imback82 opened a new pull request #26700: [SPARK-30065][SQL] DataFrameNaFunctions.drop should handle duplicate columns URL: https://github.com/apache/spark/pull/26700 <!-- Thanks for sending a pull request! Here are some tips for you: 1. If this is your first time, please read our contributor guidelines: https://spark.apache.org/contributing.html 2. Ensure you have added or run the appropriate tests for your PR: https://spark.apache.org/developer-tools.html 3. If the PR is unfinished, add '[WIP]' in your PR title, e.g., '[WIP][SPARK-XXXX] Your PR title ...'. 4. Be sure to keep the PR description updated to reflect all changes. 5. Please write your PR title to summarize what this PR proposes. 6. If possible, provide a concise example to reproduce the issue for a faster review. --> ### What changes were proposed in this pull request? <!-- Please clarify what changes you are proposing. The purpose of this section is to outline the changes and how this PR fixes the issue. If possible, please consider writing useful notes for better and faster reviews in your PR. See the examples below. 1. If you refactor some codes with changing classes, showing the class hierarchy will help reviewers. 2. If you fix some SQL features, you can provide some references of other DBMSes. 3. If there is design documentation, please add the link. 4. If there is a discussion in the mailing list, please add the link. --> `DataFrameNaFunctions.drop` doesn't handle duplicate columns even when column names are not specified. ```Scala val left = Seq(("1", null), ("3", "4")).toDF("col1", "col2") val right = Seq(("1", "2"), ("3", null)).toDF("col1", "col2") val df = left.join(right, Seq("col1")) df.printSchema df.na.drop("any").show ``` produces ``` root |-- col1: string (nullable = true) |-- col2: string (nullable = true) |-- col2: string (nullable = true) org.apache.spark.sql.AnalysisException: Reference 'col2' is ambiguous, could be: col2, col2.; at org.apache.spark.sql.catalyst.expressions.package$AttributeSeq.resolve(package.scala:240) ``` The reason for the above failure is that columns are resolved by name and if there are multiple columns with the same name, it will fail due to ambiguity. This PR updates `DataFrameNaFunctions.drop` such that if the columns to drop are not specified, it will resolve ambiguity gracefully by applying `drop` to all the eligible columns. (Note that if the user specifies the columns, it will still continue to fail due to ambiguity). ### Why are the changes needed? <!-- Please clarify why the changes are needed. For instance, 1. If you propose a new API, clarify the use case for a new API. 2. If you fix a bug, you can clarify why it is a bug. --> If column names are not specified, `drop` should not fail due to ambiguity since it should still be able to apply `drop` to the eligible columns. ### Does this PR introduce any user-facing change? <!-- If yes, please clarify the previous behavior and the change this PR proposes - provide the console output, description and/or an example to show the behavior difference if possible. If no, write 'No'. --> Yes, now all the rows with nulls are dropped in the above example: ``` scala> df.na.drop("any").show +----+----+----+ |col1|col2|col2| +----+----+----+ +----+----+----+ ``` ### How was this patch tested? <!-- If tests were added, say they were added here. Please make sure to add some test cases that check the changes thoroughly including negative and positive cases if possible. If it was tested in a way different from regular unit tests, please clarify how you tested step by step, ideally copy and paste-able, so that other reviewers can test and check, and descendants can verify in the future. If tests were not added, please describe why they were not added and/or why it was difficult to add. --> Added new unit tests.
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