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https://issues.apache.org/jira/browse/SPARK-17041?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15419158#comment-15419158
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Barry Becker commented on SPARK-17041:
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I'm not sure either. How can we find out? I think it would be better if columns
were case sensitive. Was the change intentional? This was a dataset from a
client, and for whatever reason, they thought it was reasonable to have columns
that varied only by case. Since it was something that worked before, I thought
it might be considered a regression, but maybe it should be a feature request.
In our product, someone may rename a column from "output" to "Output". If the
column names are not case sensitive, I'm not sure what problems this might
cause. At a minimum, the rename will probably not work.
> Columns in schema are no longer case sensitive when reading csv file
> --------------------------------------------------------------------
>
> Key: SPARK-17041
> URL: https://issues.apache.org/jira/browse/SPARK-17041
> Project: Spark
> Issue Type: Bug
> Components: Input/Output
> Affects Versions: 2.0.0
> Reporter: Barry Becker
>
> It used to be (in spark 1.6.2) that I could read a csv file that had columns
> with names that differed only by case. For example, one column may be
> "output" and another called "Output". Now (with spark 2.0.0) if I try to read
> such a file, I get an error like this:
> {code}
> org.apache.spark.sql.AnalysisException: Reference 'Output' is ambiguous,
> could be: Output#1263, Output#1295.;
> {code}
> The schema (dfSchema below) that I pass to the csv read looks like this:
> {code}
> StructType( StructField(Output,StringType,true), ...
> StructField(output,StringType,true), ...)
> {code}
> The code that does the read is this
> {code}
> sqlContext.read
> .format("csv")
> .option("header", "false") // Use first line of all files as header
> .option("inferSchema", "false") // Automatically infer data types
> .schema(dfSchema)
> .csv(dataFile)
> {code}
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