Fail to specify JSON schema in Spark [duplicate] - json

Question

I wrote the following code in both Scala & Python, however the DataFrame that is returned doesn't appear to apply the non-nullable fields in my schema that I am applying. italianVotes.csv is a csv file with '~' as a separator and four fields. I'm using Spark 2.1.0.
italianVotes.csv
2657~135~2~2013-11-22 00:00:00.0
2658~142~2~2013-11-22 00:00:00.0
2659~142~1~2013-11-22 00:00:00.0
2660~140~2~2013-11-22 00:00:00.0
2661~140~1~2013-11-22 00:00:00.0
2662~1354~2~2013-11-22 00:00:00.0
2663~1356~2~2013-11-22 00:00:00.0
2664~1353~2~2013-11-22 00:00:00.0
2665~1351~2~2013-11-22 00:00:00.0
2667~1357~2~2013-11-22 00:00:00.0
Scala
import org.apache.spark.sql.types._
val schema = StructType(
StructField("id", IntegerType, false) ::
StructField("postId", IntegerType, false) ::
StructField("voteType", IntegerType, true) ::
StructField("time", TimestampType, true) :: Nil)
val fileName = "italianVotes.csv"
val italianDF = spark.read.schema(schema).option("sep", "~").csv(fileName)
italianDF.printSchema()
// output
root
|-- id: integer (nullable = true)
|-- postId: integer (nullable = true)
|-- voteType: integer (nullable = true)
|-- time: timestamp (nullable = true)
Python
from pyspark.sql.types import *
schema = StructType([
StructField("id", IntegerType(), False),
StructField("postId", IntegerType(), False),
StructField("voteType", IntegerType(), True),
StructField("time", TimestampType(), True),
])
file_name = "italianVotes.csv"
italian_df = spark.read.csv(file_name, schema = schema, sep = "~")
# print schema
italian_df.printSchema()
root
|-- id: integer (nullable = true)
|-- postId: integer (nullable = true)
|-- voteType: integer (nullable = true)
|-- time: timestamp (nullable = true)
My main question is why are the first two fields nullable when I have set them to non-nullable in my schema?

Answer 1

In general Spark Datasets either inherit nullable property from its parents, or infer based on the external data types.
You can argue if it is a good approach or not but ultimately it is sensible. If semantics of a data source doesn't support nullability constraints, then application of a schema cannot either. At the end of the day it is always better to assume that things can be null, than fail on the runtime if this the opposite assumption turns out to be incorrect.