[jira] [Created] (SPARK-18709) Failure to throw error and automatic null conversion bug when creating a Spark Datarame with incompatible types for fields.

["Amogh Param (JIRA)" <ji...@apache.org>]

Amogh Param created SPARK-18709:
-----------------------------------

             Summary: Failure to throw error and automatic null conversion bug 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: Spark Core
    Affects Versions: 1.6.3, 1.6.2
            Reporter: Amogh Param
             Fix For: 2.0.2


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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