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Posted to issues@spark.apache.org by "Jeffrey (JIRA)" <ji...@apache.org> on 2019/01/29 10:41:00 UTC
[jira] [Updated] (SPARK-26767) Filter on a dropDuplicates dataframe
gives inconsistency result
[ https://issues.apache.org/jira/browse/SPARK-26767?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Jeffrey updated SPARK-26767:
----------------------------
Description:
o repeat the problem,
(1) create a csv file with records holding same values for a subset of columns (e.g. colA, colB, colC).
(2) read the csv file as a spark dataframe and then use dropDuplicates to dedup the subset of columns (i.e. dropDuplicates(["colA", "colB", "colC"]))
(3) select the resulting dataframe with where clause. (i.e. df.where("colA = 'A' and colB='B' and colG='G' and colH='H').show(100,False))
=> When (3) is rerun, it gives different number of resulting rows.
was:Fe
> Filter on a dropDuplicates dataframe gives inconsistency result
> ---------------------------------------------------------------
>
> Key: SPARK-26767
> URL: https://issues.apache.org/jira/browse/SPARK-26767
> Project: Spark
> Issue Type: Bug
> Components: Build
> Affects Versions: 2.3.0
> Environment: To repeat the problem,
> (1) create a csv file with records holding same values for a subset of columns (e.g. colA, colB, colC).
> (2) read the csv file as a spark dataframe and then use dropDuplicates to dedup the subset of columns (i.e. dropDuplicates(["colA", "colB", "colC"]))
> (3) select the resulting dataframe with where clause. (i.e. df.where("colA = 'A' and colB='B' and colG='G' and colH='H').show(100,False))
>
> => When (3) is rerun, it gives different number of resulting rows.
> Reporter: Jeffrey
> Priority: Blocker
>
> o repeat the problem,
> (1) create a csv file with records holding same values for a subset of columns (e.g. colA, colB, colC).
> (2) read the csv file as a spark dataframe and then use dropDuplicates to dedup the subset of columns (i.e. dropDuplicates(["colA", "colB", "colC"]))
> (3) select the resulting dataframe with where clause. (i.e. df.where("colA = 'A' and colB='B' and colG='G' and colH='H').show(100,False))
>
> => When (3) is rerun, it gives different number of resulting rows.
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