[GitHub] spark pull request #16680: [WIP][SPARK-16101][SQL] Refactoring CSV schema in...

[HyukjinKwon <gi...@git.apache.org>]

Github user HyukjinKwon commented on a diff in the pull request:

    https://github.com/apache/spark/pull/16680#discussion_r97706016
  
    --- Diff: sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/csv/CSVInferSchema.scala ---
    @@ -39,22 +37,76 @@ private[csv] object CSVInferSchema {
        *     3. Replace any null types with string type
        */
       def infer(
    -      tokenRdd: RDD[Array[String]],
    -      header: Array[String],
    +      csv: Dataset[String],
    +      caseSensitive: Boolean,
           options: CSVOptions): StructType = {
    -    val startType: Array[DataType] = Array.fill[DataType](header.length)(NullType)
    -    val rootTypes: Array[DataType] =
    -      tokenRdd.aggregate(startType)(inferRowType(options), mergeRowTypes)
    -
    -    val structFields = header.zip(rootTypes).map { case (thisHeader, rootType) =>
    -      val dType = rootType match {
    -        case _: NullType => StringType
    -        case other => other
    +    val firstLine: String = CSVUtils.filterCommentAndEmpty(csv, options).first()
    --- End diff --
    
    Both `CSVUtils.filterCommentAndEmpty` usages here and below should exactly the same up to my knowledge but I let them as are just simply to keep the behaviour for now.


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