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https://issues.apache.org/jira/browse/SPARK-13802?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15983094#comment-15983094
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Furcy Pin commented on SPARK-13802:
-----------------------------------
Hi, I ran into similar issues and found this Jira, so I would like to add some
water to the mill.
I ran this in the pyspark shell v2.1.0 :
{code}
>>> from pyspark.sql.types import *
>>> from pyspark.sql import Row
>>>
>>> rdd = spark.sparkContext.parallelize(range(1, 4))
>>>
>>> schema = StructType([StructField('a', IntegerType()), StructField('b',
>>> StringType())])
>>> spark.createDataFrame(rdd.map(lambda r: Row(a=1, b=None)), schema).collect()
[Row(a=1, b=None), Row(a=1, b=None), Row(a=1, b=None)]
>>>
>>> schema = StructType([StructField('b', IntegerType()), StructField('a',
>>> StringType())])
>>> spark.createDataFrame(rdd.map(lambda r: Row(b=1, a=None)), schema).collect()
[Row(b=1, a=None), Row(b=1, a=None), Row(b=1, a=None)]
{code}
When applying a Schema, it seems that the Rows fields names are correctly
matched and reordered according to the schema, which is quite nice,
even if when creating a Row alone, the fields are ordered differently:
{code}
>>> Row(b=1, a=None)
Row(a=None, b=1)
{code}
However I get an inconsistent behavior when I start using structs:
{code}
>>> schema = StructType([StructField('a', IntegerType()), StructField('b',
>>> StructType([StructField('c', StringType())]))])
>>> spark.createDataFrame(rdd.map(lambda r: Row(a=1, b=None)), schema).collect()
[Row(a=1, b=None), Row(a=1, b=None), Row(a=1, b=None)]
>>>
>>> schema = StructType([StructField('b', IntegerType()), StructField('a',
>>> StructType([StructField('c', StringType())]))])
>>> spark.createDataFrame(rdd.map(lambda r: Row(b=1, a=None)), schema).collect()
...
Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent
call last):
File "spark/python/lib/pyspark.zip/pyspark/worker.py", line 174, in main
process()
File "spark/python/lib/pyspark.zip/pyspark/worker.py", line 169, in process
serializer.dump_stream(func(split_index, iterator), outfile)
File "spark/python/lib/pyspark.zip/pyspark/serializers.py", line 268, in
dump_stream
vs = list(itertools.islice(iterator, batch))
File "spark/python/pyspark/sql/types.py", line 576, in toInternal
return tuple(f.toInternal(v) for f, v in zip(self.fields, obj))
File "spark/python/pyspark/sql/types.py", line 576, in <genexpr>
return tuple(f.toInternal(v) for f, v in zip(self.fields, obj))
File "spark/python/lib/pyspark.zip/pyspark/sql/types.py", line 436, in
toInternal
return self.dataType.toInternal(obj)
File "spark/python/lib/pyspark.zip/pyspark/sql/types.py", line 593, in
toInternal
raise ValueError("Unexpected tuple %r with StructType" % obj)
ValueError: Unexpected tuple 1 with StructType
{code}
So it seems to me that pyspark can match a Row's field names with a schema, but
only when no struct is involved.
This doesn't seem very consistent so I believe that it should be considered a
bug.
> Fields order in Row(**kwargs) is not consistent with Schema.toInternal method
> -----------------------------------------------------------------------------
>
> Key: SPARK-13802
> URL: https://issues.apache.org/jira/browse/SPARK-13802
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 1.6.0
> Reporter: Szymon Matejczyk
>
> When using Row constructor from kwargs, fields in the tuple underneath are
> sorted by name. When Schema is reading the row, it is not using the fields in
> this order.
> {code}
> from pyspark.sql import Row
> from pyspark.sql.types import *
> schema = StructType([
> StructField("id", StringType()),
> StructField("first_name", StringType())])
> row = Row(id="39", first_name="Szymon")
> schema.toInternal(row)
> Out[5]: ('Szymon', '39')
> {code}
> {code}
> df = sqlContext.createDataFrame([row], schema)
> df.show(1)
> +------+----------+
> | id|first_name|
> +------+----------+
> |Szymon| 39|
> +------+----------+
> {code}
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