Spark Create DF from json string and string scala

Question

I have a json string and a different string I'd like to create a dataframe of.

val body = """{
             |  "time": "2020-07-01T17:17:15.0495314Z",
             |  "ver": "4.0",
             |  "name": "samplename",
             |  "iKey": "o:something",
             |  "random": {
             |    "stuff": {
             |      "eventFlags": 258,
             |      "num5": "DHM",
             |      "num2": "something",
             |      "flags": 415236612,
             |      "num1": "4004825",
             |      "seq": 44
             |    },
             |    "banana": {
             |      "id": "someid",
             |      "ver": "someversion",
             |      "asId": 123
             |    },
             |    "something": {
             |      "example": "somethinghere"
             |    },
             |    "apple": {
             |      "time": "2020-07-01T17:17:37.874Z",
             |      "flag": "something",
             |      "userAgent": "someUserAgent",
             |      "auth": 12,
             |      "quality": 0
             |    },
             |    "loc": {
             |      "country": "US"
             |    }
             |  },
             |  "EventEnqueuedUtcTime": "2020-07-01T17:17:59.804Z"
             |}
             |""".stripMargin

val offset = "10"

I tried

    val data = Seq(body, offset)

    val columns = Seq("body","offset")
    import sparkSession.sqlContext.implicits._
    val df = data.toDF(columns:_*)

As well as

val data = Seq(body, offset)  
val rdd = sparkSession.sparkContext.parallelize((data))

val dfFromRdd = rdd.toDF("body", "offset")

dfFromRdd.show(20, false)

but for both I get this an error: "value toDF is not a member of org.apache.spark.RDD[String]"

Is there a different way I can create a dataframe that will have one column with my json body data, and another column with my offset string value?

Edit: I've also tried the following:

    val offset = "1000"
    val data = Seq(body, offset)

   val rdd = sparkSession.sparkContext.parallelize((data))

    val dfFromRdd = rdd.toDF("body", "offset")

    dfFromRdd.show(20, false)

and get an error of column mismatch: "The number of columns doesn't match. Old column names (1): value New column names (2): body, offset"

I dont understand why my data has the column name of "value"

Answer 1

I guess the issue is with your Seq syntax, elements should be tuples. Below code has worked for me,

val data = Seq((body, offset))  // <--- Check this line
val columns = Seq("body","offset")

import sparkSession.sqlContext.implicits._

data.toDF(columns:_*).printSchema()

/*
/
/ root
/  |-- body: string (nullable = true)
/  |-- offset: string (nullable = true)
/
*/

data.toDF(columns:_*).show()

/*
/
/ +--------------------+------+
/ |                body|offset|
/ +--------------------+------+
/ |{
/  "time": "2020...|    10|
/ +--------------------+------+
/
/*