Reading JSON and nested data in Apache Spark - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

Apache Spark

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

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

Ajson

Bparquet

Ctext

Dcsv

Attempts:

3 left

💡 Hint

Common Mistakes

Using csv() or text() to read JSON files causes errors or wrong data.

Forgetting to specify the file path as a string.

The json method reads JSON files into a DataFrame.

2fill in blank

medium

Complete the code to select the nested field 'address.city' from the DataFrame.

Apache Spark

df.select("[1]")

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

Aaddress

Bcity

Caddress.city

Daddress[city]

Attempts:

3 left

💡 Hint

Common Mistakes

Selecting only 'address' returns the whole nested struct, not the city.

Using brackets like address[city] is not valid syntax here.

Use dot notation to select nested fields in Spark DataFrames.

3fill in blank

hard

Fix the error in the code to explode the nested array field 'phones'.

Apache Spark

from pyspark.sql.functions import [1]
df.select(explode(df.phones)).show()

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Aexplode

Bflatten

Ccollect_list

Darray

Attempts:

3 left

💡 Hint

Common Mistakes

Using flatten or collect_list does not explode arrays into rows.

Not importing the function causes a NameError.

The explode function expands an array column into multiple rows.

4fill in blank

hard

Fill both blanks to create a dictionary of word lengths for words longer than 3 characters.

Apache Spark

lengths = {word: [1] for word in words if [2]

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Alen(word)

Blen(word) > 3

Cword.startswith('a')

Dword.isalpha()

Attempts:

3 left

💡 Hint

Common Mistakes

Using conditions unrelated to length causes wrong filtering.

Not using len(word) for the dictionary values.

The dictionary comprehension maps each word to its length if the word length is greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase keys and values greater than 0.

Apache Spark

result = [1]: [2] for k, v in data.items() if v [3] 0}

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Ak.upper()

Bv

C>

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using k.lower() instead of uppercase keys.

Using wrong comparison operators like < or ==.

The dictionary comprehension uses uppercase keys, keeps values, and filters values greater than zero.

Practice

1. What does Apache Spark do when reading a JSON file with nested data?

easy

A. It automatically parses nested fields into a structured DataFrame.

B. It ignores nested fields and reads only top-level data.

C. It converts JSON into plain text without structure.

D. It requires manual flattening before reading nested data.

Solution

1. Step 1: Understand Spark JSON reading

   Spark reads JSON files and automatically detects nested structures.

2. Step 2: Effect on DataFrame

   Nested JSON fields become nested columns accessible via dot notation.

3. Final Answer:

   It automatically parses nested fields into a structured DataFrame. -> Option A

4. Quick Check:

   Spark JSON reading = automatic nested parsing [OK]

Hint: Spark reads nested JSON as nested columns automatically [OK]

Common Mistakes:

• Thinking Spark ignores nested fields
• Assuming JSON is read as plain text
• Believing manual flattening is always needed

2. Which of the following is the correct syntax to read a JSON file named data.json into a Spark DataFrame?

easy

A. spark.read.text("data.json")

B. spark.read.csv("data.json")

C. spark.load.json("data.json")

D. spark.read.json("data.json")

Solution

1. Step 1: Identify correct Spark method for JSON

   The method to read JSON files is spark.read.json().

2. Step 2: Check other options

   csv reads CSV files, load is generic but needs format specified, text reads plain text lines.

3. Final Answer:

   spark.read.json("data.json") -> Option D

4. Quick Check:

   Read JSON file = spark.read.json() [OK]

Hint: Use spark.read.json() to load JSON files [OK]

Common Mistakes:

• Using spark.read.csv() for JSON files
• Using spark.load.json() without format
• Using spark.read.text() for structured JSON

3. Given the following JSON data loaded into a DataFrame df:

[{"name": "Alice", "info": {"age": 30, "city": "NY"}}, {"name": "Bob", "info": {"age": 25, "city": "LA"}}]

What will be the output of df.select("info.age").collect()?

medium

A. [Row(age="NY"), Row(age="LA")]

B. [Row(info=30), Row(info=25)]

C. [Row(age=30), Row(age=25)]

D. [30, 25]

Solution

1. Step 1: Understand nested field selection

   Using select("info.age") selects the nested field age inside info.

2. Step 2: Result of collect()

   collect() returns a list of Row objects with the selected field values.

3. Final Answer:

   [Row(age=30), Row(age=25)] -> Option C

4. Quick Check:

   Select nested field = list of Rows with that field [OK]

Hint: Selecting nested fields returns Rows with field names [OK]

Common Mistakes:

• Expecting plain values instead of Row objects
• Confusing nested field name with top-level
• Selecting wrong nested key

4. You try to select a nested field using df.select("info-age") but get an error. What is the likely cause?

medium

A. Using a dash (-) instead of dot (.) to access nested fields.

B. The JSON file is empty.

C. The DataFrame was not loaded with spark.read.json().

D. Nested fields cannot be selected in Spark.

Solution

1. Step 1: Check syntax for nested field access

   Spark uses dot notation like info.age, not dash.

2. Step 2: Understand error cause

   Using info-age is invalid syntax causing an error.

3. Final Answer:

   Using a dash (-) instead of dot (.) to access nested fields. -> Option A

4. Quick Check:

   Nested field access = dot notation [OK]

Hint: Use dot, not dash, to access nested JSON fields [OK]

Common Mistakes:

• Using dash instead of dot for nested fields
• Assuming nested fields can't be selected
• Ignoring error message details

5. You have a JSON file with nested arrays like this:

[{"id": 1, "scores": [10, 20, 30]}, {"id": 2, "scores": [15, 25]}]

How can you transform this DataFrame to have one row per score with columns id and score?

hard

A. Use df.select("id", "scores") without explode.

B. Use df.select("id", explode("scores").alias("score")) to flatten the array.

C. Use df.groupBy("id").count() to count scores.

D. Use df.withColumn("score", array("scores")) to copy scores.

Solution

1. Step 1: Understand array flattening

   The explode() function creates a new row for each element in the array.

2. Step 2: Apply explode to nested array

   Using df.select("id", explode("scores").alias("score")) flattens scores into separate rows.

3. Final Answer:

   Use df.select("id", explode("scores").alias("score")) to flatten the array. -> Option B

4. Quick Check:

   Flatten nested arrays = explode() [OK]

Hint: Use explode() to flatten nested arrays into rows [OK]

Common Mistakes:

• Not using explode and keeping arrays nested
• Using groupBy() instead of explode()
• Trying to copy arrays without flattening