Parse JSON file using Spark Scala - json

Question

I've the JSON source data file as like below and i'll need the Expected Results in a quite different format which is also shown below, is there a way i can achieve this using Spark Scala. Appreciate your help on this
JSON source data file
{
"APP": [
{
"E": 1566799999225,
"V": 44.0
},
{
"E": 1566800002758,
"V": 61.0
}
],
"ASP": [
{
"E": 1566800009446,
"V": 23.399999618530273
}
],
"TT": 0,
"TVD": [
{
"E": 1566799964040,
"V": 50876515
}
],
"VIN": "FU74HZ501740XXXXX"
}
Expected Results:
JSON Schema:
|-- APP: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- E: long (nullable = true)
| | |-- V: double (nullable = true)
|-- ASP: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- E: long (nullable = true)
| | |-- V: double (nullable = true)
|-- ATO: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- E: long (nullable = true)
| | |-- V: double (nullable = true)
|-- MSG_TYPE: string (nullable = true)
|-- RPM: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- E: long (nullable = true)
| | |-- V: double (nullable = true)
|-- TT: long (nullable = true)
|-- TVD: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- E: long (nullable = true)
| | |-- V: long (nullable = true)
|-- VIN: string (nullable = true)

Answer 1

You can start by reading your json file:
val inputDataFrame: DataFrame = sparkSession
.read
.option("multiline", true)
.json(yourJsonPath)
Then you can create a simple rule to get APP, ASP, ATO, since it's the only fields in the input that have a struct datatype:
val inputDataFrameFields: Array[StructField] = inputDataFrame.schema.fields
var snColumn = new Array[String](inputDataFrame.schema.length)
for( x <- 0 to (inputDataFrame.schema.length -1)) {
if(inputDataFrameFields.apply(x).dataType.isInstanceOf[ArrayType] && !inputDataFrameFields.apply(x).name.isEmpty) {
snColumn(x) = inputDataFrameFields.apply(x).name
}
}
Then you create your empty dataframe as follow and populate it:
val outputSchema = StructType(
List(
StructField("VIN", StringType, true),
StructField(
"EVENTS",
ArrayType(
StructType(Array(
StructField("SN", StringType, true),
StructField("E", IntegerType, true),
StructField("V", DoubleType, true)
)))),
StructField("TT", StringType, true)
)
)
val outputDataFrame = sparkSession.createDataFrame(sparkSession.sparkContext.emptyRDD[Row], outputSchema)
Then you need to create some udfs to parse your input and do the correct mapping.
Hope this helps

Answer 2

Here is a solution to parse a json to a spark dataframe adapted to your data :
val input = "{\"APP\":[{\"E\":1566799999225,\"V\":44.0},{\"E\":1566800002758,\"V\":61.0}],\"ASP\":[{\"E\":1566800009446,\"V\":23.399999618530273}],\"TT\":0,\"TVD\":[{\"E\":1566799964040,\"V\":50876515}],\"VIN\":\"FU74HZ501740XXXXX\"}"
import sparkSession.implicits._
val outputDataFrame = sparkSession.read.option("multiline", true).option("mode","PERMISSIVE")
.json(Seq(input).toDS)
.withColumn("APP", explode(col("APP")))
.withColumn("ASP", explode(col("ASP")))
.withColumn("TVD", explode(col("TVD")))
.select(
col("VIN"),col("TT"),
col("APP").getItem("E").as("APP_E"),
col("APP").getItem("V").as("APP_V"),
col("ASP").getItem("E").as("ASP_E"),
col("ASP").getItem("V").as("ASP_E"),
col("TVD").getItem("E").as("TVD_E"),
col("TVD").getItem("V").as("TVD_E")
)
outputDataFrame.show(truncate = false)
/*
+-----------------+---+-------------+-----+-------------+------------------+-------------+--------+
|VIN |TT |APP_E |APP_V|ASP_E |ASP_E |TVD_E |TVD_E |
+-----------------+---+-------------+-----+-------------+------------------+-------------+--------+
|FU74HZ501740XXXXX|0 |1566799999225|44.0 |1566800009446|23.399999618530273|1566799964040|50876515|
|FU74HZ501740XXXXX|0 |1566800002758|61.0 |1566800009446|23.399999618530273|1566799964040|50876515|
+-----------------+---+-------------+-----+-------------+------------------+-------------+--------+
*/