[jira] [Updated] (SPARK-20356) Spark sql group by returns incorrect results after join + distinct transformations

["Josh Rosen (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-20356?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Josh Rosen updated SPARK-20356:
-------------------------------
    Labels: correctness  (was: )

> Spark sql group by returns incorrect results after join + distinct transformations
> ----------------------------------------------------------------------------------
>
>                 Key: SPARK-20356
>                 URL: https://issues.apache.org/jira/browse/SPARK-20356
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.2.0
>         Environment: Linux mint 18
> Python 3.5
>            Reporter: Chris Kipers
>            Assignee: Liang-Chi Hsieh
>            Priority: Major
>              Labels: correctness
>             Fix For: 2.2.0, 2.3.0
>
>
> I'm experiencing a bug with the head version of spark as of 4/17/2017. After joining to dataframes, renaming a column and invoking distinct, the results of the aggregation is incorrect after caching the dataframe. The following code snippet consistently reproduces the error.
> from pyspark.sql import SparkSession
> import pyspark.sql.functions as sf
> import pandas as pd
> spark = SparkSession.builder.master("local").appName("Word Count").getOrCreate()
> mapping_sdf = spark.createDataFrame(pd.DataFrame([
>     {"ITEM": "a", "GROUP": 1},
>     {"ITEM": "b", "GROUP": 1},
>     {"ITEM": "c", "GROUP": 2}
> ]))
> items_sdf = spark.createDataFrame(pd.DataFrame([
>     {"ITEM": "a", "ID": 1},
>     {"ITEM": "b", "ID": 2},
>     {"ITEM": "c", "ID": 3}
> ]))
> mapped_sdf = \
>     items_sdf.join(mapping_sdf, on='ITEM').select("ID", sf.col("GROUP").alias('ITEM')).distinct()
> print(mapped_sdf.groupBy("ITEM").count().count())  # Prints 2, correct
> mapped_sdf.cache()
> print(mapped_sdf.groupBy("ITEM").count().count())  # Prints 3, incorrect
> The next code snippet is almost the same after the first except I don't call distinct on the dataframe. This snippet performs as expected:
> mapped_sdf = \
>     items_sdf.join(mapping_sdf, on='ITEM').select("ID", sf.col("GROUP").alias('ITEM'))
> print(mapped_sdf.groupBy("ITEM").count().count())  # Prints 2, correct
> mapped_sdf.cache()
> print(mapped_sdf.groupBy("ITEM").count().count())  # Prints 2, correct
> I don't experience this bug with spark 2.1 or event earlier versions for 2.2



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