map-reduce之wordCount DEMO

小咪咪 2022-05-15 03:56 139阅读 0赞

<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>hadoop</groupId>
        <artifactId>demo</artifactId>
        <version>0.0.1-SNAPSHOT</version>
        <packaging>jar</packaging>
    
        <properties>
            <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        </properties>
    
        <dependencies>
            <dependency>
                <groupId>org.apache.hadoop</groupId>
                <artifactId>hadoop-common</artifactId>
                <version>2.6.0</version>
            </dependency>
    
            <dependency>
                <groupId>org.apache.hadoop</groupId>
                <artifactId>hadoop-hdfs</artifactId>
                <version>2.6.0</version>
            </dependency>
    
            <dependency>
                <groupId>jdk.tools</groupId>
                <artifactId>jdk.tools</artifactId>
                <version>1.6</version>
                <scope>system</scope>
                <systemPath>${JAVA_HOME}/lib/tools.jar</systemPath>
            </dependency>
    
            <dependency>
                <groupId>org.apache.hadoop</groupId>
                <artifactId>hadoop-mapreduce-client-core</artifactId>
                <version>2.6.0</version>
            </dependency>
    
            <dependency>
                <groupId>junit</groupId>
                <artifactId>junit</artifactId>
                <version>3.8.1</version>
                <scope>test</scope>
            </dependency>
        </dependencies>
    </project>

package hadoop;
    
    import org.apache.hadoop.conf.Configuration;
    import org.apache.hadoop.fs.FileSystem;
    import org.apache.hadoop.fs.Path;
    import org.apache.hadoop.io.IntWritable;
    import org.apache.hadoop.io.Text;
    import org.apache.hadoop.mapreduce.Job;
    import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
    import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
    
    public class WCMain {
    
        private static String iPath = "hdfs://localhost:9000/wordcount/input/test.txt";
        private static String oPath = "hdfs://localhost:9000/wordcount/output/";
    
        /**
         * 1. 业务逻辑相关信息通过job对象定义与实现 2. 将绑定好的job提交给集群去运行
         */
        public static void main(String[] args) throws Exception {
            Configuration conf = new Configuration();
            Job wcjob = Job.getInstance(conf);
    
            wcjob.setJarByClass(WCMain.class);
            wcjob.setMapperClass(WCMapper.class);
            wcjob.setReducerClass(WCReducer.class);
    
            // 设置业务逻辑Mapper类的输出key和value的数据类型
            wcjob.setMapOutputKeyClass(Text.class);
            wcjob.setMapOutputValueClass(IntWritable.class);
    
            // 设置业务逻辑Reducer类的输出key和value的数据类型
            wcjob.setOutputKeyClass(Text.class);
            wcjob.setOutputValueClass(IntWritable.class);
    
            // 指定要处理的数据所在的位置
            FileSystem fs = FileSystem.get(conf);
            Path IPath = new Path(iPath);
            if (fs.exists(IPath)) {
                FileInputFormat.addInputPath(wcjob, IPath);
            }
    
            // 指定处理完成之后的结果所保存的位置
            Path OPath = new Path(oPath);
            fs.delete(OPath, true);
            FileOutputFormat.setOutputPath(wcjob, OPath);
    
            // 向yarn集群提交这个job
            boolean res = wcjob.waitForCompletion(true);
            System.exit(res ? 0 : 1);
        }
    }

package hadoop;
    
    import java.io.IOException;
    import org.apache.hadoop.io.IntWritable;
    import org.apache.hadoop.io.Text;
    import org.apache.hadoop.mapreduce.Reducer;
    
    /**
     * @ClassName: WCReducer
     * @Description: TODO
     * @author kngines
     * @date 2018年3月17日
     */
    
    public class WCReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
    
        //  生命周期：框架每传递进来一个kv 组，reduce方法被调用一次
        @Override
        protected void reduce(Text key, Iterable<IntWritable> values, Context context)
                throws IOException, InterruptedException {
    
            int count = 0;  // 定义一个计数器
            for (IntWritable value : values) { // 遍历所有v，并累加到count中
                count += value.get();
            }
            context.write(key, new IntWritable(count));
        }
    }

package hadoop;
    
    import java.io.IOException;
    import org.apache.hadoop.io.IntWritable;
    import org.apache.hadoop.io.LongWritable;
    import org.apache.hadoop.io.Text;
    import org.apache.hadoop.mapreduce.Mapper;
    
    /**
     * @ClassName: WCMapper
     * @Description: TODO
     * @author kngines
     * @date 2018年3月17日
     */
    
    public class WCMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
    
        // map方法的生命周期： 框架每传一行数据就被调用一次
        @Override
        protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
    
            String line = value.toString();  // 行数据转换为string
            String[] words = line.split(" ");  // 行数据分隔单词
    
            for (String word : words) {  // 遍历数组，输出<单词，1>
                context.write(new Text(word), new IntWritable(1));
            }
        }
    }

问题总结
    问题 A ：
    FileAlreadyExistsException
    FileAlreadyExistsException: Output directory hdfs://localhost:9000/wordcount/output already exists
    解决
    代码逻辑判断（java）
    // 指定处理完成之后的结果所保存的位置
    Path OPath = new Path(oPath);
    fs.delete(OPath, true);
    手动删除Hadoop 文件目录
    
    问题 B：
    SafeModeException
    问题描述 & 原因分析 
    该问题可能会使 Hadoop运行任务一直卡在: INFO mapreduce.Job: Runing job。
    由空间磁盘剩余不足导致。实验时，虚拟机根目录剩余空间不足10%，将新安装的一些软件包删除后，重新运行问题得到解决。
    org.apache.hadoop.ipc.RemoteException(org.apache.hadoop.hdfs.server.namenode.SafeModeException): Cannot delete /benchmarks/TestDFSIO. Name node is in safe mode.  
    Resources are low on NN. Please add or free up more resources then turn off safe mode manually. NOTE:  If you turn off safe mode before adding resources, the NN will immediately return to safe mode. Use "hdfs dfsadmin -safemode leave" to turn safe mode off.  
    解决方式
    离开安全模式
    hdfs dfsadmin -safemode leave  
    删除 LInux上 多余文件（实验中采取，简单有效）， 或者 扩展虚拟机分区
    
    
    其他知识
    杀掉当前运行的 Hadoop 任务
    hadoop job -list  # 列出当前运行所有 job
    
    hadoop job -kill job_xx_xx  # 通过job_id 杀掉某个job任务
    查找 Linux 系统上的 大文件
    find . -type f -size +100M  # 查找100M以上的文件
    df -hl  # 查看Linux 磁盘使用情况
    
    
     hadoop jar hadoop-mapreduce-examples-2.7.7.jar wordcount /user/root/input/ /user/root/out1/