1. case class学习

样例类模式匹配

1.1 样例类（case class）适合用于不可变的数据。它是一种特殊的类，能够被优化以用于模式匹配。

case class MetaData(userId: String)case class Book(name: String) {def printBookName(): Unit = {println(name)}
}object BookTest {def main(args: Array[String]): Unit = {val book = Book("Java入门到放弃")book.printBookName()}
}

在实例化case class类时，不需要使用关键字New，case class类编译成class文件之后会自动生成apply方法，这个方法负责对象的创建。

1.2 模式匹配

一个模式匹配语句包括一个待匹配的值，match关键字，以及至少一个case语句。示例如下：

def matchTest(x: Int): String = x match {case 1 => "one"case 2 => "two"case _ => "many"}

1.3 case class的匹配

abstract class Notification # 抽象类，虚基类，父类case class Email(sender: String, title: String, body: String) extends Notification
case class SMS(caller: String, message: String) extends Notification
case class VoiceRecording(contactName: String, link: String) extends Notification

Notification 是一个虚基类，它有三个具体的子类Email, SMS和VoiceRecording，我们可以在这些Case Class类上使用模式匹配：

def showNotification(notification: Notification): String = {notification match {case Email(email, title, _) =>s"You got an email from $email with title: $title"case SMS(number, message) =>s"You got an SMS from $number! Message: $message"case VoiceRecording(name, link) =>s"you received a Voice Recording from $name! Link: $link"}
}val someSms = SMS("12345", "Are you there?")
val someVoiceRecording = VoiceRecording("Tom", "voicerecording.org/id/123")println(showNotification(someSms))  // prints You got an SMS from 12345! Message: Are you there?
println(showNotification(someVoiceRecording))  // you received a Voice Recording from Tom! Click the link to hear it: voicerecording.org/id/123

2. 类转json

scala自带的Json解析工具，简单使用，参考Scala json字符串和json对象互相转换

import org.json4s.jackson.{JsonMethods, Serialization}case class User(name: String, age: Int)object Json4STest {def main(args: Array[String]): Unit = {//数据格式：user:{"name": "zs","age": 10}val json = "{\"name\": \"zs\",\"age\": 10}"//导隐式函数implicit val f = org.json4s.DefaultFormats/** ************************* json字符串->对象 ***************************************/val user: User = JsonMethods.parse(json).extract[User]println(user)/** ************************* 对象->json字符串 ***************************************/val userJStr: String = Serialization.write(user)println(userJStr)}
}

3. Array 和 List

val listTest = List(1,2,3,4)val arrayTest = Array(1, 2, 3, 4) //正确
for (i <- arrayTest.indices){println(arrayTest(i))
}scala> 1 until 10 by 3
res17: scala.collection.immutable.Range = Range(1, 4, 7)scala> 1 to 10 by 3
res18: scala.collection.immutable.Range = Range(1, 4, 7, 10)scala> arrayTest.indices
res19: scala.collection.immutable.Range = Range(0, 1, 2, 3)

4. Scala常用函数式编程

4.1 map

• map方法可以将某个函数应用到集合中的每个元素并产出其结果的集合，比如

scala> val names = List("a", "b", "c")
names: List[String] = List(a, b, c)scala> names.map(_.toUpperCase)
res1: List[String] = List(A, B, C)

4.2 foreach

• foreach和map相似，只不过它没有返回值，foreach只要是为了对参数进行作用。

scala> names.foreach{name => println(name)}
a
b
c

4.3 flatten

• flatten可以把嵌套的结构展开.

scala> List(List(1,2), List(3,4)).flatten
res3: List[Int] = List(1, 2, 3, 4)

4.4 flatmap

• flatMap结合了map和flatten的功能。接收一个可以处理嵌套列表的函数，然后把返回结果连接起来。

scala> List(List(1,2), List(3,4)).flatMap(x => x.map(x => x *2))
res4: List[Int] = List(2, 4, 6, 8)

4.5 filter

• 滤除掉使函数返回false的元素

scala> def isEven(i:Int):Boolean = i %2 == 0
isEven: (i: Int)Booleanscala> List(1,2,3,4,5,6).filter(isEven _)
res5: List[Int] = List(2, 4, 6)

4.6 zip

• zip方法将两个集合结合在一起

scala> List("a","b", "c").zip(List(1,2,3))
res6: List[(String, Int)] = List((a,1), (b,2), (c,3))		

4.7 zipWithIndex

• 将元素和下标结合在一起

scala> List(2,3,4,5).zipWithIndex
res8: List[(Int, Int)] = List((2,0), (3,1), (4,2), (5,3))

4.8 sort

函数学习

• 在scala集合中，可以使用以下几种方式来进行排序
  • sorted默认排序
  • sortBy指定字段排序
  • sortWith自定义排序

//定义一个List集合
scala> val list=List(5,1,2,4,3)
list: List[Int] = List(5, 1, 2, 4, 3)//默认就是升序
scala> list.sorted
res25: List[Int] = List(1, 2, 3, 4, 5)

//定义一个List集合
scala> val list=List("1 hadoop","2 spark","3 flink")
list: List[String] = List(1 hadoop, 2 spark, 3 flink)//按照单词的首字母进行排序
scala> list.sortBy(x=>x.split(" ")(1))
res33: List[String] = List(3 flink, 1 hadoop, 2 spark)

scala> val list = List(2,3,1,6,4,5)
a: List[Int] = List(2, 3, 1, 6, 4, 5)//降序
scala> list.sortWith((x,y)=>x>y)
res35: List[Int] = List(6, 5, 4, 3, 2, 1)//升序
scala> list.sortWith((x,y)=>x<y)
res36: List[Int] = List(1, 2, 3, 4, 5, 6)

4.9 分组 - groupBy

scala> val a = List("张三"->"男", "李四"->"女", "王五"->"男")
a: List[(String, String)] = List((张三,男), (李四,女), (王五,男))// 按照性别分组
scala> a.groupBy(_._2)
res0: scala.collection.immutable.Map[String,List[(String, String)]] = Map(男 -> List((张三,男), (王五,男)),
女 -> List((李四,女)))// 将分组后的映射转换为性别/人数元组列表
scala> res0.map(x => x._1 -> x._2.size)
res3: scala.collection.immutable.Map[String,Int] = Map(男 -> 2, 女 -> 1)

4.10 聚合 - reduce

• reduce表示将列表，传入一个函数进行聚合计算

scala> val a = List(1,2,3,4,5,6,7,8,9,10)
a: List[Int] = List(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)scala> a.reduce((x,y) => x + y)
res5: Int = 55// 第一个下划线表示第一个参数，就是历史的聚合数据结果
// 第二个下划线表示第二个参数，就是当前要聚合的数据元素
scala> a.reduce(_ + _)
res53: Int = 55// 与reduce一样，从左往右计算
scala> a.reduceLeft(_ + _)
res0: Int = 55// 从右往左聚合计算
scala> a.reduceRight(_ + _)
res1: Int = 55

4.11 折叠 - fold

• fold与reduce很像，但是多了一个指定初始值参数

//定义一个List集合
scala> val a = List(1,2,3,4,5,6,7,8,9,10)
a: List[Int] = List(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)//求和
scala> a.sum
res41: Int = 55//给定一个初始值，，折叠求和
scala> a.fold(0)(_+_)
res42: Int = 55scala> a.fold(10)(_+_)
res43: Int = 65//从左往右
scala> a.foldLeft(10)(_+_)
res44: Int = 65//从右往左
scala> a.foldRight(10)(_+_)
res45: Int = 65//fold和foldLet效果一致，表示从左往右计算
//foldRight表示从右往左计算

4.12 高阶函数

• 使用函数值作为参数，或者返回值为函数值的“函数”和“方法”，均称之为“高阶函数”

4.12.1 函数值作为参数

//定义一个数组
scala> val array=Array(1,2,3,4,5)
array: Array[Int] = Array(1, 2, 3, 4, 5)//定义一个函数
scala> val func=(x:Int)=>x*10
func: Int => Int = <function1>//函数作为参数传递到方法中
scala> array.map(func)
res0: Array[Int] = Array(10, 20, 30, 40, 50)

4.12.2 匿名函数

//定义一个数组
scala> val array=Array(1,2,3,4,5)
array: Array[Int] = Array(1, 2, 3, 4, 5)//定义一个没有名称的函数----匿名函数
scala> array.map(x=>x*10)
res1: Array[Int] = Array(10, 20, 30, 40, 50)

5. spark程序

5.1 创建sparkSession

val spark = SparkSession
.builder()
.master("local")
.appName("test")
.config("spark.sql.execution.arrow.enabled", "true")
.getOrCreate()spark.sql("select 1 as num").show()

• master: 设置运行方式：local代表本机单核运行，**local[4]代表在本机用4核跑，spark://master:7077**是以standalone方式运行；
• appName: 设置spark程序的名字，可以在web UI界面看到；
• config: 附加配置项；
• getOrCreate： 创建一个SparkSession。

5.2 读取和保存本地excel文件

<!--excel依赖-->
<dependency><groupId>com.crealytics</groupId><artifactId>spark-excel_2.11</artifactId><version>0.13.1</version>
</dependency>

fake_data.xlsx

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读excel，字段取值都是string类型

val dataPath = "C:\\Users\\86183\\Desktop\\教程\\fake_data.xlsx"
//读excel
val df = spark.read.format("com.crealytics.spark.excel")
.option("header", "true").load(dataPath)df.show(false)println(df.dtypes.mkString("Array(", ", ", ")"))val saveDataPath = "C:\\Users\\86183\\Desktop\\教程\\fake_data_1.xlsx"
//写excel
df.coalesce(1).write
.format("com.crealytics.spark.excel")
.option("dataAddress", "A1")
.option("header", "true")
.mode("overwrite")
.save(saveDataPath)

5.3 dataFrame注册hive表

df.createTempView("temp_table")
val sqlString =
"""
|select
|*
|from temp_table
|""".stripMarginspark.sql(sqlString).show(false)

5.4 聚合函数groupBy用法

5.4.1 基础用法

import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions._object GroupbyExample extends App {val spark: SparkSession = SparkSession.builder().master("local[1]").appName("SparkByExamples.com").getOrCreate()spark.sparkContext.setLogLevel("ERROR")import spark.implicits._val simpleData = Seq(("James","Sales","NY",90000,34,10000),("Michael","Sales","NY",86000,56,20000),("Robert","Sales","CA",81000,30,23000),("Maria","Finance","CA",90000,24,23000),("Raman","Finance","CA",99000,40,24000),("Scott","Finance","NY",83000,36,19000),("Jen","Finance","NY",79000,53,15000),("Jeff","Marketing","CA",80000,25,18000),("Kumar","Marketing","NY",91000,50,21000))val df = simpleData.toDF("employee_name","department","state","salary","age","bonus")df.show()//Group By on single columndf.groupBy("department").count().show(false)df.groupBy("department").avg("salary").show(false)df.groupBy("department").sum("salary").show(false)df.groupBy("department").min("salary").show(false)df.groupBy("department").max("salary").show(false)df.groupBy("department").mean("salary").show(false)//GroupBy on multiple columnsdf.groupBy("department","state").sum("salary","bonus").show(false)df.groupBy("department","state").avg("salary","bonus").show(false)df.groupBy("department","state").max("salary","bonus").show(false)df.groupBy("department","state").min("salary","bonus").show(false)df.groupBy("department","state").mean("salary","bonus").show(false)//Running Filterdf.groupBy("department","state").sum("salary","bonus").show(false)//using agg functiondf.groupBy("department").agg(sum("salary").as("sum_salary"),avg("salary").as("avg_salary"),sum("bonus").as("sum_bonus"),max("bonus").as("max_bonus")).show(false)df.groupBy("department").agg(sum("salary").as("sum_salary"),avg("salary").as("avg_salary"),sum("bonus").as("sum_bonus"),stddev("bonus").as("stddev_bonus")).where(col("sum_bonus") > 50000).show(false)
}

# 使用多个聚合函数前，先导入相关的包 "import org.apache.spark.sql.functions._"
import org.apache.spark.sql.functions._
df
.groupBy("first_class_id")
.agg(count("first_class_id").as("count_first_class_id"),sum("exposure_cnt").as("sum_exposure_cnt")).show()

选择多列

val groupByList = Array("item_id", "first_class_id")
# : _* 表示讲列名传入select
df.select(groupByList.map(col): _*).show
df.groupBy(groupByList.map(col): _*).agg(sum("exposure_cnt").as("sum_exposure_cnt")).show()# agg方法
var map = scala.collection.immutable.Map.empty[String, String]
for (func <- aggFunc) {for (colName <- calculateCol) {map += (colName -> func)}
}
df.groupBy(groupByList.map(col): _*).agg(map).show(false)

Spark 指南：Spark SQL（二）—— 结构化操作

Spark中选取DataFrame多列的三种方式

SparkSQL 高级篇(一) 聚合操作

Spark：group by和聚合函数使用

【Saprk】Spark DataFrame 列的类型转换

Spark 指南：Spark SQL（三）—— 结构化类型

Explain different ways of groupBy() in spark SQL

Spark读取本地文件和HDFS文件

spark读取excel成dataframe的几种方式

package com.huawei.rcm.studyabstract class Notificationcase class Email(sender: String, title: String, body: String) extends Notificationcase class SMS(caller: String, message: String) extends Notificationcase class VoiceRecording(contactName: String, link: String) extends Notificationobject demo {def matchTest(x: Int): String = x match {case 1 => "one"case 2 => "two"case _ => "many"}def showNotification(notification: Notification): String = {notification match {case Email(email, title, _) =>s"You got an email from $email with title: $title"case SMS(number, message) =>s"You got an SMS from $number! Message: $message"case VoiceRecording(name, link) =>s"you received a Voice Recording from $name! Link: $link"}}def main(args: Array[String]): Unit = {val someSms = SMS("12345", "Are you there?")val someVoiceRecording = VoiceRecording("Tom", "voicerecording.org/id/123")println(showNotification(someSms)) // prints You got an SMS from 12345! Message: Are you there?println(showNotification(someVoiceRecording)) // you received a Voice Recording from Tom! Click the link to hear it: voicerecording.org/id/123println(matchTest(1))}
}import org.apache.spark.sql.SparkSession
import org.json4s.jackson.{JsonMethods, Serialization}case class User(name: String, age: Int)object Json4STest {def main(args: Array[String]): Unit = {//数据格式：user:{"name": "zs","age": 10}val json = "{\"name\": \"zs\",\"age\": 10}"//导隐式函数implicit val f = org.json4s.DefaultFormats/** ************************* json字符串->对象 ************************************** */val user: User = JsonMethods.parse(json).extract[User]println(user)/** ************************* 对象->json字符串 ************************************** */val userJStr: String = Serialization.write(user)println(userJStr)val strings = new Array[String](3)val arrayTest = Array(1, 2, 3, 4) //正确val listTest = List(1, 2, 3, 4)for (i <- listTest.indices) {println(listTest(i))}// sort//    val list=List(5,1,2,4,3)//    list.sortedval list = List("1 hadoop", "2 spark", "3 flink")list.sortBy(x => x.split(" ")(1))// groupBy//    arrayTest.map()//    val a = List("张三"->"男", "李四"->"女", "王五"->"男")//    a.groupBy(x=>x._2)//    arrayTest.groupBy()//    arrayTest.sorted//    val a = List(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)//    a.reduce((x, y) => x + y)//    a.reduceLeft(_+_)val a = List(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)a.fold(0)(_ + _)val spark = SparkSession.builder().master("local").appName("test").config("spark.sql.execution.arrow.enabled", "true").getOrCreate()spark.sql("select 1 as num").show()val dataPath = "C:\\Users\\86183\\Desktop\\教程\\fake_data.xlsx"//读excelval df = spark.read.format("com.crealytics.spark.excel").option("header", "true").load(dataPath)df.show(false)println(df.dtypes.mkString("Array(", ", ", ")"))val saveDataPath = "C:\\Users\\86183\\Desktop\\教程\\fake_data_1.xlsx"//写exceldf.coalesce(1).write.format("com.crealytics.spark.excel").option("dataAddress", "A1").option("header", "true").mode("overwrite").save(saveDataPath)df.createTempView("temp_table")val sqlString ="""|select|*|from temp_table|""".stripMarginspark.sql(sqlString).show(false)import org.apache.spark.sql.functions._df.groupBy("first_class_id").agg(count("first_class_id").as("count_first_class_id"), sum("exposure_cnt").as("sum_exposure_cnt")).show()val groupByList = Array("item_id", "first_class_id")val aggFunc = Array("avg", "max")val calculateCol = Array("ctr", "exposure_cnt")//    val columns = df.columnsimport org.apache.spark.sql.{Column, DataFrame}import org.apache.spark.sql.functions.colval columns: Array[String] = df.columnsval arrayColumn: Array[Column] = columns.map(column => col(column))val df2: DataFrame = df.select(arrayColumn: _*)df.select(groupByList.map(col): _*).showdf.groupBy(groupByList.map(col): _*).agg(sum("exposure_cnt").as("sum_exposure_cnt")).show()df.groupBy(groupByList.map(col): _*).count().show()var map = scala.collection.immutable.Map.empty[String, String]for (func <- aggFunc) {for (colName <- calculateCol) {map += (colName -> func)}}df.groupBy(groupByList.map(col): _*).agg(map).show(false)}
}

6. spark ML之特征处理

spark ML之特征处理

1、VectorIndexer

根据源码注释，VectorIndexer是用于在“向量”的数据集中索引分类特征列的类（Class for indexing categorical feature columns in a dataset of Vector），这看起来不太好理解，直接看用法，举例说明就好了。

1.1 数据

我们用普通的数据格式即可：

data1.txt

1,-1.0 1.0
0,0.0 3.0
1,-1.0 5.0
0,0.0 1.0

其中第一列为label,后面的为features

spark读取数据程序（供参考）：

import spark.implicits._
val data_path = "files/ml/featureprocessing/data1.txt"
val data = spark.read.text(data_path).map {case Row(line: String) =>var arr = line.split(',')(arr(0), Vectors.dense(arr(1).split(' ').map(_.toDouble)))
}.toDF("label", "features")
data.show(false)

7. Spark运行日志去除——log4j.properties

log4j.rootLogger=ERROR,stdout
log4j.appender.stdout=org.apache.log4j.ConsoleAppender
log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
log4j.appender.stdout.layout.ConversionPattern=%d %p [%-20c] - %m%n
log4j.appender.logfile=org.apache.log4j.FileAppender
log4j.appender.logfile.File=target/spring.log
log4j.appender.logfile.layout=org.apache.log4j.PatternLayout
log4j.appender.logfile.layout.ConversionPattern=%d %p [%c] - %m%n

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spark.read.text(data_path).map {
case Row(line: String) =>
var arr = line.split(‘,’)
(arr(0), Vectors.dense(arr(1).split(’ ').map(_.toDouble)))
}.toDF(“label”, “features”)
data.show(false)

# 7. Spark运行日志去除——log4j.properties```properties
log4j.rootLogger=ERROR,stdout
log4j.appender.stdout=org.apache.log4j.ConsoleAppender
log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
log4j.appender.stdout.layout.ConversionPattern=%d %p [%-20c] - %m%n
log4j.appender.logfile=org.apache.log4j.FileAppender
log4j.appender.logfile.File=target/spring.log
log4j.appender.logfile.layout=org.apache.log4j.PatternLayout
log4j.appender.logfile.layout.ConversionPattern=%d %p [%c] - %m%n