Currently, when a PySpark Row is created with keyword arguments, the fields are sorted alphabetically. This has created a lot of confusion with users because it is not obvious (although it is stated in the pydocs) that they will be sorted alphabetically. Then later when applying a schema and the field order does not match, an error will occur. Here is a list of some of the JIRAs that I have been tracking all related to this issue: SPARK-24915, SPARK-22232, SPARK-27939, SPARK-27712, and relevant discussion of the issue [1].
The original reason for sorting fields is because kwargs in python < 3.6 are not guaranteed to be in the same order that they were entered [2]. Sorting alphabetically ensures a consistent order. Matters are further complicated with the flag _*from_dict*_ that allows the Row fields to to be referenced by name when made by kwargs, but this flag is not serialized with the Row and leads to inconsistent behavior. For instance: >>> spark.createDataFrame([Row(A="1", B="2")], "B string, A string").first() Row(B='2', A='1')>>> spark.createDataFrame(spark.sparkContext.parallelize([Row(A="1", B="2")]), "B string, A string").first() Row(B='1', A='2') I think the best way to fix this is to remove the sorting of fields when constructing a Row. For users with Python 3.6+, nothing would change because these versions of Python ensure that the kwargs stays in the ordered entered. For users with Python < 3.6, using kwargs would check a conf to either raise an error or fallback to a LegacyRow that sorts the fields as before. With Python < 3.6 being deprecated now, this LegacyRow can also be removed at the same time. There are also other ways to create Rows that will not be affected. I have opened a JIRA [3] to capture this, but I am wondering what others think about fixing this for Spark 3.0? [1] https://github.com/apache/spark/pull/20280 [2] https://www.python.org/dev/peps/pep-0468/ [3] https://issues.apache.org/jira/browse/SPARK-29748
