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Posted to issues@spark.apache.org by "Manan Bakshi (JIRA)" <ji...@apache.org> on 2018/02/19 08:51:00 UTC
[jira] [Commented] (SPARK-23463) Filter operation fails to handle
blank values and evicts rows that even satisfy the filtering condition
[ https://issues.apache.org/jira/browse/SPARK-23463?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16368879#comment-16368879 ]
Manan Bakshi commented on SPARK-23463:
--------------------------------------
I believe that the bug has something to do with CBO that was introduced in Spark 2.2.0. The CBO looks at blank min value in table stats and is unable to decide on filter selectivity.
> Filter operation fails to handle blank values and evicts rows that even satisfy the filtering condition
> -------------------------------------------------------------------------------------------------------
>
> Key: SPARK-23463
> URL: https://issues.apache.org/jira/browse/SPARK-23463
> Project: Spark
> Issue Type: Bug
> Components: PySpark
> Affects Versions: 2.2.1
> Reporter: Manan Bakshi
> Priority: Critical
>
> I have a simple dataframe with some blank values as below
> ||dev||val||
> |ALL|0.01|
> |ALL|0.02|
> |ALL|0.004|
> |ALL| |
> |ALL|2.5|
> |ALL|4.5|
> |ALL|45|
> Running a simple filter operation over val column in this dataframe yields unexpected results. For eg. the following query returned an empty dataframe:
> df.filter(df["val"] > 0)
> ||dev||val||
> However, the filter operation works as expected if 0 in filter condition is replaced by float 0.0
> df.filter(df["val"] > 0.0)
> ||dev||val||
> |ALL|0.01|
> |ALL|0.02|
> |ALL|0.004|
> |ALL|2.5|
> |ALL|4.5|
> |ALL|45|
>
> Note that this bug only exists in Spark 2.2.0 and later. The previous versions filter as expected for both int (0) and float (0.0) values in the filter condition.
> Also, the filter operation works as expected for all versions, if there are no blank values.
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