SparkSQL中，有时需要根据需求基于原数据新增一些列，下面介绍一下dataframe新增列的四种方法，根据情况可采用不同的方法实现需求。

package com.longi.bigdata.spark.etl

import com.longi.bigdata.spark.udf.DataFrameForCreatingNewColumnUDF
import com.longi.bigdata.spark.utils.SparkSessionCreate
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.{DataFrame, Row, SparkSession}
import org.apache.spark.sql.types.StringType
import org.apache.spark.sql.functions._


/**
  * Author: whn
  * Date: 2019-12-17 13:39
  * Version: 1.0
  * Function:  dataframe新增一列的四种方法
  */
object DataFrameForCreatingNewColumn {

  case class TestCase(a: String, b: String, c: Int)

  def main(args: Array[String]): Unit = {
    val spark = SparkSessionCreate.getSparkSession("DataFrameForCreatingNewColumn", "local[6]")
    spark.udf.register("add_col", DataFrameForCreatingNewColumnUDF.addCol _)
    import spark.implicits._
    val inputDF = spark.sparkContext.parallelize(Seq(
      ("WYC", "a", 81), ("WYC", "a", 77), ("WYC", "a", 81),
      ("WYC", "a", 66), ("WYC", "a", 99), ("WYC", "a", 22),
      ("WYC", "b", 92), ("WYC", "b", 92), ("WYC", "b", 92),
      ("CJJ", "b", 22), ("CJJ", "b", 33), ("CJJ", "b", 13),
      ("CJJ", "b", 66), ("CJJ", "b", 88), ("CJJ", "b", 11),
      ("CJJ", "b", 99), ("CJJ", "b", 99), ("CJJ", "b", 12),
      ("CJJ", "a", 66), ("CJJ", "a", 66), ("CJJ", "a", 33),
      ("CJJ", "a", 55), ("CJJ", "a", 99), ("CJJ", "a", 64),
      ("CJJ", "a", 66), ("CJJ", "a", 97), ("CJJ", "a", 66),
      ("WHN", "c", 32), ("WHN", "c", 26), ("WHN", "c", 84),
      ("WHN", "c", 88), ("WHN", "c", 88), ("WHN", "c", 34),
      ("WHN", "c", 88), ("WHN", "c", 77), ("WHN", "c", 88)
    )).map(xs => TestCase(xs._1, xs._2, xs._3)).toDF("name", "class", "scores")
    val res1 = createNewColumnByCreateDataFrameMethod(inputDF, spark)
    //    res1.show()
    val res2 = createNewColumnByUDF(inputDF, spark)
    //    res2.show()
    val res3 = createNewColumnBySQL(inputDF, spark)
    //    res3.show()
    val res4 = createNewColumnForIndex(inputDF, spark)
    res4.show()
  }

  // TODO 方法 1.通过createDataFrame方法新增一列
  def createNewColumnByCreateDataFrameMethod(inputDF: DataFrame, spark: SparkSession): DataFrame = {
    val newRdd: RDD[Row] = inputDF.rdd.map((row: Row) => {
      if (row.getInt(2) >= 60) Row(row.getString(0), row.getString(1), row.getInt(2), "及格")
      else Row(row.getString(0), row.getString(1), row.getInt(2), "不及格")
    })
    val schema = inputDF.schema.add("newCol", StringType, nullable = true)
    spark.createDataFrame(newRdd, schema)
  }

  // TODO 方法 2.通过udf新增一列
 //  -》udf通过 spark.udf.register("fun_name", func)方式注册的UDF函数可以用于df.selectExpr("fun_name(colName)")新增列
    -》udf通过  val addColUDF: UserDefinedFunction = udf(addCol _)方式注册的UDF函数可以用于withColumn方法新增列

  def createNewColumnByUDF(inputDF: DataFrame, spark: SparkSession): DataFrame = {
    // 用withColumn的方法，如果想要替换原数据中的某列， 则新名字与该列命名相同
    val newDF = inputDF.withColumn("newCol", DataFrameForCreatingNewColumnUDF.addColUDF(col("scores")))
   // val newDF2 = inputDF.selectExpr("name", "class", "scores", "add_col(scores) AS newCol")
    newDF2
  }

  // TODO 方法 3.通过sql新增一列
  def createNewColumnBySQL(inputDF: DataFrame, spark: SparkSession): DataFrame = {
    inputDF.createOrReplaceTempView("temp")
    val newDF = spark.sql(
      """
        |SELECT name, class, scores,
        | case when scores >= 60 then '及格'
        |      when scores < 60 then '不及格'
        |      else '不可能'
        | end AS newCol
        |FROM temp
        |
      """.stripMargin)
    newDF
  }

  // TODO 方法 4.新增序号列
  def createNewColumnForIndex(inputDF: DataFrame, spark: SparkSession): DataFrame = {
    val wd = Window.partitionBy("name","class").orderBy("scores")
    val newDF = inputDF.withColumn("index", row_number().over(wd))
//    val newDF1 = inputDF.withColumn("index", monotonically_increasing_id) // 单调递增序号
    newDF
  }


}