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https://issues.apache.org/jira/browse/SPARK-7301?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15065021#comment-15065021
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Simeon Simeonov commented on SPARK-7301:
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It would be nice if using backticks to refer to the column resolved the
ambiguity, e.g., {{select `A` as upperA, `a` as lowerA from test}}.
> Issue with duplicated fields in interpreted json schemas
> --------------------------------------------------------
>
> Key: SPARK-7301
> URL: https://issues.apache.org/jira/browse/SPARK-7301
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Reporter: David Crossland
>
> I have a large json dataset that has evolved over time as such some fields
> seem to have slight renames or have been capitalised in some way. This means
> there are certain fields that spark considers ambiguous when i attempt to
> access them
> i get a
> org.apache.spark.sql.AnalysisException: Ambiguous reference to fields
> StructField(Currency,StringType,true), StructField(currency,StringType,true);
> error
> There appears to be no way to resolve an ambiguous field after its been
> inferred by spark sql other than to manually construct the schema using
> StructType/StructField which is a bit heavy handed as the schema is quite
> large. Is there some way to resolve an ambiguous reference? or affect the
> schema post inference? It seems like something of a bug that i cant tell
> spark to treat both fields as though they were the same. Ive created a test
> where i manually defined a schema as
> val schema = StructType(Seq(StructField("A", StringType, true)))
> And it returns 2 rows when i perform a count on the following dataset
> {"A":"test1"}
> {"a":"test2"}
> If i could modify the schema to remove the duplicate entries then i could
> work around this issue.
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