+1 On Thu, Nov 7, 2019 at 6:08 PM Hyukjin Kwon <gurwls...@gmail.com> wrote: > > +1 > > 2019년 11월 6일 (수) 오후 11:38, Wenchen Fan <cloud0...@gmail.com>님이 작성: >> >> Sounds reasonable to me. We should make the behavior consistent within Spark. >> >> On Tue, Nov 5, 2019 at 6:29 AM Bryan Cutler <cutl...@gmail.com> wrote: >>> >>> 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
-- Shane Knapp UC Berkeley EECS Research / RISELab Staff Technical Lead https://rise.cs.berkeley.edu --------------------------------------------------------------------- To unsubscribe e-mail: dev-unsubscr...@spark.apache.org
