How to convert JSON file into regular table DataFrame in Apache Spark - apache-spark

Question

I have the following JSON fields
{"constructorId":1,"constructorRef":"mclaren","name":"McLaren","nationality":"British","url":"http://en.wikipedia.org/wiki/McLaren"}
{"constructorId":2,"constructorRef":"bmw_sauber","name":"BMW Sauber","nationality":"German","url":"http://en.wikipedia.org/wiki/BMW_Sauber"}
The following code produces the the following DataFrame:
I'm running the code on Databricks
df = (spark.read
.format(csv) \
.schema(mySchema) \
.load(dataPath)
)
display(df)
However, I need the DataFrame to look like the following:
I believe the problem is because the JSON is nested, and I'm trying to convert to CSV. However, I do need to convert to CSV.
Is there code that I can apply to remove the nested feature of the JSON?

Answer 1

Just try:
someDF = spark.read.json(somepath)
Infer schema by default or supply your own, set in your case in pySpark multiLine to false.
someDF = spark.read.json(somepath, someschema, multiLine=False)
See https://spark.apache.org/docs/latest/sql-data-sources-json.html
With schema inference:
df = spark.read.option("multiline","false").json("/FileStore/tables/SOabc2.txt")
df.printSchema()
df.show()
df.count()
returns:
root
|-- constructorId: long (nullable = true)
|-- constructorRef: string (nullable = true)
|-- name: string (nullable = true)
|-- nationality: string (nullable = true)
|-- url: string (nullable = true)
+-------------+--------------+----------+-----------+--------------------+
|constructorId|constructorRef| name|nationality| url|
+-------------+--------------+----------+-----------+--------------------+
| 1| mclaren| McLaren| British|http://en.wikiped...|
| 2| bmw_sauber|BMW Sauber| German|http://en.wikiped...|
+-------------+--------------+----------+-----------+--------------------+
Out[11]: 2