Reading JSON and nested data in Apache Spark

Introduction

We use JSON files because they store data in a simple, organized way. Reading JSON helps us get this data into Spark to analyze it. Nested data means some data is inside other data, like boxes inside boxes.

You have data from web APIs that comes in JSON format.

You want to analyze user information where addresses are inside user details.

You receive logs or events stored as JSON with nested fields.

You want to load configuration files saved as JSON with multiple levels.

Syntax

Apache Spark

spark.read.json(path)

This reads a JSON file or folder into a Spark DataFrame.

Spark automatically detects nested structures and creates columns for them.

Examples

Reads a simple JSON file and shows the data.

Apache Spark

df = spark.read.json("data/simple.json")
df.show()

Reads a JSON file with nested data and prints the schema to see the structure.

Apache Spark

df = spark.read.json("data/nested.json")
df.printSchema()

Selects a top-level field and a nested field inside 'address'.

Apache Spark

df.select("name", "address.city").show()

Sample Program

This program creates a Spark session, writes sample JSON data with nested fields to a file, reads it back into a DataFrame, and shows the data and schema. It also selects a nested field to demonstrate accessing nested data.

Apache Spark

from pyspark.sql import SparkSession

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

# Sample JSON data with nested structure
json_data = [
  '{"name": "Alice", "age": 30, "address": {"city": "New York", "zip": "10001"}}',
  '{"name": "Bob", "age": 25, "address": {"city": "Los Angeles", "zip": "90001"}}'
]

# Save sample data to a file
with open("sample.json", "w") as f:
    for line in json_data:
        f.write(line + "\n")

# Read the JSON file
df = spark.read.json("sample.json")

# Show the data
print("DataFrame content:")
df.show()

# Show the schema to understand nested data
print("DataFrame schema:")
df.printSchema()

# Select nested field
print("Selected nested field (address.city):")
df.select("name", "address.city").show()

spark.stop()

Important Notes

Nested fields are accessed using dot notation like address.city.

Use printSchema() to understand the structure of nested JSON data.

Make sure the JSON file is accessible to Spark (local or distributed file system).

Summary

JSON files store data in a structured way, often with nested fields.

Spark can read JSON files directly and handle nested data automatically.

Use dot notation to select nested fields inside the DataFrame.

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