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https://issues.apache.org/jira/browse/SPARK-33057?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17215468#comment-17215468
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Li Jin commented on SPARK-33057:
--------------------------------
I agree this is an improvement rather than a bug.
Although, I am not sure why this is a "Won't fix"/"Invalid".
Yes the second code adds project and the first one doesn't. However, I think
the logically they are doing the same thing - "Filtering based on the output of
a window operation". Whether the output of the window operation is assigned to
a new field or not doesn't seem to change the logically meaning.
> Cannot use filter with window operations
> ----------------------------------------
>
> Key: SPARK-33057
> URL: https://issues.apache.org/jira/browse/SPARK-33057
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 3.0.1
> Reporter: Li Jin
> Priority: Major
>
> Current, trying to use filter with a window operations will fail:
>
> {code:java}
> df = spark.range(100)
> win = Window.partitionBy().orderBy('id')
> df.filter(F.rank().over(win) > 10).show()
> {code}
> Error:
> {code:java}
> Traceback (most recent call last):
> File "<stdin>", line 1, in <module>
> File
> "/Users/icexelloss/opt/miniconda3/envs/ibis-dev-spark-3/lib/python3.8/site-packages/pyspark/sql/dataframe.py",
> line 1461, in filter
> jdf = self._jdf.filter(condition._jc)
> File
> "/Users/icexelloss/opt/miniconda3/envs/ibis-dev-spark-3/lib/python3.8/site-packages/pyspark/python/lib/py4j-0.10.9-src.zip/py4j/java_gateway.py",
> line 1304, in __call__
> File
> "/Users/icexelloss/opt/miniconda3/envs/ibis-dev-spark-3/lib/python3.8/site-packages/pyspark/sql/utils.py",
> line 134, in deco
> raise_from(converted)
> File "<string>", line 3, in raise_from
> pyspark.sql.utils.AnalysisException: It is not allowed to use window
> functions inside WHERE clause;{code}
> Although the code is same as the code below, which works:
> {code:java}
> df = spark.range(100)
> win = Window.partitionBy().orderBy('id')
> df = df.withColumn('rank', F.rank().over(win))
> df = df[df['rank'] > 10]
> df = df.drop('rank'){code}
>
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