Reading JSON and nested data in Apache Spark - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of reading nested JSON with Spark

What is the output of the following Spark code when reading a nested JSON file and selecting a nested field?

Apache Spark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName('Test').getOrCreate()

json_data = '''[
  {"id": 1, "info": {"name": "Alice", "age": 30}},
  {"id": 2, "info": {"name": "Bob", "age": 25}}
]'''

rdd = spark.sparkContext.parallelize([json_data])
df = spark.read.json(rdd)
df.select('info.name').show()

A AnalysisException: cannot resolve 'info.name' given input columns: [id, info]

B

+----------+
|info.name |
+----------+
|Alice     |
|Bob       |
+----------+

C

+----------+
|info.name |
+----------+
|null      |
|null      |
+----------+

D

+----------+
|name      |
+----------+
|Alice     |
|Bob       |
+----------+

💡 Hint

Remember how Spark handles nested fields in select statements.

In Spark, to select a nested field, you must use the dot notation without quotes or use col() function. Using 'info.name' as a string in select causes an AnalysisException.

❓ data_output

intermediate

Count of nested JSON records after flattening

Given a DataFrame loaded from nested JSON, what is the count of rows after flattening the nested array field?

Apache Spark

from pyspark.sql import SparkSession
from pyspark.sql.functions import explode

spark = SparkSession.builder.appName('Test').getOrCreate()

json_data = '''[
  {"id": 1, "items": [{"name": "apple"}, {"name": "banana"}]},
  {"id": 2, "items": [{"name": "orange"}]}
]'''
rdd = spark.sparkContext.parallelize([json_data])
df = spark.read.json(rdd)

flat_df = df.select('id', explode('items').alias('item'))
count = flat_df.count()

A 3

B 1

C 2

D 0

💡 Hint

Count how many total items are in all nested arrays combined.

The first record has 2 items, the second has 1 item, so total rows after explode are 3.

🔧 Debug

advanced

Identify the error when reading malformed nested JSON

What error will this Spark code produce when reading a malformed nested JSON string?

Apache Spark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName('Test').getOrCreate()

json_data = '''[
  {"id": 1, "info": {"name": "Alice", "age": 30},
  {"id": 2, "info": {"name": "Bob", "age": 25}}
]'''

rdd = spark.sparkContext.parallelize([json_data])
df = spark.read.json(rdd)
df.show()

A ValueError: Expecting ',' delimiter

B AnalysisException: Malformed records are detected in record parsing

C No error, DataFrame shows both records correctly

D TypeError: 'str' object is not iterable

💡 Hint

Check the JSON syntax carefully for missing commas or brackets.

The JSON string is missing a comma between the two objects, causing Spark to detect malformed records and raise AnalysisException.

🚀 Application

advanced

Extract nested fields and create new columns

Which code snippet correctly extracts the nested 'age' field from 'info' and creates a new column 'age' in the DataFrame?

Apache Spark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName('Test').getOrCreate()

json_data = '''[
  {"id": 1, "info": {"name": "Alice", "age": 30}},
  {"id": 2, "info": {"name": "Bob", "age": 25}}
]'''
rdd = spark.sparkContext.parallelize([json_data])
df = spark.read.json(rdd)

A df.withColumn('age', df['info.age']).show()

B df.withColumn('age', df.info['age']).show()

C df.withColumn('age', df['info']['age']).show()

D df.withColumn('age', df.info.age).show()

💡 Hint

Use dot notation to access nested fields in Spark DataFrame columns.

In Spark, nested fields can be accessed using dot notation like df.info.age. Other options cause errors or do not work.

🧠 Conceptual

expert

Understanding schema inference for nested JSON in Spark

When Spark reads a nested JSON file without a predefined schema, what is the behavior of schema inference for nested fields?

A Spark requires a user-defined schema to read nested JSON; otherwise, it raises an error.

B Spark infers only the top-level fields and treats nested fields as strings.

C Spark infers the schema recursively, including all nested fields and their types automatically.

D Spark infers the schema but ignores nested arrays and maps, flattening them into strings.

💡 Hint

Think about how Spark handles complex JSON structures by default.

Spark's JSON reader automatically infers nested schemas recursively, detecting nested structs, arrays, and their types.

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