sparkSQL------读取Hive数据、UDF和UDAF自定义函数、SparkSQL开窗函数

1.读取Hive中的数据加载成Dataframe

    
      org.apache.spark
      spark-hive_2.11
      2.3.1
    

object sparkSQL06 {
  def main(args: Array[String]): Unit = {
    val session: SparkSession = SparkSession.builder().appName("test06").enableHiveSupport().getOrCreate()
    session.sql("use spark")
    //创建表
    session.sql("create table person (id int,name String,age int) row format delimited fields terminated by 't'")
    //加载数据
    val frame: Dataframe = session.sql("load data local inpath '/root/test/student_infos' into tables student_infos")
    //将结果写入hive表中
    val df: Dataframe = session.sql("select * from student_infos")
    df.write.mode(SaveMode.Overwrite).saveAsTable("good_student_infos")
  }
}

2.UDF自定义函数

object sparkSQL07 {
  def main(args: Array[String]): Unit = {
    val session: SparkSession = SparkSession.builder().master("local").appName("test07").getOrCreate()
    val nameList = List[String]("zjc", "lff", "ly")
    import session.implicits._
    val df = nameList.toDF();
    df.createTempView("temp")
    
    session.udf.register("STRLEN",(name:String)=>{name.length})
    session.sql("select name,STRLEN(name) from temp").show()
  }
}

3.UDAF函数

object sparkSQL08 {
  def main(args: Array[String]): Unit = {
    val session: SparkSession = SparkSession.builder().master("local").appName("test08").getOrCreate()
    val nameList=List[String]("zjc","lff","ergou")
    import session.implicits._
    val df = nameList.toDF("name")
    df.createTempView("mytable")
    session.udf.register("MyCount",new MyCount)
    session.sql("select name,MyCount(name) from mytable").show()
  }
}

//自定义聚合函数
class MyCount extends UserDefinedAggregateFunction{

  //输入数据的类型
  override def inputSchema: StructType = StructType(List[StructField](StructField("xx",StringType,true)))

  //在聚合过程中处理的数据类型
  override def bufferSchema: StructType = StructType(List[StructField](StructField("xx",StringType,true)))

  //最终返回值的类型，与evaluate返回的值一致
  override def dataType: DataType = IntegerType

  //多次运行数据是否一致
  override def deterministic: Boolean = true

  //每个分区中每组key对应的初始值
  override def initialize(buffer: MutableAggregationBuffer): Unit = buffer.update(0,0)

  //每个分区中，每个分组内进行聚合操作
  override def update(buffer: MutableAggregationBuffer, input: Row): Unit = {buffer.update(0,buffer.getInt(0)+1)}

  //不同的分区中相同的key的数据进行聚合
  override def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit =  {buffer1.update(0,buffer1.getInt(0)+buffer2.getInt(0))}

  //聚合之后，每个分组最终返回的值,类型要和dataType 一致
  override def evaluate(buffer: Row): Any = buffer.getInt(0)
}

4.SparkSQL开窗函数

row_number() over (partition by XXX order by XXX) 同个分组内生成连续的序号，每个分组内从1开始且排序相同的数据会标不同的号。
rank() over (partition by XXX order by XXX) 同个分组内生成不连续的序号，在每个分组内从1开始，同个分组内相同数据标号相同。
dense_rank() over (partitin by XXX order by XXX) 同个分组内生成连续的序号，在每个分组内从1开始，同个分组内相同数据标号相同，之后的数据标号连续。

mport org.apache.spark.sql.SparkSession


object sparkSQL09 {
  def main(args: Array[String]): Unit = {
    val session: SparkSession = SparkSession.builder().appName("test09").enableHiveSupport().getOrCreate()
    session.sql("use spark")
    session.sql("create table if not exists sales (riqi string,leibie string,jine Int) " +
      	"row format delimited fields terminated by 't'")
    session.sql("load data local inpath '/root/test/sales' into table sales")
    session.sql("select * from " +
      "(select * row_number() over (partition by riqi order by riqi desc)").show()
  }
}