Load Nested JSON with Array into a Spark Dataframe using Scala

In this article we will load nested JSON data with an array into a Spark Dataframe using Scala.

The nested json file with an array is provided below for reference.



The SBT library dependencies are shown below for reference.

scalaVersion := "2.11.12"

libraryDependencies += "org.apache.spark" %% "spark-core" % "2.3.0"

libraryDependencies += "org.apache.spark" %% "spark-sql" % "2.3.0"

The Scala program is provided below.

import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions._

object NestedJsonReader extends App {

  System.setProperty("hadoop.home.dir","C:\\intellij.winutils")

  val spark = SparkSession.builder()
    .master("local")
    .appName("JsonFileReader")
    .getOrCreate()

  val df = spark.read
    .format("json")
    .option("multiline","true")
    .load("C:\\data\\nested-data.json")

  //show dataframe with truncated data values if they exceed default column display width  df.show()

  //don't truncate the value, instead expand the column automatically so all the entire value can be viewed  df.show(false)

  //explode the Employees array into rows  df.withColumn("Employees", explode(col("Employees"))).show(false)

  //select each column from the exploded data frame  df.withColumn("Employees", explode(col("Employees")))
    .select("Department", "Employees.Id", "Employees.Age", "Employees.Name", "Employees.Name.FirstName", "Employees.Name.FirstName").show(false)
}

Here is the output after running - df.show().

Here is the output after running - df.show(false).

Here is the output after running - df.withColumn("Employees", explode(col("Employees"))).show(false).

Here is the output after running - df.withColumn("Employees", explode(col("Employees")))
  .select("Department", "Employees.Id", "Employees.Age", "Employees.Name", "Employees.Name.FirstName", "Employees.Name.FirstName").show(false).

Thanks. That is all for now!