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https://issues.apache.org/jira/browse/SPARK-32431?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17164499#comment-17164499
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Maxim Gekk commented on SPARK-32431:
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I am working on this.
> The .schema() API behaves incorrectly for nested schemas that have column
> duplicates in case-insensitive mode
> -------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-32431
> URL: https://issues.apache.org/jira/browse/SPARK-32431
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.4.6, 3.0.0
> Reporter: Michał Świtakowski
> Priority: Major
>
> The code below throws org.apache.spark.sql.AnalysisException: Found duplicate
> column(s) in the data schema: `camelcase`; for multiple file formats due to a
> duplicate column in the requested schema.
> {code:java}
> import org.apache.spark.sql.types._
> spark.conf.set("spark.sql.caseSensitive", "false")
> val formats = Seq("parquet", "orc", "avro", "json")
> val caseInsensitiveSchema = new StructType().add("LowerCase",
> LongType).add("camelcase", LongType).add("CamelCase", LongType)
> formats.map{ format =>
> val path = s"/tmp/$format"
> spark
> .range(1L)
> .selectExpr("id AS lowercase", "id + 1 AS camelCase")
> .write.mode("overwrite").format(format).save(path)
> spark.read.schema(caseInsensitiveSchema).format(format).load(path).show
> }
> {code}
> Similar code with nested schema behaves inconsistently across file formats
> and sometimes returns incorrect results:
> {code:java}
> import org.apache.spark.sql.types._
> spark.conf.set("spark.sql.caseSensitive", "false")val formats =
> Seq("parquet", "orc", "avro", "json")val caseInsensitiveSchema = new
> StructType().add("StructColumn", new StructType().add("LowerCase",
> LongType).add("camelcase", LongType).add("CamelCase", LongType))formats.map{
> format =>
> val path = s"/tmp/$format"
> spark
> .range(1L)
> .selectExpr("NAMED_STRUCT('lowercase', id, 'camelCase', id + 1) AS
> StructColumn")
> .write.mode("overwrite").format(format).save(path)
>
> spark.read.schema(caseInsensitiveSchema).format(format).load(path).show
> }
> {code}
> The desired behavior likely should be returning an exception just like in the
> flat schema scenario.
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