hadoop编程实践（二）

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集群上使用

jar包

• 首先将之前FileExist文件进行打包，得到.jar文件：
  • 
• 将其拷贝到集群中，并使用hadoop jar命令运行：
  • 

WordCount

添加依赖

• 首先我们需要新建一个WordCount项目，首先要添加Hadoop的包依赖
  • /usr/local/hadoop/share/hadoop/common
    • hadoop-common-xxx.jar
    • hadoop-nfs-xxx.jar
  • /usr/local/hadoop/share/hadoop/common/lib 下的所有Jar包
  • /usr/local/hadoop/share/hadoop/mapreduce该目录下所有JAR包
  • /usr/local/hadoop/share/hadoop/mapreduce/lib目录下所有JAR包
  • 

编写程序

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.IntWritable;
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.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;

public class WordCount {
    public WordCount () {
    }

    public static class TokenizerMapper
            extends Mapper<Object, Text, Text, IntWritable>{

        private final static IntWritable one = new IntWritable(1);
        private Text word = new Text();
        public TokenizerMapper () {
        }
        public void map(Object key, Text value, Mapper<Object, Text, Text, IntWritable>.Context context
        ) throws IOException, InterruptedException {
            StringTokenizer itr = new StringTokenizer(value.toString());
            while (itr.hasMoreTokens()) {
                this.word.set(itr.nextToken());
                context.write(this.word, one);
            }
        }
    }

    public static class IntSumReducer
            extends Reducer<Text,IntWritable,Text,IntWritable> {
        private IntWritable result = new IntWritable();

        public void reduce(Text key, Iterable<IntWritable> values,
                           Reducer<Text,IntWritable,Text,IntWritable>.Context context
        ) throws IOException, InterruptedException {
            int sum = 0;
            for (IntWritable val : values) {
                sum += val.get();
            }
            this.result.set(sum);
            context.write(key, this.result);
        }
    }

    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        String[] otherArgs = (new GenericOptionsParser(conf, args)).getRemainingArgs();
        if (otherArgs.length < 2) {
           System.err.println("Usage: wordcount <in>[<in>...] <out>");
           System.exit(2);
        }
        Job job = Job.getInstance(conf, "word count");
        job.setJarByClass(WordCount.class);
        job.setMapperClass(WordCount.TokenizerMapper.class);
        job.setCombinerClass(WordCount.IntSumReducer.class);
        job.setReducerClass(WordCount.IntSumReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        for (int i = 0; i < otherArgs.length-1; i++) {
            FileInputFormat.addInputPath(job, new Path(otherArgs[i]));
        }
        FileOutputFormat.setOutputPath(job, new Path(otherArgs[otherArgs.length-1]));
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

打包成JAR包

• 打开Project Structure：
  • 
• 进行编译：
  • 
• 生成并查看JAR包：
  • 

本地伪分布式运行

• 创建两个文件作为输入，内容为：

  • I love Spark
    I love Hadoop

    Hadoop is good
    Spark is fast

• 将本地文件放入hdfs中：

  • hdfs dfs -mkdir -p /user/hadoop/input
    hdfs dfs -put ./wordfile1.txt input
    hdfs dfs -put ./wordfile2.txt input
    

• 在hdfs中查看：

  • hdfs dfs -ls input
    

  • 

• 运行：

  • hadoop jar WordCount.jar input output
    

• 查看结果：

  • hdfs dfs -cat output/*
    

  • 

集群上运行

• 首先将JAR包和文件放入集群：

  • 

• 将其拷贝到HDFS中：

  • hdfs dfs -mkdir -p /user/hadoop7/input
    hdfs dfs -put ./wordfile1.txt input
    hdfs dfs -put ./wordfile2.txt input
    

• 查看文件：

  • 

• 运行：

  • hadoop jar WordCount.jar input output
    

  • 

• 查看集群运行情况

  • 在连接VPN时，在浏览器中输入10.11.6.91:50070
  •