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https://issues.apache.org/jira/browse/SPARK-11319?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14974579#comment-14974579
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Kevin Cox commented on SPARK-11319:
-----------------------------------
Furthermore it appears that some functions are "optimized" based on the
nullability of the column. For example it makes the following expression
incredibly confusing.
{code}
In [29]: df.withColumn('b', df.a.isNull()).collect()
Out[29]: [Row(a=None, b=False)]
{code}
> 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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