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https://issues.apache.org/jira/browse/SPARK-11319?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15014360#comment-15014360
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Yin Huai commented on SPARK-11319:
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For those data sources, the nullable field is always true unless you specify a
schema and explicitly say the nullable is false. If a user really want to
specify the nullable to false, I think it is reasonable to ask him/her to make
sure there is no null.
I mean for JSON/CSV, it is really easy to have nulls. Even you do not have null
values for some files, it is so easy to have null values in future data.
> PySpark silently Accepts null values in non-nullable DataFrame fields.
> ----------------------------------------------------------------------
>
> Key: SPARK-11319
> URL: https://issues.apache.org/jira/browse/SPARK-11319
> Project: Spark
> Issue Type: Bug
> Components: PySpark, SQL
> Reporter: Kevin Cox
>
> Running the following code with a null value in a non-nullable column
> silently works. This makes the code incredibly hard to trust.
> {code}
> In [2]: from pyspark.sql.types import *
> In [3]: sqlContext.createDataFrame([(None,)], StructType([StructField("a",
> TimestampType(), False)])).collect()
> Out[3]: [Row(a=None)]
> {code}
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