• spark使用udf给dataFrame新增列


    spark 中给 dataframe 增加一列的方法一般使用 withColumn

    // 新建一个dataFrame
    val sparkconf = new SparkConf()
      .setMaster("local")
      .setAppName("test")
    val spark = SparkSession.builder().config(sparkconf).getOrCreate()
    val tempDataFrame = spark.createDataFrame(Seq(
      (1, "asf"),
      (2, "2143"),
      (3, "rfds")
    )).toDF("id", "content")
    // 增加一列
    val addColDataframe = tempDataFrame.withColumn("col", tempDataFrame("id")*0)
    addColDataframe.show(10,false)
    

    打印结果如下:

    +---+-------+---+
    |id |content|col|
    +---+-------+---+
    |1  |asf    |0  |
    |2  |2143   |0  |
    |3  |rfds   |0  |
    +---+-------+---+
    

    可以看到 withColumn 很依赖原来 dataFrame 的结构,但是假设没有 id 这一列,那么增加列的时候灵活度就降低了很多,假设原始 dataFrame 如下:

    +---+-------+
    | id|content|
    +---+-------+
    |  a|    asf|
    |  b|   2143|
    |  b|   rfds|
    +---+-------+
    

    这样可以用 udf 写自定义函数进行增加列:

    import org.apache.spark.sql.functions.udf
    // 新建一个dataFrame
    val sparkconf = new SparkConf()
      .setMaster("local")
      .setAppName("test")
    val spark = SparkSession.builder().config(sparkconf).getOrCreate()
    val tempDataFrame = spark.createDataFrame(Seq(
      ("a, "asf"),
      ("b, "2143"),
      ("c, "rfds")
    )).toDF("id", "content")
    // 自定义udf的函数
    val code = (arg: String) => {
          if (arg.getClass.getName == "java.lang.String") 1 else 0
        }
    
    val addCol = udf(code)
    // 增加一列
    val addColDataframe = tempDataFrame.withColumn("col", addCol(tempDataFrame("id")))
    addColDataframe.show(10, false)
    

    得到结果:

    +---+-------+---+
    |id |content|col|
    +---+-------+---+
    |a  |asf    |1  |
    |b  |2143   |1  |
    |c  |rfds   |1  |
    +---+-------+---+
    

    还可以写下更多的逻辑判断:

    // 新建一个dataFrame
    val sparkconf = new SparkConf()
      .setMaster("local")
      .setAppName("test")
    val spark = SparkSession.builder().config(sparkconf).getOrCreate()
    val tempDataFrame = spark.createDataFrame(Seq(
      (1, "asf"),
      (2, "2143"),
      (3, "rfds")
    )).toDF("id", "content")
    
    val code :(Int => String) = (arg: Int) => {if (arg < 2) "little" else "big"}
    val addCol = udf(code)
    val addColDataframe = tempDataFrame.withColumn("col", addCol(tempDataFrame("id")))
    addColDataframe.show(10, false)
    
    +---+-------+------+
    |1  |asf    |little|
    |2  |2143   |big   |
    |3  |rfds   |big   |
    +---+-------+------+
    

    传入多个参数:

    val sparkconf = new SparkConf()
      .setMaster("local")
      .setAppName("test")
    val spark = SparkSession.builder().config(sparkconf).getOrCreate()
    val tempDataFrame = spark.createDataFrame(Seq(
      ("1", "2"),
      ("2", "3"),
      ("3", "1")
    )).toDF("content1", "content2")
    
    val code = (arg1: String, arg2: String) => {
      Try(if (arg1.toInt > arg2.toInt) "arg1>arg2" else "arg1<=arg2").getOrElse("error")
    }
    val compareUdf = udf(code)
    
    val addColDataframe = tempDataFrame.withColumn("compare", compareUdf(tempDataFrame("content1"),tempDataFrame("content2")))
    addColDataframe.show(10, false)
    
    +--------+--------+----------+
    |content1|content2|compare   |
    +--------+--------+----------+
    |1       |2       |arg1<=arg2|
    |2       |3       |arg1<=arg2|
    |3       |1       |arg1>arg2 |
    +--------+--------+----------+
    
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  • 原文地址:https://www.cnblogs.com/TTyb/p/7169148.html
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