.format("parquet").[1]("data/employees.parquet")

Drag options to blanks, or click blank then click option'

Acsv

Bread

Cjson

Dload

Attempts:

3 left

💡 Hint

Common Mistakes

Using read() instead of load() after format().

Using json() or csv() methods for Parquet files.

The load() method loads the file after specifying the format.

4fill in blank

hard

Fill both blanks to read a CSV file with header and infer schema options.

Apache Spark

df = spark.read.format("csv").option([1], "true").option([2], "true").load("data/employees.csv")

Drag options to blanks, or click blank then click option'

A"header"

B"inferSchema"

C"delimiter"

D"path"

Attempts:

3 left

💡 Hint

Common Mistakes

Using delimiter or path options incorrectly.

Not setting header or inferSchema to "true".

To read CSV with headers and infer data types, use options header and inferSchema set to "true".

5fill in blank

hard

Fill both blanks to create a DataFrame from JSON with multiLine option enabled.

Apache Spark

df = spark.read.format([1]).option([2], "true").load("data/employees_multiline.json")

Drag options to blanks, or click blank then click option'

A"json"

B"multiLine"

C"csv"

D"parquet"

Attempts:

3 left

💡 Hint

Common Mistakes

Using csv or parquet format for JSON files.

Not setting the multiLine option when needed.

To read multiline JSON files, specify format as "json" and set option multiLine to "true".

Practice

1. What is the main purpose of creating a DataFrame from a file in Apache Spark?

easy

A. To write data back to a file

B. To organize data in rows and columns for easy analysis

C. To delete data from the file system

D. To convert data into plain text format

Solution

1. Step 1: Understand DataFrame concept

   A DataFrame is a table-like structure with rows and columns used for data analysis.

2. Step 2: Purpose of loading files

   Loading files into DataFrames allows Spark to process and analyze data efficiently.

3. Final Answer:

   To organize data in rows and columns for easy analysis -> Option B

4. Quick Check:

   DataFrames = organized data [OK]

Hint: DataFrames = tables for easy data work [OK]

Common Mistakes:

• Confusing DataFrame creation with file writing
• Thinking DataFrames delete files
• Assuming DataFrames convert data to plain text

2. Which of the following is the correct syntax to load a CSV file into a Spark DataFrame?

easy

A. spark.read.format('csv').load('file.csv')

B. spark.read.load('file.csv', format='csv')

C. spark.load('csv', 'file.csv')

D. spark.read.csv('file.csv')

Solution

1. Step 1: Recall Spark CSV loading syntax

   The standard way is to use spark.read.format('csv').load('filename').

2. Step 2: Check each option

   spark.read.format('csv').load('file.csv') matches the correct syntax; others are incorrect or invalid methods.

3. Final Answer:

   spark.read.format('csv').load('file.csv') -> Option A

4. Quick Check:

   Use format('csv').load() for CSV [OK]

Hint: Use spark.read.format('csv').load() for CSV files [OK]

Common Mistakes:

• Using spark.read.csv() without options
• Trying spark.load() which doesn't exist
• Passing format as a keyword argument in load()

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

df = spark.read.format('json').load('people.json')
df.printSchema()

Assuming 'people.json' contains records like {"name": "Alice", "age": 30}.

medium

A. root
|-- name: string (nullable = true)
|-- age: long (nullable = true)

B. root
|-- name: integer (nullable = true)
|-- age: string (nullable = true)

C. root
|-- name: string (nullable = false)
|-- age: integer (nullable = false)

D. root
|-- name: string (nullable = true)
|-- age: string (nullable = true)

Solution

1. Step 1: Understand JSON schema inference

   Spark infers types from JSON: "name" is string, "age" is number (long).

2. Step 2: Check nullability and types

   By default, fields are nullable. Age inferred as long (integer type in Spark).

3. Final Answer:

   root |-- name: string (nullable = true) |-- age: long (nullable = true) -> Option A

4. Quick Check:

   JSON fields inferred correctly [OK]

Hint: JSON fields infer types automatically [OK]

Common Mistakes:

• Assuming age is string instead of long
• Thinking fields are non-nullable by default
• Confusing integer with string types

4. What is wrong with this code snippet to load a Parquet file?

df = spark.read.format('parquet').load('data.parquet')
df.show()

Assuming 'data.parquet' exists and is valid.

medium

A. You must specify schema before loading Parquet files

B. The format should be 'parq' instead of 'parquet'

C. The load method requires a file extension in the path

D. Nothing is wrong; this code will load and show the DataFrame correctly

Solution

1. Step 1: Check Parquet loading syntax

   Using spark.read.format('parquet').load('file') is correct and standard.

2. Step 2: Validate assumptions

   If the file exists and is valid, df.show() will display the data without error.

3. Final Answer:

   Nothing is wrong; this code will load and show the DataFrame correctly -> Option D

4. Quick Check:

   Correct Parquet load syntax [OK]

Hint: Parquet loads with format('parquet').load() [OK]

Common Mistakes:

• Using wrong format string like 'parq'
• Thinking schema is mandatory for Parquet
• Believing file extension is required in load()

5. You want to load a CSV file with a header and infer the schema automatically. Which code snippet correctly achieves this?

hard

A. spark.read.format('csv').load('data.csv').option('header', 'true').option('inferSchema', 'true')

B. spark.read.csv('data.csv', header=True, inferSchema=True)

C. spark.read.format('csv').option('header', 'true').option('inferSchema', 'true').load('data.csv')

D. spark.read.load('data.csv').option('header', 'true').option('inferSchema', 'true')

Solution

1. Step 1: Understand CSV options usage

   Options like header and inferSchema must be set before load() when using format().

2. Step 2: Analyze each option

   spark.read.format('csv').option('header', 'true').option('inferSchema', 'true').load('data.csv') sets options correctly before load(). spark.read.csv('data.csv', header=True, inferSchema=True) uses invalid syntax for Spark. spark.read.format('csv').load('data.csv').option('header', 'true').option('inferSchema', 'true') sets options after load(), which is wrong. spark.read.load('data.csv').option('header', 'true').option('inferSchema', 'true') misses format('csv').

3. Final Answer:

   spark.read.format('csv').option('header', 'true').option('inferSchema', 'true').load('data.csv') -> Option C

4. Quick Check:

   Set options before load() [OK]

Hint: Set CSV options before load() with format('csv') [OK]

Common Mistakes:

• Setting options after load() call
• Using Python-style keyword arguments in Spark
• Not specifying format('csv') before load()