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https://issues.apache.org/jira/browse/SPARK-30421?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17011422#comment-17011422
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Tobias Hermann commented on SPARK-30421:
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[~cloud_fan] I think it is an issue because it means one can not simply look at
the schema of a dataframe to determine if an operation is valid. Instead one
has to consider the whole history of how the dataframe was created/derived.
This leads to the effect that refactorings, e.g., changing the way of creation
of a dataframe, will break one's code, even though the refactoring should be
totally OK because it results in the exact same dataframe schema.
> Dropped columns still available for filtering
> ---------------------------------------------
>
> Key: SPARK-30421
> URL: https://issues.apache.org/jira/browse/SPARK-30421
> Project: Spark
> Issue Type: Bug
> Components: Spark Core
> Affects Versions: 2.4.4
> Reporter: Tobias Hermann
> Priority: Minor
>
> The following minimal example:
> {quote}val df = Seq((0, "a"), (1, "b")).toDF("foo", "bar")
> df.select("foo").where($"bar" === "a").show
> df.drop("bar").where($"bar" === "a").show
> {quote}
> should result in an error like the following:
> {quote}org.apache.spark.sql.AnalysisException: cannot resolve '`bar`' given
> input columns: [foo];
> {quote}
> However, it does not but instead works without error, as if the column "bar"
> would exist.
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