大文本文件（接近7GB）: 统计频数，Top K问题求解（二）

说明

本文是接着上一篇博文：大文本文件（接近7GB）: 统计频数，Top K问题求解，用Hadoop mapreduce 处理求解说明，标题沿用了上一篇的

原始输入

5.6G的原始txt数据，hdfs的block size=128MB，原始文件被分成了48个block

word count MR & 输出

输出

如下是类似word count先求出每个IP出现的总次数，最后输出文件如下

• 随机数 以及 随机数出现的总次数
  

MR日志

如下是截取的 mapreduce 日志

2020-05-01 11:40:58,120 INFO [org.apache.hadoop.mapreduce.Job] - Counters: 36
	File System Counters
		FILE: Number of bytes read=11257636997
		FILE: Number of bytes written=12669380730
		FILE: Number of read operations=0
		FILE: Number of large read operations=0
		FILE: Number of write operations=0
		HDFS: Number of bytes read=164048907782
		HDFS: Number of bytes written=1188890
		HDFS: Number of read operations=2649
		HDFS: Number of large read operations=0
		HDFS: Number of write operations=100
		HDFS: Number of bytes read erasure-coded=0
	Map-Reduce Framework
		Map input records=1073741824
		Map output records=1073741824
		Map output bytes=10618119939
		Map output materialized bytes=57067008
		Input split bytes=4800
		Combine input records=1109778196
		Combine output records=40836372
		Reduce input groups=100000
		Reduce shuffle bytes=57067008
		Reduce input records=4800000
		Reduce output records=100000
		Spilled Records=45636372
		Shuffled Maps =48
		Failed Shuffles=0
		Merged Map outputs=48
		GC time elapsed (ms)=5273
		Total committed heap usage (bytes)=52556201984
	Shuffle Errors
		BAD_ID=0
		CONNECTION=0
		IO_ERROR=0
		WRONG_LENGTH=0
		WRONG_MAP=0
		WRONG_REDUCE=0
	File Input Format Counters 
		Bytes Read=6323345155
	File Output Format Counters 
		Bytes Written=1188890
2020-05-01 11:40:58,121 DEBUG [org.apache.hadoop.security.UserGroupInformation] - PrivilegedAction as:mubi (auth:SIMPLE) from:org.apache.hadoop.mapreduce.Job.updateStatus(Job.java:328)
Job Finished

• Map input records=1073741824: 即随机产生的1024 * 1024 * 1024=1073741824条原始数据

• map处理是读取一行处理一个数据，所以 Map output records=1073741824

• 由于没有特殊设置split size，所以默认的map数量就是block size，即 48 个

• 设置了 Combiner，因为求sum是符合的Combiner的

• Reduce output records=100000： 即reduce输出是 100000 条记录，即我们随机生产的【0，100000】的随机整数

附：Java代码

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.LongWritable;
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 java.io.IOException;
import java.net.URI;
import java.util.Iterator;


/**
 * @Author mubi
 * @Date 2020/4/30 22:42
 */
public class IpCntMR {

    static class IpCountMapper extends Mapper<LongWritable, Text, Text, IntWritable> {

        @Override
        public void map(LongWritable key, Text value, Context context)
                throws IOException, InterruptedException {
            // 将mapTask传给我们的文本内容先转换为String
            String ip = value.toString();
            // 将 ip 输出为
            context.write(new Text(ip), new IntWritable(1));
        }
    }

    static class IpCountReducer extends
            Reducer<Text, IntWritable, Text, IntWritable> {
        @Override
        protected void reduce(Text key, Iterable<IntWritable> values,Context context)throws IOException, InterruptedException {
            int count=0;
            Iterator<IntWritable> it = values.iterator();
            while(it.hasNext()){
                count += it.next().get();
            }
            context.write(key, new IntWritable(count));
        }
    }

    public static void main(String[] args) throws Exception {
        String uri = "hdfs://localhost:9000";
        //输入路径
        String dst = uri + "/input/ipinput";

        //输出路径，必须是不存在的，空文件也不行。
        String dstOut = uri + "/output/ipoutput";

        Configuration hadoopConfig = new Configuration();
        hadoopConfig.set("fs.hdfs.impl", org.apache.hadoop.hdfs.DistributedFileSystem.class.getName());

        hadoopConfig.set("fs.file.impl", org.apache.hadoop.fs.LocalFileSystem.class.getName());

        // 删除输出目录
        FileSystem fs = FileSystem.get(new URI(uri), hadoopConfig);
        String path = "/output/ipoutput";
        Path outputPath = new Path(path);
        if (fs.exists(outputPath)){
            fs.delete(outputPath, true);;
        }

        Job job = new Job(hadoopConfig);
        //如果需要打成jar运行，需要下面这句
        job.setJarByClass(IpCntMR.class);
        job.setJobName("IpCntMR");

        //job执行作业时输入和输出文件的路径
        FileInputFormat.addInputPath(job, new Path(dst));
        FileOutputFormat.setOutputPath(job, new Path(dstOut));

        //指定自定义的Mapper和Reducer作为两个阶段的任务处理类
        job.setMapperClass(IpCountMapper.class);
        job.setReducerClass(IpCountReducer.class);

        // 设置combiner
        job.setCombinerClass(IpCountReducer.class);

        //设置最后输出结果的Key和Value的类型
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);

        //执行job，直到完成
        job.waitForCompletion(true);
        System.out.println("Job Finished");
    }

}

TopK 求解

输入

输入则是上文 word count 的输出文件

输出

• 第一列是频数，第二列是对应的IP
  

附：Java代码

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.*;
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 sun.security.krb5.internal.KRBError;

import java.io.IOException;
import java.net.URI;
import java.util.Iterator;
import java.util.Stack;
import java.util.TreeMap;


/**
 * @Author mubi
 * @Date 2020/4/30 22:42
 */
public class TopCntMR {

    static int K = 10;

    static class KMapper extends Mapper<LongWritable, Text, IntWritable, Text> {

        TreeMap<Integer, String> map = new TreeMap<Integer, String>();

        @Override
        public void map(LongWritable key, Text value, Context context)
                throws IOException, InterruptedException {
            String line = value.toString();
            if(line.trim().length() > 0 && line.indexOf("\t") != -1) {

                String[] arr = line.split("\t", 2);
                String name = arr[0];
                Integer num = Integer.parseInt(arr[1]);
                map.put(num, name);

                if(map.size() > K) {
                    map.remove(map.firstKey());
                }
            }
        }

        @Override
        protected void cleanup(
                Mapper<LongWritable, Text, IntWritable, Text>.Context context)
                throws IOException, InterruptedException {
            for(Integer num : map.keySet()) {
                context.write(new IntWritable(num), new Text(map.get(num)));
            }
        }
    }

    public static class KReducer extends Reducer<IntWritable, Text, IntWritable, Text> {

        TreeMap<Integer, String> map = new TreeMap<Integer, String>();

        @Override
        public void reduce(IntWritable key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
            map.put(key.get(), values.iterator().next().toString());
            if(map.size() > K) {
                map.remove(map.firstKey());
            }
        }

        @Override
        protected void cleanup(
                Reducer<IntWritable, Text, IntWritable, Text>.Context context)
                throws IOException, InterruptedException {
            Stack<Integer> sta = new Stack<Integer>();
            for(Integer num : map.keySet()) {
                sta.push(num);
            }
            while (!sta.isEmpty()){
                int num = sta.pop();
                context.write(new IntWritable(num), new Text(map.get(num)));
            }
        }
    }

    public static void main(String[] args) throws Exception {
        String uri = "hdfs://localhost:9000";
        //输入路径
        String dst = uri + "/output/ipoutput/part-r-00000";

        //输出路径，必须是不存在的，空文件也不行。
        String dstOut = uri + "/output/topk";

        Configuration hadoopConfig = new Configuration();
        hadoopConfig.set("fs.hdfs.impl", org.apache.hadoop.hdfs.DistributedFileSystem.class.getName());

        hadoopConfig.set("fs.file.impl", org.apache.hadoop.fs.LocalFileSystem.class.getName());

        // 删除输出目录
        FileSystem fs = FileSystem.get(new URI(uri), hadoopConfig);
        String path = "/output/topk";
        Path outputPath = new Path(path);
        if (fs.exists(outputPath)){
            fs.delete(outputPath, true);;
        }

        Job job = new Job(hadoopConfig);
        //如果需要打成jar运行，需要下面这句
        job.setJarByClass(TopCntMR.class);
        job.setJobName("IpCntMR");

        //job执行作业时输入和输出文件的路径
        FileInputFormat.addInputPath(job, new Path(dst));
        FileOutputFormat.setOutputPath(job, new Path(dstOut));

        //指定自定义的Mapper和Reducer作为两个阶段的任务处理类
        job.setMapperClass(KMapper.class);
        job.setReducerClass(KReducer.class);

        // 设置combiner
        job.setCombinerClass(KReducer.class);

        //设置最后输出结果的Key和Value的类型
        job.setOutputKeyClass(IntWritable.class);
        job.setOutputValueClass(Text.class);

        //执行job，直到完成
        job.waitForCompletion(true);
        System.out.println("Job Finished");
    }

}