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https://issues.apache.org/jira/browse/SPARK-30421?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17010271#comment-17010271
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Takeshi Yamamuro commented on SPARK-30421:
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Based on the current implementation, drop and select (drop is a shorthand for a
partial use-case of select?) seems to have the same semantics. If so, that
query might be correct in lazy evaluation.
btw, for changing this behaviour, IMO it would be better to reconstruct
dataframe([https://github.com/maropu/spark/commit/fac04161405b9ee755b4c7f87de2a144c609c7fa])
instead of modifying the resolution logic. That's because the resolution logic
affects many places.
> 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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