SparkSql 自定义函数(看这一篇就够了~)

SparkSql 自定义函数(看这一篇就够了~),第1张

SparkSql 自定义函数(看这一篇就够了~) 简述:

开发过程中,有时候函数满足不了我们的需求,我们需要自己去定义函数使用。在spark中,有三种自定义函数,分别为UDF,UDAF,UDTF。
UDF:一对一
UDAF:多对一
UDTF:一对多

UDF函数实例:

hobbies.txt文件内容

alice jogging,Coding,cooking
lina travel,dance

需求:求出每个人hobbies的数量

*** 作代码:

	val conf: SparkConf = new SparkConf().setAppName("innserdemo").setMaster("local[*]")
    val spark: SparkSession = SparkSession.builder().config(conf).getOrCreate()
    val sc: SparkContext = spark.sparkContext
    import spark.implicits._
    //文件路径
    val hobbyDF: Dataframe = sc.textFile("in/hobbies.txt")
      .map(x => x.split(" "))
      .map(x => Hobbies(x(0), x(1))).toDF()
	hobbyDF.createOrReplaceTempView("hobby")
    spark.udf.register("hobby_num",(x:String)=>{x.split(",").size})
    import org.apache.spark.sql.functions
    val hobby_num: UserDefinedFunction = functions.udf((hobbies: String) => {
      hobbies.split(",").size
    })
    val newhobbyDF: Dataframe = hobbyDF.withColumn("hobbynum", hobby_num($"hobbies"))
    newhobbyDF.printSchema()
    newhobbyDF.show(false)

运行结果:

UDAF函数实例:

自定义函数UDAF 继承 UserDefinedAggregateFunction
需求:根据性别分组求平均年龄

*** 作代码:

	val conf: SparkConf = new SparkConf().setAppName("innserdemo").setMaster("local[*]")
    val spark: SparkSession = SparkSession.builder().config(conf).getOrCreate()
    val sc: SparkContext = spark.sparkContext
    val students: Seq[Student] = Seq(
      Student(1, "zhangsan", "F", 22),
      Student(2, "lisi", "M", 38),
      Student(3, "wangwu", "M", 13),
      Student(4, "zhaoliu", "F", 17),
      Student(5, "songba", "M", 32),
      Student(6, "sunjiu", "M", 16),
      Student(7, "qianshiyi", "F", 17),
      Student(8, "yinshier", "F", 15),
      Student(9, "fangshisan", "M", 12),
      Student(10, "yeshisan", "F", 11),
      Student(11, "ruishiyi", "F", 26),
      Student(12, "chenshier", "M", 28)
    )
    val frame: Dataframe = spark.createDataframe(students)
    frame.printSchema()
    //    import org.apache.spark.sql.functions._
    spark.udf.register("myAvg",new MyAgeAvgFunction)
    frame.createOrReplaceTempView("students")
    val resultDF: Dataframe = spark.sql(
      "select gender,myAvg(age) from students group by gender"
    )
    resultDF.printSchema()
    resultDF.show(false)

自定义函数MyAgeAvgFunction

class MyAgeAvgFunction extends UserDefinedAggregateFunction{

  //聚合函数的输入数据的数据结构
  override def inputSchema: StructType = {
//    new StructType().add("age",LongType)
    StructType(StructField("age",LongType) :: Nil)
  }

  //在缓冲区内的数据结构   ageSum(1000) ageNum(200)
  //sum 用来记录  所有年龄值相加的总和   43 + 52 + 61 + 78 = 234 => sum
  //count 用来记录相加的总和   1 + 1 + 1 + 1 = 4 => count
  override def bufferSchema: StructType = {
//      new StructType().add("sum",LongType).add("count",LongType)
    StructType(StructField("num",LongType) :: StructField("count",LongType) :: Nil)
  }

  //定义当前函数返回值的类型   sum/count
  override def dataType: DataType = DoubleType

  // 聚合函数幂等
  override def deterministic: Boolean = true

  override def initialize(buffer: MutableAggregationBuffer): Unit = {
    buffer(0)=0L //记录传入所有用户年龄相加的总和
    buffer(1)=0L //记录传入所有用户年龄的个数
  }

  //传入一条新数据后需要进行处理
  //将Row(63)对象中的值取出与buffer(0)相加
  //buffer(1)数据个数加1
  override def update(buffer: MutableAggregationBuffer, input: Row): Unit = {
    buffer(0) = buffer.getLong(0) + input.getLong(0)
    buffer(1) = buffer.getLong(1) + 1

  }

  //合并各分区内的数据
  //例如 p1(321,6) p2(128,2) p3(219,3)
  override def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit = {
    //计算年龄相加总和
    buffer1(0) = buffer1.getLong(0) + buffer2.getLong(0)
    //总人数
    buffer1(1) = buffer1.getLong(1) + buffer2.getLong(1)

  }

  //计算最终结果
  override def evaluate(buffer: Row): Any = {
    buffer.getLong(0)/buffer.getLong(1).toDouble
  }
}

运行结果:

UDTF函数实例:

自定义函数UDTF继承GenericUDTF
UDTF.txt文件内容

01//zs//Hadoop scala spark hive hbase
02//ls//Hadoop scala kafka hive hbase Oozie
03//ww//Hadoop scala spark hive sqoop

需求:求出某一位同学课程的信息

*** 作代码:

	val conf: SparkConf = new SparkConf().setAppName("UDTFDemo").setMaster("local[*]")
    val spark: SparkSession = SparkSession.builder()
      .config(conf)
      .config("hive.metastore.uris", "thrift://192.168.91.135:9083")
      .enableHiveSupport()
      .getOrCreate()
    val sc: SparkContext = spark.sparkContext
    import spark.implicits._
    val rdd: RDD[String] = sc.textFile("in/UDTF.txt")
    val rdd2: RDD[(String, String, String)] = rdd.map(x => {
      x.split("//")
    }).filter(x => x(1).equals("ls"))
      .map(x => (x(0), x(1), x(2)))
    val frame: Dataframe = rdd2.toDF("id", "name", "class")
    frame.createOrReplaceTempView("udtftable")
    spark.sql("create temporary function Myudtf as 'day12_13.MyUDTF'")
    spark.sql("select Myudtf(class) from udtftable").show(false)

自定义函数MyUDTF:

class MyUDTF extends GenericUDTF{

  

  override def process(objects: Array[AnyRef]): Unit = {
    val strings: Array[String] = objects(0).toString.split(" ")
    for(str<-strings){
      val temp = new Array[String](1)
      temp(0)=str
      forward(temp)
    }
  }

  override def close(): Unit = {

  }

  override def initialize(argOIs: Array[ObjectInspector]): StructObjectInspector = {
    val fieldName = new java.util.ArrayList[String]()
    val fieldOIS = new java.util.ArrayList[ObjectInspector]()
    //定义输出字段的类型
    fieldName.add("type")
    fieldOIS.add(PrimitiveObjectInspectorFactory.javaStringObjectInspector)
    ObjectInspectorFactory.getStandardStructObjectInspector(fieldName,fieldOIS)
  }

}

运行结果:

欢迎分享,转载请注明来源:内存溢出

原文地址: https://outofmemory.cn/zaji/5664778.html

(0)
打赏 微信扫一扫 微信扫一扫 支付宝扫一扫 支付宝扫一扫
上一篇 2022-12-16
下一篇 2022-12-16

发表评论

登录后才能评论

评论列表(0条)

保存