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https://issues.apache.org/jira/browse/SPARK-18709?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15724101#comment-15724101
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Sean Owen commented on SPARK-18709:
-----------------------------------
BTW do you know what change fixed this, by any chance?
> Automatic null conversion bug (instead of throwing error) when creating a
> Spark Datarame with incompatible types for fields.
> ----------------------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-18709
> URL: https://issues.apache.org/jira/browse/SPARK-18709
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.6.2, 1.6.3
> Reporter: Amogh Param
> Labels: bug
> Fix For: 2.0.0
>
>
> When converting an RDD with a `float` type field to a spark dataframe with an
> `IntegerType` / `LongType` schema field, spark 1.6.2 and 1.6.3 silently
> convert the field values to nulls instead of throwing an error like `LongType
> can not accept object ___ in type <type 'float'>`. However, this seems to be
> fixed in Spark 2.0.2.
> The following example should make the problem clear:
> {code}
> from pyspark.sql.types import StructField, StructType, LongType, DoubleType
> schema = StructType([
> StructField("0", LongType(), True),
> StructField("1", DoubleType(), True),
> ])
> data = [[1.0, 1.0], [nan, 2.0]]
> spark_df = sqlContext.createDataFrame(sc.parallelize(data), schema)
> spark_df.show()
> {code}
> Instead of throwing an error like:
> {code}
> LongType can not accept object 1.0 in type <type 'float'>
> {code}
> Spark converts all the values in the first column to nulls
> Running `spark_df.show()` gives:
> {code}
> +----+---+
> | 0| 1|
> +----+---+
> |null|1.0|
> |null|1.0|
> +----+---+
> {code}
> For the purposes of my computation, I'm doing a `mapPartitions` on a spark
> data frame, and for each partition, converting it into a pandas data frame,
> doing a few computations on this pandas dataframe and the return value will
> be a list of lists, which is converted to an RDD while being returned from
> 'mapPartitions' (for all partitions). This RDD is then converted into a spark
> dataframe similar to the example above, using
> `sqlContext.createDataFrame(rdd, schema)`. The rdd has a column that should
> be converted to a `LongType` in the spark data frame, but since it has
> missing values, it is a `float` type. When spark tries to create the data
> frame, it converts all the values in that column to nulls instead of throwing
> an error that there is a type mismatch.
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