Manan Bakshi created SPARK-23463:
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             Summary: 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


I have a simple dataframe 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.



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