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Spark文档阅读之二:Programming Guides - Quick Start

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Quick Start: https://spark.apache.org/docs/latest/quick-start.html

 

在Spark 2.0之前,Spark的编程接口为RDD (Resilient Distributed Dataset)。而在2.0之后,RDDs被Dataset替代。Dataset很像RDD,但是有更多优化。RDD仍然支持,不过强烈建议切换到Dataset,以获得更好的性能。 RDD文档:https://spark.apache.org/docs/latest/rdd-programming-guide.html Dataset文档:https://spark.apache.org/docs/latest/sql-programming-guide.html  

一、最简单的Spark Shell交互分析

scala> val textFile = spark.read.textFile("README.md")   # 构建一个Dataset
textFile: org.apache.spark.sql.Dataset[String] = [value: string]

scala> textFile.count()  # Dataset的简单计算
res0: Long = 104 

scala> val linesWithSpark = textFile.filter(line => line.contain("Spark"))  # 由现有Dataset生成新Dataset
res1: org.apache.spark.sql.Dataset[String] = [value: string]
# 等价于:
# res1 = new Dataset()
# for line in textFile:
#     if line.contain("Spark"):
#         res1.append(line)
# linesWithSpark = res1

scala> linesWithSpark.count()
res2: Long = 19

# 可以将多个操作串行起来
scala> textFile.filter(line => line.contain("Spark")).count()
res3: Long = 19

 

进一步的Dataset分析:

scala> textFile.map(line => line.split(" ").size).reduce((a,b) => if (a > b) a else b)
res12: Int = 16
# 其实map和reduce就是两个普通的算子,不要被MapReduce中一个map配一个reduce、先map后reduce的思想所束缚
# map算子就是对Dataset的元素X计算fun(X),并且将所有f(X)作为新的Dataset返回
# reduce算子其实就是通过两两计算fun(X,Y)=Z,将Dataset中的所有元素归约为1个值

# 也可以引入库进行计算
scala> import java.lang.Math
import java.lang.Math

scala> textFile.map(line => line.split(" ").size).reduce((a, b) => Math.max(a, b))
res14: Int = 16

# 还可以使用其他算子
scala> val wordCounts = textFile.flatMap(line => line.split(" ")).groupByKey(identity).count()

# flatMap算子也是对Dataset的每个元素X执行fun(X)=Y,只不过map的res是
#     res.append(Y),如[[Y11, Y12], [Y21, Y22]],结果按元素区分
# 而flatMap是
#     res += Y,如[Y11, Y12, Y21, Y22],各元素结果合在一起

# groupByKey算子将Dataset的元素X作为参数传入进行计算f(X),并以f(X)作为key进行分组,返回值为KeyValueGroupedDataset类型
# 形式类似于(key: k; value: X1, X2, ...),不过KeyValueGroupedDataset不是一个Dataset,value列表也不是一个array
# 注意:这里的textFile和textFile.flatMap都是Dataset,不是RDD,groupByKey()中可以传func;如果以sc.textFile()方法读文件,得到的是RDD,groupByKey()中间不能传func

# identity就是函数 x => x,即返回自身的函数

# KeyValueGroupedDataset的count()方法返回(key, len(value))列表,结果是Dataset类型

scala> wordCounts.collect()
res37: Array[(String, Long)] = Array((online,1), (graphs,1), ...
# collect操作:将分布式存储在集群上的RDD/Dataset中的所有数据都获取到driver端

 

数据的cache:

scala> linesWithSpark.cache()  # in-memory cache,让数据在分布式内存中缓存
res38: linesWithSpark.type = [value: string]

scala> linesWithSpark.count()
res41: Long = 19

 

二、最简单的独立Spark任务(spark-submit提交)

需提前安装sbt,sbt是scala的编译工具(Scala Build Tool),类似java的maven。 brew install sbt   1)编写SimpleApp.scala
import org.apache.spark.sql.SparkSession

object SimpleApp {
    def main(args: Array[String]) {
        val logFile = "/Users/dxm/work-space/spark-2.4.5-bin-hadoop2.7/README.md"
        val spark = SparkSession.builder.appName("Simple Application").getOrCreate()
        val logData = spark.read.textFile(logFile).cache()
        val numAs = logData.filter(line => line.contains("a")).count()  # 包含字母a的行数
        val numBs = logData.filter(line => line.contains("b")).count()  # 包含字母b的行数
        println(s"Lines with a: $numAs, Lines with b: $numBs")
        spark.stop()
    }
}

 

2)编写sbt依赖文件build.sbt

name := "Simple Application"

version := "1.0"

scalaVersion := "2.12.10"

libraryDependencies += "org.apache.spark" %% "spark-sql" % "2.4.5"

 

其中,"org.apache.spark" %% "spark-sql" % "2.4.5"这类库名可以在网上查到,例如https://mvnrepository.com/artifact/org.apache.spark/spark-sql_2.10/1.0.0

 

3)使用sbt打包 目录格式如下,如果SimpleApp.scala和build.sbt放在一个目录下会编不出来
$ find .
.
./build.sbt
./src
./src/main
./src/main/scala
./src/main/scala/SimpleApp.scala

 

sbt目录格式要求见官方文档 https://www.scala-sbt.org/1.x/docs/Directories.html

src/
  main/
    resources/
       <files to include in main jar here>
    scala/
       <main Scala sources>
    scala-2.12/
       <main Scala 2.12 specific sources>
    java/
       <main Java sources>
  test/
    resources
       <files to include in test jar here>
    scala/
       <test Scala sources>
    scala-2.12/
       <test Scala 2.12 specific sources>
    java/
       <test Java sources>

 

使用sbt打包

# 打包
$ sbt package
...
[success] Total time: 97 s (01:37), completed 2020-6-10 10:28:24
# jar包位于 target/scala-2.12/simple-application_2.12-1.0.jar

 

4)提交并执行Spark任务

$ bin/spark-submit --class "SimpleApp" --master spark://xxx:7077 ../scala-tests/SimpleApp/target/scala-2.12/simple-application_2.12-1.0.jar
# 报错:Caused by: java.lang.ClassNotFoundException: scala.runtime.LambdaDeserialize
# 参考:https://stackoverflow.com/questions/47172122/classnotfoundexception-scala-runtime-lambdadeserialize-when-spark-submit
# 这是spark版本和scala版本不匹配导致的

 

查询spark所使用的scala的版本

$ bin/spark-shell --master spark://xxx:7077

scala> util.Properties.versionString
res0: String = version 2.11.12

 

修改build.sbt: scalaVersion := "2.11.12" 从下载页也可验证,下载的spark 2.4.5使用的是scala 2.11  

 

重新sbt package,产出位置变更为target/scala-2.11/simple-application_2.11-1.0.jar 再次spark-submit,成功

 

$ bin/spark-submit --class "SimpleApp" --master spark://xxx:7077 ../scala-tests/SimpleApp/target/scala-2.11/simple-application_2.11-1.0.jar 
Lines with a: 61, Lines with b: 30

 

标签:sbt,scala,Programming,Dataset,Start,Quick,spark,textFile,line
来源: https://www.cnblogs.com/desertfish/p/13137492.html