Spark SQL

Spark SQL

目录

• Spark SQL
  • DataFrame
  • DataSet
  • 使用IDEA来创建项目
  • 用户自定义函数
  • 用户自定义聚合函数(弱类型)
  • 用户自定义聚合函数(强类型)

DataFrame

创建DataFrame

scala> spark
res0: org.apache.spark.sql.SparkSession = org.apache.spark.sql.SparkSession@4e8598d9

查看spark数据源进行创建的文件格式

scala> spark.read.
csv   format   jdbc   json   load   option   options   orc   parquet   schema   table   text   textFile

读取json文件创建DataFrame

scala> val df = spark.read.json("hdfs://192.168.83.101:9000/datas/2.json")
res2: org.apache.spark.sql.DataFrame = [age: bigint, name: string]

展示结果

scala> df.show()
+---+--------+
|age|    name|
+---+--------+
| 20|zhangsan|
| 20|    lisi|
| 20|  wangwu|
+---+--------+

创建临时视图表 (view只能读不能改)

scala> df.createTempView("emp")

查询临时视图表

scala> spark.sql("select * from emp").show
+---+--------+
|age|    name|
+---+--------+
| 20|zhangsan|
| 20|    lisi|
| 20|  wangwu|
+---+--------+

临时表是Session范围内的 ，Session退出后，表就失效了，如果想应用范围内有效，使用全局表
全局表需要全路径访问，如:global_temp.people

DSL语法风格

scala> df.printSchema
root
 |-- age: long (nullable = true)
 |-- name: string (nullable = true)

查看name列数据

scala> df.select("name").show()
+--------+
|    name|
+--------+
|zhangsan|
|    lisi|
|  wangwu|
+--------+

查看name列数据以及age+1数据

scala> df.select($"name",$"age" + 1).show()
+--------+---------+
|    name|(age + 1)|
+--------+---------+
|zhangsan|       21|
|    lisi|       21|
|  wangwu|       21|
+--------+---------+

查看age小于21的数据

scala> df.filter($"age" < 21).show()
+---+--------+
|age|    name|
+---+--------+
| 20|zhangsan|
| 20|    lisi|
| 20|  wangwu|
+---+--------+

按照年龄分组，查看数据条目

scala> df.groupBy("age").count().show()
+---+-----+
|age|count|
+---+-----+
| 21|    2|
| 20|    2|
+---+-----+

RDD转换为DataFrame

scala> val rdd = sc.makeRDD(List(1,2,3,4))
rdd: org.apache.spark.rdd.RDD[Int] = ParallelCollectionRDD[40] at makeRDD at <console>:24

scala> val df = rdd.toDF("id")
df: org.apache.spark.sql.DataFrame = [id: int]

scala> df.show()
+---+
| id|
+---+
|  1|
|  2|
|  3|
|  4|
+---+

一条RDD转换为DataFrame

scala> val rdd = sc.makeRDD(List((1,"zhangsan",20),(2,"lisi",30),(3,"wangwu",40)))
rdd: org.apache.spark.rdd.RDD[(Int, String, Int)] = ParallelCollectionRDD[45] at makeRDD at <console>:24

scala> val df = rdd.toDF("id","name","age")
df: org.apache.spark.sql.DataFrame = [id: int, name: string ... 1 more field]

scala> df.show()
+---+--------+---+
| id|    name|age|
+---+--------+---+
|  1|zhangsan| 20|
|  2|    lisi| 30|
|  3|  wangwu| 40|
+---+--------+---+

通过样例类来创建表

scala> case class People(name:String,age:Int)
defined class People

scala> val rdd = sc.makeRDD(List(("zhangsan",20),("lisi",30),("wangwu",40)))
rdd: org.apache.spark.rdd.RDD[(String, Int)] = ParallelCollectionRDD[55] at makeRDD at <console>:24

scala> val peopleRDD = rdd.map(t=>{People(t._1,t._2)})
peopleRDD: org.apache.spark.rdd.RDD[People] = MapPartitionsRDD[56] at map at <console>:28

scala> peopleRDD.toDF
res25: org.apache.spark.sql.DataFrame = [name: string, age: int]

DataFrame转换为RDD

直接调用rdd即可

scala> df.rdd
res27: org.apache.spark.rdd.RDD[org.apache.spark.sql.Row] = MapPartitionsRDD[64] at rdd at <console>:29

DataSet

先创建一个样例类

scala> case class Person(name:String,age:Long)
defined class Person

创建DataSet

scala> val caseClassDS = Seq(Person("Andy",32)).toDS()
caseClassDS: org.apache.spark.sql.Dataset[Person] = [name: string, age: bigint]

访问Dataset (和DataFrame大同小异)

scala> caseClassDS.show()
+----+---+
|name|age|
+----+---+
|Andy| 32|
+----+---+

RDD转换为DataSet

scala> case class Person(name:String,age:Long)
defined class Person

scala> val mapRDD = rdd.map(t=>{Person(t._1,t._2)})
mapRDD: org.apache.spark.rdd.RDD[Person] = MapPartitionsRDD[65] at map at <console>:28

scala> mapRDD.toDS
res30: org.apache.spark.sql.Dataset[Person] = [name: string, age: bigint]

scala> mapRDD.toDS.show()
+--------+---+
|    name|age|
+--------+---+
|zhangsan| 20|
|    lisi| 30|
|  wangwu| 40|
+--------+---+

DataSet转换为RDD

scala> val ds = mapRDD.toDS
ds: org.apache.spark.sql.Dataset[Person] = [name: string, age: bigint]

scala> ds.rdd
res32: org.apache.spark.rdd.RDD[Person] = MapPartitionsRDD[73] at rdd at <console>:33

DataFrame转换为DataSet

scala> rdd.collect
res33: Array[(String, Int)] = Array((zhangsan,20), (lisi,30), (wangwu,40))

scala> val df = rdd.toDF("name","age")
df: org.apache.spark.sql.DataFrame = [name: string, age: int]

scala> df.as[Person]
res34: org.apache.spark.sql.Dataset[Person] = [name: string, age: int]

DataSet转换为DataFrame

scala> val ds = df.as[Person]
ds: org.apache.spark.sql.Dataset[Person] = [name: string, age: int]

scala> ds.toDF
res35: org.apache.spark.sql.DataFrame = [name: string, age: int]

使用IDEA来创建项目

package com.bigdata.sparksql

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.SparkSession

object SparkSQL01_Demo {
  def main(args: Array[String]): Unit = {
    //TODO 创建配置对象
    val sparkConf = new SparkConf().setMaster("local[*]").setAppName("Demo")
    val sc = new SparkContext(sparkConf)
    //TODO 创建SparkSQL的环境对象
    val spark = SparkSession.builder().config(sparkConf).config("spark.sql.warehouse.dir", "C:/Users/RenZetong/Desktop/桌面备份/SparkSQLDev/spark-warehouse").getOrCreate()
    //TODO 构建DataFrame
    val frame = spark.read.json("input/2.json")
    frame.show()
    spark.stop()
  }
}

构建DataFrame

package com.bigdata.sparksql

import org.apache.spark.sql.SparkSession
import org.apache.spark.{SparkConf, SparkContext}

object SparkSQL02_SQL {
  def main(args: Array[String]): Unit = {
    //TODO 创建配置对象
    val sparkConf = new SparkConf().setMaster("local[*]").setAppName("Demo")
    val sc = new SparkContext(sparkConf)
    //TODO 创建SparkSQL的环境对象
    val spark = SparkSession.builder().config(sparkConf).config("spark.sql.warehouse.dir", "C:/Users/RenZetong/Desktop/桌面备份/SparkSQLDev/spark-warehouse").getOrCreate()
    //TODO 构建DataFrame
    val frame = spark.read.json("input/2.json")
    //将frame转换为表
    frame.createTempView("user")
    //采用SQL语法进行访问
    spark.sql("select * from user").show()
    spark.stop()
  }
}

类型相互转换

package com.bigdata.sparksql

import org.apache.spark.sql.SparkSession
import org.apache.spark.{SparkConf, SparkContext}

object SparkSQL03_Transform {
  def main(args: Array[String]): Unit = {
    //TODO 创建配置对象
    val sparkConf = new SparkConf().setMaster("local[*]").setAppName("Demo")
    val sc = new SparkContext(sparkConf)
    //TODO 创建SparkSQL的环境对象
    val spark = SparkSession.builder().config(sparkConf).config("spark.sql.warehouse.dir", "C:/Users/RenZetong/Desktop/桌面备份/SparkSQLDev/spark-warehouse").getOrCreate()

    //转换之前，需要引入隐式转换规则
    import spark.implicits._

    //创建RDD
    val rdd = spark.sparkContext.makeRDD(List((1,"张三",22),(2,"李四",20),(3,"王五",30)))
    //转换为DF
    val df = rdd.toDF("id","name","age")
    //转换为DS
    val ds = df.as[User]
    //转换为DF
    val df1 = ds.toDF()
    //转换为RDD
    val rdd1 = df1.rdd

    rdd1.foreach(
      row=>{
        println(row.getInt(0) + "," + row.getString(1) + "," + row.getInt(2))
      }
    )
  }
}
//TODO 增加一个样例类来辅助表结构
case class User(id:Int,name:String,age:Int)

用户自定义函数

在Sehll窗口中可以通过spark.udf功能用户可以自定义函数

scala> var df = spark.read.json("hdfs://192.168.83.101:9000/datas/2.json")
df: org.apache.spark.sql.DataFrame = [age: bigint, name: string]

scala> spark.udf.register("addName",(x:String)=>"Name:" + x)
res1: org.apache.spark.sql.expressions.UserDefinedFunction = UserDefinedFunction(<function1>,StringType,Some(List(StringType)))

scala> df.createTempView
   def createTempView(viewName: String): Unit

scala> df.createTempView("user")
+---+--------+
|age|    name|
+---+--------+
| 20|zhangsan|
| 20|    lisi|
| 21|  wangwu|
| 21|   liliu|
| 18|   liyue|
+---+--------+

scala> spark.sql("select * from user").show()

scala> spark.sql("select addName(name)  from user").show()
+-------------+
|    UDF(name)|
+-------------+
|Name:zhangsan|
|    Name:lisi|
|  Name:wangwu|
|   Name:liliu|
|   Name:liyue|
+-------------+

用户自定义聚合函数(弱类型)

强类型的Dataset和弱类型的DataFrame都提供了相关的聚合函数，如count() countDistinct() avg() max() min() 。除此之外，用户可以设定自己的定义聚合函数

弱类型用户自定义聚合函数：通过继承UserDefinedAggregateFunction来实现用户自定义聚合函数

package com.bigdata.sparksql

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.sql.{Row, SparkSession}
import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction}
import org.apache.spark.sql.types.{DataType, DoubleType, LongType, StructType}

object SparkSQL01_Demo {
  def main(args: Array[String]): Unit = {
    //TODO 创建配置对象
    val sparkConf = new SparkConf().setMaster("local[*]").setAppName("Demo")
    val sc = new SparkContext(sparkConf)
    //TODO 创建SparkSQL的环境对象
    val spark = SparkSession.builder().config(sparkConf).config("spark.sql.warehouse.dir", "C:/Users/RenZetong/Desktop/桌面备份/SparkSQLDev/spark-warehouse").getOrCreate()
    //TODO 自定义聚合函数
    val udaf = new MyAgeAvgFunction
    spark.udf.register("avgAge", udaf)

    //使用聚合函数
    val frame = spark.read.json("input/2.json")

    frame.createTempView("user")
    spark.sql("select avgAge(age) from user").show()

    spark.stop()
  }
}

//TODO 声明用户自定义聚合函数_弱类型
//1、继承UserDefinedAggregateFunction
//2、实现方法
class MyAgeAvgFunction extends UserDefinedAggregateFunction {

  //TODO 函数输入的数据结构
  override def inputSchema: StructType = {
    //当前输入的类型只有一个age字段，为Long类型
    new StructType().add("age", LongType)
  }

  //TODO 计算时的数据结构
  override def bufferSchema: StructType = {
    new StructType().add("sum", LongType).add("count", LongType)
  }

  //TODO 数据计算完毕之后的结构类型，函数返回的数据类型
  override def dataType: DataType = DoubleType

  //TODO 函数是否稳定,(传入的数据类型是否一支切稳定)
  override def deterministic: Boolean = true

  //TODO 计算之前的缓冲区的初始化
  override def initialize(buffer: MutableAggregationBuffer): Unit = {
    buffer(0) = 0L //代表第一个结构"sum"
    buffer(1) = 0L //代表第二个结构"count"
  }

  //TODO 根据查询结果来更新缓冲区数据
  override def update(buffer: MutableAggregationBuffer, input: Row): Unit = {
    buffer(0) = buffer.getLong(0) + input.getLong(0)
    buffer(1) = buffer.getLong(1) + 1
  }

  //TODO 将多个节点的缓冲区合并
  override def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit = {
    //new_sum = sum + sum
    buffer1(0) = buffer1.getLong(0) + buffer2.getLong(0)
    //new_count = count + count
    buffer1(1) = buffer1.getLong(1) + buffer2.getLong(1)
  }

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

用户自定义聚合函数(强类型)

package com.bigdata.sparksql

import org.apache.spark.sql.expressions.Aggregator
import org.apache.spark.sql.{Dataset, Encoder, Encoders, Row, SparkSession}
import org.apache.spark.{SparkConf, SparkContext}

object SparkSQL04_UDAF_Class {
  def main(args: Array[String]): Unit = {
    //TODO 创建配置对象
    val sparkConf = new SparkConf().setMaster("local[*]").setAppName("Demo")
    val sc = new SparkContext(sparkConf)
    //TODO 创建SparkSQL的环境对象
    val spark = SparkSession.builder().config(sparkConf).config("spark.sql.warehouse.dir", "C:/Users/RenZetong/Desktop/桌面备份/SparkSQLDev/spark-warehouse").getOrCreate()
    //转换之前，需要引入隐式转换规则
    import spark.implicits._

    //创建聚合函数对象
    val udaf = new MyAgeAvgClassFunction
    //将聚合函数转换为查询列
    val vagCol = udaf.toColumn.name("avgAge")

    val frame = spark.read.json("input/2.json")

    val userDS = frame.as[UserBean]

    //应用函数
    userDS.select(vagCol).show()

    spark.stop()
  }
}

case class UserBean(name: String, age: BigInt)

case class AvgBuffer(var sum: BigInt, var count: Int)

//TODO 声明用户自定义聚合函数_强类型
//1、继承Aggregator
//2、实现方法

class MyAgeAvgClassFunction extends Aggregator[UserBean, AvgBuffer, Double] {
  //TODO 初始化
  override def zero: AvgBuffer = {
    AvgBuffer(0, 0)
  }

  /**
   * 聚合数据
   */
  override def reduce(b: AvgBuffer, a: UserBean): AvgBuffer = {
    b.sum = b.sum + a.age
    b.count = b.count + 1
    b
  }

  //TODO 缓冲区合并
  override def merge(b1: AvgBuffer, b2: AvgBuffer): AvgBuffer = {
    b1.sum = b1.sum + b2.sum
    b1.count = b1.count + b2.count
    b1
  }

  //TODO 完成计算
  override def finish(reduction: AvgBuffer): Double = {
    reduction.sum.toDouble / reduction.count
  }

  //TODO 数据类型转码操作
  override def bufferEncoder: Encoder[AvgBuffer] = Encoders.product

  //TODO 数据类型转码操作
  override def outputEncoder: Encoder[Double] = Encoders.scalaDouble
}