Spark: correct schema to load JSON as DataFrame - json

Question

I have a JSON like
{ 1234 : "blah1", 9807: "blah2", 467: "blah_k", ...}
written to a gzipped file. It is a mapping of one ID space to another where the keys are ints and values are strings.
I want to load it as a DataFrame in Spark.
I loaded it as,
val df = spark.read.format("json").load("my_id_file.json.gz")
By default, Spark loaded it with a schema that looks like
|-- 1234: string (nullable = true)
|-- 9807: string (nullable = true)
|-- 467: string (nullable = true)
Instead, I want to my DataFrame to look like
+----+------+
|id1 |id2 |
+----+------+
|1234|blah1 |
|9007|blah2 |
|467 |blah_k|
+----+------+
So, I tried the following.
import org.apache.spark.sql.types._
val idMapSchema = StructType(Array(StructField("id1", IntegerType, true), StructField("id2", StringType, true)))
val df = spark.read.format("json").schema(idMapSchema).load("my_id_file.json.gz")
However, the loaded data frame looks like
scala> df.show
+----+----+
|id1 |id2 |
+----+----+
|null|null|
+----+----+
How can I specify the schema to fix this? Is there a "pure" dataframe approach (without creating an RDD and then creating DataFrame)?

Answer 1

One way to achieve this is to read the input file as textFile and apply your parsing logic within map() and then convert the result to dataframe
import scala.collection.JavaConversions._
import scala.collection.mutable.ListBuffer
val rdd = sparkSession.sparkContext.textFile("my_input_file_path")
.map(row => {
val list = new ListBuffer[String]()
val inputJson = new JSONObject(row)
for (key <- inputJson.keySet()) {
val resultJson = new JSONObject()
resultJson.put("col1", key)
resultJson.put("col2", inputJson.get(key))
list += resultJson.toString()
}
list
}).flatMap(row => row)
val df = sparkSession.read.json(rdd)
df.printSchema()
df.show(false)
output:
root
|-- col1: string (nullable = true)
|-- col2: string (nullable = true)
+----+------+
|col1|col2 |
+----+------+
|1234|blah1 |
|467 |blah_k|
|9807|blah2 |
+----+------+