Exception in thread “main“ java.lang.UnsatisfiedLinkError: org.apache.hadoop.io.nativeio.NativeIO$PO-Toy模板网

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Exception in thread “main” java.lang.UnsatisfiedLinkError: org.apache.hadoop.io.nativeio.NativeIO $POS I X . s t a t (L ja v a / l an g / St r in g;) L or g / a p a c h e / ha d oo p / i o / na t i v e i o / N a t i v e I O$ POSIX$Stat;

报错如下图所示
Exception in thread “main“ java.lang.UnsatisfiedLinkError: org.apache.hadoop.io.nativeio.NativeIO$PO,mapreduce,hadoop,hadoop,java
报错原因：就是你导入的依赖的hadoop版本和你本地window的hadoop版本不匹配的而导致的，解法方法就是把导入的依赖的版本换成和window版本一样的就可以了。
解决方法如下：
之前导入的依赖是：

<?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>Mapreduce</artifactId>
    <version>1.0-SNAPSHOT</version>

    <properties>
        <!--设置项目的编码为UTF-8-->
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <!--使用java8进行编码-->

        <maven.compiler.source>8</maven.compiler.source>
        <maven.compiler.target>8</maven.compiler.target>
        <!--设置hadoop的版本-->
        <hadoop.version>3.1.2</hadoop.version>

    </properties>
    <!--jar包的依赖-->
    <dependencies>
        <!--测试的依赖坐标-->
        <dependency>
            <groupId>junit</groupId>
            <artifactId>junit</artifactId>
            <version>4.11</version>
        </dependency>
        <!--日志打印的依赖坐标-->
        <dependency>
            <groupId>org.apache.logging.log4j</groupId>
            <artifactId>log4j-core</artifactId>
            <version>2.8.2</version>
        </dependency>
        <!--hadoop的通用模块的依赖坐标-->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-common</artifactId>
            <version>${hadoop.version}</version>
        </dependency>
        <!--hadoop的对HDFS分布式文件系统访问的技术支持的依赖坐标-->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-hdfs</artifactId>
            <version>${hadoop.version}</version>
        </dependency>
        <!--hadoop的客户端访问的依赖坐标-->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>${hadoop.version}</version>
        </dependency>
    </dependencies>


</project>

也就是说我之前导入的依赖版本是3.1.2,和我虚拟机上面是一样的，而我自己本地的是：
Exception in thread “main“ java.lang.UnsatisfiedLinkError: org.apache.hadoop.io.nativeio.NativeIO$PO,mapreduce,hadoop,hadoop,java

所有就把导入的依赖换成了本地有的3.0.0

<?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>java</artifactId>
    <version>1.0-SNAPSHOT</version>

    <properties>
        <maven.compiler.source>8</maven.compiler.source>
        <maven.compiler.target>8</maven.compiler.target>
        <hadoop.version>3.0.0</hadoop.version>
    </properties>

    <dependencies>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>3.0.0</version>
        </dependency>
        <dependency>
            <groupId>junit</groupId>
            <artifactId>junit</artifactId>
            <version>4.12</version>
        </dependency>
        <dependency>
            <groupId>org.slf4j</groupId>
            <artifactId>slf4j-log4j12</artifactId>
            <version>1.7.30</version>
        </dependency>
        <!--hadoop的通用模块的依赖坐标-->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-common</artifactId>
            <version>${hadoop.version}</version>
        </dependency>
        <!--hadoop的对HDFS分布式文件系统访问的技术支持的依赖坐标-->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-hdfs</artifactId>
            <version>${hadoop.version}</version>
        </dependency>
        <!--hadoop的客户端访问的依赖坐标-->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>${hadoop.version}</version>
        </dependency>
    </dependencies>


</project>

然后重写运行mapreduce经典案例倒排索引
代码如下：

package com.atguigu.mapreduce.mapreducedemo;
import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.FileSplit;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class InvertedIndex {

    public static class Map extends Mapper<Object, Text, Text, Text> {
        private Text keyInfo = new Text(); // 存储单词和URL组合
        private Text valueInfo = new Text(); // 存储词频
        private FileSplit split; // 存储Split对象
        // 实现map函数
        public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
            // 获得<key,value>对所属的FileSplit对象
            split = (FileSplit) context.getInputSplit();
            StringTokenizer itr = new StringTokenizer(value.toString());
            while (itr.hasMoreTokens()) {
                // key值由单词和URL组成，如"MapReduce：file1.txt"
                // 获取文件的完整路径
                // keyInfo.set(itr.nextToken()+":"+split.getPath().toString());
                // 这里为了好看，只获取文件的名称。
                int splitIndex = split.getPath().toString().indexOf("file");
                keyInfo.set(itr.nextToken() + ":" + split.getPath().toString().substring(splitIndex));
                // 词频初始化为1
                valueInfo.set("1");
                context.write(keyInfo, valueInfo);
            }
        }
    }

    public static class Combine extends Reducer<Text, Text, Text, Text> {
        private Text info = new Text();
        // 实现reduce函数
        public void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
            // 统计词频
            int sum = 0;
            for (Text value : values) {
                sum += Integer.parseInt(value.toString());
            }
            int splitIndex = key.toString().indexOf(":");
            // 重新设置value值由URL和词频组成
            info.set(key.toString().substring(splitIndex + 1) + ":" + sum);
            // 重新设置key值为单词
            key.set(key.toString().substring(0, splitIndex));
            context.write(key, info);
        }
    }

    public static class Reduce extends Reducer<Text, Text, Text, Text> {
        private Text result = new Text();
        // 实现reduce函数
        public void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
            // 生成文档列表
            String fileList = new String();
            for (Text value : values) {
                fileList += value.toString() + ";";
            }
            result.set(fileList);
            context.write(key, result);
        }
    }

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();


        Job job = Job.getInstance(conf);
        job.setJarByClass(InvertedIndex.class);

        // 设置Map、Combine和Reduce处理类
        job.setMapperClass(Map.class);
        job.setCombinerClass(Combine.class);
        job.setReducerClass(Reduce.class);

        // 设置Map输出类型
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(Text.class);

        // 设置Reduce输出类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);

        // 设置输入和输出目录
        FileInputFormat.addInputPath(job, new Path("D:\\input"));
        FileOutputFormat.setOutputPath(job, new Path("D:\\output"));
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

运行结果：

log4j:WARN No appenders could be found for logger (org.apache.hadoop.metrics2.lib.MutableMetricsFactory).
log4j:WARN Please initialize the log4j system properly.
log4j:WARN See http://logging.apache.org/log4j/1.2/faq.html#noconfig for more info.
Process finished with exit code 0文章来源地址https://www.toymoban.com/news/detail-661506.html

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