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https://issues.apache.org/jira/browse/SPARK-10847?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-10847:
------------------------------------

    Assignee: Apache Spark

> Pyspark - DataFrame - Optional Metadata with `None` triggers cryptic failure
> ----------------------------------------------------------------------------
>
>                 Key: SPARK-10847
>                 URL: https://issues.apache.org/jira/browse/SPARK-10847
>             Project: Spark
>          Issue Type: Bug
>          Components: PySpark, SQL
>    Affects Versions: 1.5.0
>         Environment: Windows 7
> java version "1.8.0_60" (64bit)
> Python 3.4.x
> Standalone cluster mode (not local[n]; a full local cluster)
>            Reporter: Shea Parkes
>            Assignee: Apache Spark
>            Priority: Minor
>
> If the optional metadata passed to `pyspark.sql.types.StructField` includes a 
> pythonic `None`, the `pyspark.SparkContext.createDataFrame` will fail with a 
> very cryptic/unhelpful error.
> Here is a minimal reproducible example:
> {code:none}
> # Assumes sc exists
> import pyspark.sql.types as types
> sqlContext = SQLContext(sc)
> literal_metadata = types.StructType([
>     types.StructField(
>         'name',
>         types.StringType(),
>         nullable=True,
>         metadata={'comment': 'From accounting system.'}
>         ),
>     types.StructField(
>         'age',
>         types.IntegerType(),
>         nullable=True,
>         metadata={'comment': None}
>         ),
>     ])
> literal_rdd = sc.parallelize([
>     ['Bob', 34],
>     ['Dan', 42],
>     ])
> print(literal_rdd.take(2))
> failed_dataframe = sqlContext.createDataFrame(
>     literal_rdd,
>     literal_metadata,
>     )
> {code}
> This produces the following ~stacktrace:
> {noformat}
> Traceback (most recent call last):
>   File "<stdin>", line 1, in <module>
>   File "<string>", line 28, in <module>
>   File 
> "S:\ZQL\Software\Hotware\spark-1.5.0-bin-hadoop2.6\python\pyspark\sql\context.py",
>  line 408, in createDataFrame
>     jdf = self._ssql_ctx.applySchemaToPythonRDD(jrdd.rdd(), schema.json())
>   File 
> "S:\ZQL\Software\Hotware\spark-1.5.0-bin-hadoop2.6\python\lib\py4j-0.8.2.1-src.zip\py4j\java_gateway.py",
>  line 538, in __call__
>   File 
> "S:\ZQL\Software\Hotware\spark-1.5.0-bin-hadoop2.6\python\pyspark\sql\utils.py",
>  line 36, in deco
>     return f(*a, **kw)
>   File 
> "S:\ZQL\Software\Hotware\spark-1.5.0-bin-hadoop2.6\python\lib\py4j-0.8.2.1-src.zip\py4j\protocol.py",
>  line 300, in get_return_value
> py4j.protocol.Py4JJavaError: An error occurred while calling 
> o757.applySchemaToPythonRDD.
> : java.lang.RuntimeException: Do not support type class scala.Tuple2.
>       at 
> org.apache.spark.sql.types.Metadata$$anonfun$fromJObject$1.apply(Metadata.scala:160)
>       at 
> org.apache.spark.sql.types.Metadata$$anonfun$fromJObject$1.apply(Metadata.scala:127)
>       at scala.collection.immutable.List.foreach(List.scala:318)
>       at org.apache.spark.sql.types.Metadata$.fromJObject(Metadata.scala:127)
>       at 
> org.apache.spark.sql.types.DataType$.org$apache$spark$sql$types$DataType$$parseStructField(DataType.scala:173)
>       at 
> org.apache.spark.sql.types.DataType$$anonfun$parseDataType$1.apply(DataType.scala:148)
>       at 
> org.apache.spark.sql.types.DataType$$anonfun$parseDataType$1.apply(DataType.scala:148)
>       at 
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
>       at 
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
>       at scala.collection.immutable.List.foreach(List.scala:318)
>       at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
>       at scala.collection.AbstractTraversable.map(Traversable.scala:105)
>       at 
> org.apache.spark.sql.types.DataType$.parseDataType(DataType.scala:148)
>       at org.apache.spark.sql.types.DataType$.fromJson(DataType.scala:96)
>       at org.apache.spark.sql.SQLContext.parseDataType(SQLContext.scala:961)
>       at 
> org.apache.spark.sql.SQLContext.applySchemaToPythonRDD(SQLContext.scala:970)
>       at sun.reflect.GeneratedMethodAccessor38.invoke(Unknown Source)
>       at sun.reflect.DelegatingMethodAccessorImpl.invoke(Unknown Source)
>       at java.lang.reflect.Method.invoke(Unknown Source)
>       at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)
>       at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)
>       at py4j.Gateway.invoke(Gateway.java:259)
>       at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)
>       at py4j.commands.CallCommand.execute(CallCommand.java:79)
>       at py4j.GatewayConnection.run(GatewayConnection.java:207)
>       at java.lang.Thread.run(Unknown Source)
> {noformat}
> I believe the most important line of the traceback is this one:
> {noformat}
> py4j.protocol.Py4JJavaError: An error occurred while calling 
> o757.applySchemaToPythonRDD.
> : java.lang.RuntimeException: Do not support type class scala.Tuple2.
> {noformat}
> But it wasn't enough for me to figure out the problem; I had to steadily 
> simplify my program until I could identify what caused the problem.



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