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Posted to issues@spark.apache.org by "Manan Bakshi (JIRA)" <ji...@apache.org> on 2018/02/19 08:48:00 UTC
[jira] [Created] (SPARK-23463) Filter operation fails to handle
blank values and evicts rows that even satisfy the filtering condition
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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