windows下本地部署安装hadoop+scala+spark-【不需要虚拟机】

注意版本依赖【本实验版本如下】

Hadoop 3.1.1 
spark 2.3.2 
scala 2.11

1.依赖环境

1.1 java

安装java并配置环境变量【如果未安装搜索其他教程】

环境验证如下：

C:\Users\wangning>java -version
java version "1.8.0_261"
Java(TM) SE Runtime Environment (build 1.8.0_261-b12)
Java HotSpot(TM) 64-Bit Server VM (build 25.261-b12, mixed mode)

1.2 hadoop安装

下载地址：https://hadoop.apache.org/releases.html
本案例下载：hadoop-3.1.1.tar.gz 或者直接访问：
https://hadoop.apache.org/release/3.1.1.html

1.2.1 hadoop安装

环境变量新增：HADOOP_HOME 值，本地安装目录（根据实际更改）D:\apps\hadoop-3.3.6

path增加%HADOOP_HOME%\bin 和 %HADOOP_HOME%\sbin

验证hadoop是否安装好：

C:\Users\wangning>hadoop version
Hadoop 3.1.1
Source code repository https://github.com/apache/hadoop -r 2b9a8c1d3a2caf1e733d57f346af3ff0d5ba529c
Compiled by leftnoteasy on 2018-08-02T04:26Z
Compiled with protoc 2.5.0
From source with checksum f76ac55e5b5ff0382a9f7df36a3ca5a0
This command was run using /D:/apps/hadoop-3.1.1/share/hadoop/common/hadoop-common-3.1.1.jar

1.2.2 修改hadoop配置文件

修改hadoop的配置文件，这些配置文件决定了hadoop是否能正常启动

配置文件的位置：在%HADOOP_HOME%\etc\hadoop\

core-site.xml, -- 是全局配置

hdfs-site.xml, --hdfs的局部配置。

mapred-site.xml -- mapred的局部配置。

a：在coresite.xml下的配置：

添加

<configuration><property><name>fs.defaultFS</name><value>hdfs://localhost:9000</value></property>
</configuration>

b: hdfs文件都可以建立在本地监听的这个服务下

在hdfs-site.xml下的配置:

添加

<configuration><property><name>dfs.replication</name><value>1</value></property><property><name>dfs.namenode.name.dir</name><value>/D:/apps/hadoop-3.1.1/data/namenode</value> </property><property><name>dfs.datanode.data.dir</name><value>/D:/apps/hadoop-3.1.1/data/datanode</value> </property></configuration>

在Hadoop3.1.1的安装目录下新建data文件夹，再data下，新建namenode和datanode 文件夹，

yarn-site.xml下的配置:

<configuration><!-- Site specific YARN configuration properties --><property><name>yarn.nodemanager.aux-services</name><value>mapreduce_shuffle</value></property>
</configuration>

mapred-site.xml文件下的配置：

<configuration><property><name>mapreduce.framework.name</name><value>yarn</value></property>
</configuration>

1.2.3 配置文件下载

下载的hadoop安装包默认是在linux环境下运行的，如果需要在windows中启动，需要额外增加两个步骤

a、下载对应版本的bin文件包，替换本机hadoop安装目录下的bin包

https://github.com/cdarlint/winutils

b、将对应版本bin包中的hadoop.dll这个文件放在本机的C:\Windows\System32下

step4： 启动hadoop

进入sbin目录中，用 管理员模式启动cmd：

先初始化NameNode：hdfs namenode -format

再运行start-dfs.cmd，

再运行start-yarn.cmd 

运行完上述命令，会出现2*2个窗口，如果没有报错继续，如果报错根据错误定位原因。

在cmd中输入jps，如果返回如下几个进程，就说明启动成功了

1.2.4 访问验证

http://localhost:8088 ——查看应用管理界面ResourceManager

http://localhost:9870 ——NameNode界面

1.3 Spark安装

spark下载路径:[根据自己的版本进行下载]
https://archive.apache.org/dist/spark/spark-2.3.2/
下载对应的预编译文件：[spark-2.3.2-bin-hadoop2.7.tgz]下载后解压到路径，配置环境变量：
SPARK_HOME 变量值：Spark 的解压目录，例如 C:\Spark
编辑 Path，添加：%SPARK_HOME%\bin验证 Spark：[cmd下执行：spark-shell]
C:\Users\wangning>spark-shell
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Spark context Web UI available at http://DESKTOP-8B1BDRS.mshome.net:4040
Spark context available as 'sc' (master = local[*], app id = local-1737362793261).
Spark session available as 'spark'.
Welcome to____              __/ __/__  ___ _____/ /___\ \/ _ \/ _ `/ __/  '_//___/ .__/\_,_/_/ /_/\_\   version 2.3.2/_/Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_261)
Type in expressions to have them evaluated.
Type :help for more information

ui页面验证：http://localhost:4040

1.4 Scala安装

下载scala
https://www.scala-lang.org/download/2.11.0.html

下载后执行安装，比如安装目录为：D:\apps\scala-2.11.0

配置环境变量：

SCALA_HOME

配置完执行验证

C:\Users\wangning>scala -version
Scala code runner version 2.11.0 -- Copyright 2002-2013, LAMP/EPFLC:\Users\wangning>scala
Welcome to Scala version 2.11.0 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_261).
Type in expressions to have them evaluated.
Type :help for more information.scala> print("hello scala")
hello scala
scala>

2. 创建scala项目

增加scala插件

2.1 项目初始化

对应的pom.xml文件如下：

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"><modelVersion>4.0.0</modelVersion><groupId>org.example</groupId><artifactId>untitled</artifactId><version>1.0-SNAPSHOT</version><properties><maven.compiler.source>8</maven.compiler.source><maven.compiler.target>8</maven.compiler.target><project.build.sourceEncoding>UTF-8</project.build.sourceEncoding><spark.version>2.3.2</spark.version><scala.version>2.11</scala.version></properties><dependencies><dependency><groupId>org.scala-lang</groupId><artifactId>scala-library</artifactId><version>2.11.0</version></dependency><dependency><groupId>org.scala-lang</groupId><artifactId>scala-compiler</artifactId><version>2.11.0</version></dependency><dependency><groupId>junit</groupId><artifactId>junit</artifactId><version>4.4</version><scope>test</scope></dependency><dependency><groupId>org.specs</groupId><artifactId>specs</artifactId><version>1.2.5</version><scope>test</scope></dependency><dependency><groupId>org.apache.spark</groupId><artifactId>spark-core_${scala.version}</artifactId><version>${spark.version}</version></dependency><dependency><groupId>org.apache.spark</groupId><artifactId>spark-streaming_${scala.version}</artifactId><version>${spark.version}</version></dependency><dependency><groupId>org.apache.spark</groupId><artifactId>spark-sql_${scala.version}</artifactId><version>${spark.version}</version></dependency><dependency><groupId>org.apache.spark</groupId><artifactId>spark-hive_${scala.version}</artifactId><version>${spark.version}</version></dependency><dependency><groupId>org.apache.spark</groupId><artifactId>spark-mllib_${scala.version}</artifactId><version>${spark.version}</version></dependency></dependencies></project>

2.2 coding

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._object WordCount_local {def main(args: Array[String]) {//    if (args.length < 1) {//      System.err.println("Usage: <file>")//      System.exit(1)//    }val conf = new SparkConf().setMaster("local").setAppName("HuiTest")  //本地调试需要//    val conf = new SparkConf()       //onlineval sc = new SparkContext(conf)//    val line = sc.textFile(args(0))  //online
//    val line = sc.textFile("hdfs://localhost:9000/user/words.txt")  //本地调试val line = sc.textFile("file:///D:/file/words.txt")line.flatMap(_.split(" ")).map((_, 1)).reduceByKey(_+_).collect().foreach(println)sc.stop()}
}

2.3 打包

1. File->Project Structure

注意接下来删除除了jar包和compile output之外的所有jar，否则执行阶段会报错

执行相关操作：

C:\Windows\system32>hdfs dfs -ls hdfs://localhost:9000/C:\Windows\system32>hdfs dfs -mkdir hdfs://localhost:9000/user/C:\Windows\system32>hdfs dfs -ls hdfs://localhost:9000/
Found 1 items
drwxr-xr-x   - wangning supergroup          0 2025-01-22 18:09 hdfs://localhost:9000/userC:\Windows\system32>hdfs dfs -put D:/file/words.txt hdfs://localhost:9000/user/words.txt
put: `/file/words.txt': No such file or directoryC:\Windows\system32>hdfs dfs -put file:///D:/file/words.txt hdfs://localhost:9000/user/words.txtC:\Windows\system32>
C:\Windows\system32>hdfs dfs -cat hdfs://localhost:9000/user/words.txt
hello
hello spark
hello redis
hello flink
hello doris
C:\Windows\system32>

2.4 执行验证

cmd下执行：

# 查看编译是否成功jar tf D:\code\testcode\t6\out\artifacts\untitled_jar\untitled.jar | findstr "WordCount_local"# 运行代码
spark-submit --master local --name huihui --class WordCount_local D:\code\testcode\t6\out\artifacts\untitled_jar\untitled.jar

查看运行结果如下：