MapReduce实战（四）倒排索引的实现

需求：

以上三个文件，用MapReduce进行处理，最终输出以下格式：

hello c.txt-->2 b.txt-->2 a.txt-->3
jerry c.txt-->1 b.txt-->3 a.txt-->1
tom c.txt-->1 b.txt-->1 a.txt-->2

思考：

我们需要进行两个步骤：

1.就是之前的统计单词个数的练习，只不过现在需要加上文件名而已。得到如下效果

hello-->a.txt 3
hello-->b.txt 2
hello-->c.txt 2
jerry-->a.txt 1
jerry-->b.txt 3
jerry-->c.txt 1
tom-->a.txt 2
tom-->b.txt 1
tom-->c.txt 1

2.将key由hello-->a.txt这种形式转化成hello这种形式，然后进行分组。得到如下效果：

hello c.txt-->2 b.txt-->2 a.txt-->3
jerry c.txt-->1 b.txt-->3 a.txt-->1
tom c.txt-->1 b.txt-->1 a.txt-->2

文件目录如下：

InverseIndexStepOne.java:

package cn.darrenchan.hadoop.mr.ii;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
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.input.FileSplit;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class InverseIndexStepOne {
    public static class StepOneMapper extends
            Mapper<LongWritable, Text, Text, LongWritable> {
        @Override
        protected void map(LongWritable key, Text value, Context context)
                throws IOException, InterruptedException {
            // 拿到一行数据
            String line = value.toString();
            // 切分出各个单词
            String[] fields = line.split("\t");
            // 获取这一行数据所在的文件切片
            FileSplit inputSplit = (FileSplit) context.getInputSplit();
            // 从文件切片中获取文件名
            String fileName = inputSplit.getPath().getName();
            for (String field : fields) {
                // 封装kv输出 ， k ： hello-->a.txt v: 1
                context.write(new Text(field + "-->" + fileName),
                        new LongWritable(1));
            }
        }
    }

    public static class StepOneReducer extends
            Reducer<Text, LongWritable, Text, LongWritable> {
        @Override
        protected void reduce(Text key, Iterable<LongWritable> values,
                Context context) throws IOException, InterruptedException {
            int count = 0;
            for (LongWritable value : values) {
                count += value.get();
            }
            // <hello-->a.txt,{1,1,1....}>
            context.write(key, new LongWritable(count));
        }
    }

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

        job.setJarByClass(InverseIndexStepOne.class);

        job.setMapperClass(StepOneMapper.class);
        job.setReducerClass(StepOneReducer.class);

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(LongWritable.class);

        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(LongWritable.class);

        //检查一下参数所指定的输出路径是否存在，如果已存在，先删除
        Path outputPath = new Path(args[1]);
        FileSystem fileSystem = FileSystem.get(conf);
        if (fileSystem.exists(outputPath)) {
            fileSystem.delete(outputPath, true);
        }

        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, outputPath);

        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }

}

InverseIndexStepTwo.java:

package cn.darrenchan.hadoop.mr.ii;

import java.io.IOException;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
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;

public class InverseIndexStepTwo {
    // k: 行起始偏移量 v: {hello-->a.txt 3}
    // map输出的结果是这个形式 ： <hello,a.txt-->3>
    public static class StepTwoMapper extends
            Mapper<LongWritable, Text, Text, Text> {
        @Override
        protected void map(LongWritable key, Text value, Context context)
                throws IOException, InterruptedException {
            String line = value.toString();
            String[] fields = line.split("-->");
            String[] strings = fields[1].split("\t");
            context.write(new Text(fields[0]), new Text(strings[0] + "-->"
                    + strings[1]));
        }
    }

    // 拿到的数据 <hello,{a.txt-->3,b.txt-->2,c.txt-->1}>
    // 输出的结果就是 k: hello v: a.txt-->3 b.txt-->2 c.txt-->1
    public static class StepTwoReducer extends Reducer<Text, Text, Text, Text> {
        @Override
        protected void reduce(Text key, Iterable<Text> values, Context context)
                throws IOException, InterruptedException {
            String result = "    ";
            for (Text value : values) {
                result += value + "    ";
            }
            context.write(key, new Text(result));
        }
    }

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

        job.setJarByClass(InverseIndexStepTwo.class);

        job.setMapperClass(StepTwoMapper.class);
        job.setReducerClass(StepTwoReducer.class);

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(Text.class);

        // 检查一下参数所指定的输出路径是否存在，如果已存在，先删除
        Path outputPath = new Path(args[1]);
        FileSystem fileSystem = FileSystem.get(conf);
        if (fileSystem.exists(outputPath)) {
            fileSystem.delete(outputPath, true);
        }

        FileInputFormat.setInputPaths(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, outputPath);

        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }

}

首先将三个文件传到HDFS的/ii/srcdata目录下面，执行指令：

hadoop jar ii.jar cn.darrenchan.hadoop.mr.ii.InverseIndexStepOne /ii/srcdata /ii/output1

打印运行信息：

17/03/01 17:55:38 INFO client.RMProxy: Connecting to ResourceManager at weekend110/192.168.230.134:8032
17/03/01 17:55:38 WARN mapreduce.JobSubmitter: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
17/03/01 17:55:39 INFO input.FileInputFormat: Total input paths to process : 3
17/03/01 17:55:39 INFO mapreduce.JobSubmitter: number of splits:3
17/03/01 17:55:40 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1488372977056_0001
17/03/01 17:55:41 INFO impl.YarnClientImpl: Submitted application application_1488372977056_0001
17/03/01 17:55:41 INFO mapreduce.Job: The url to track the job: http://weekend110:8088/proxy/application_1488372977056_0001/
17/03/01 17:55:41 INFO mapreduce.Job: Running job: job_1488372977056_0001
17/03/01 17:55:52 INFO mapreduce.Job: Job job_1488372977056_0001 running in uber mode : false
17/03/01 17:55:52 INFO mapreduce.Job: map 0% reduce 0%
17/03/01 17:56:11 INFO mapreduce.Job: map 33% reduce 0%
17/03/01 17:56:12 INFO mapreduce.Job: map 100% reduce 0%
17/03/01 17:56:18 INFO mapreduce.Job: map 100% reduce 100%
17/03/01 17:56:18 INFO mapreduce.Job: Job job_1488372977056_0001 completed successfully
17/03/01 17:56:18 INFO mapreduce.Job: Counters: 49
File System Counters
FILE: Number of bytes read=382
FILE: Number of bytes written=372665
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=402
HDFS: Number of bytes written=138
HDFS: Number of read operations=12
HDFS: Number of large read operations=0
HDFS: Number of write operations=2
Job Counters
Launched map tasks=3
Launched reduce tasks=1
Data-local map tasks=3
Total time spent by all maps in occupied slots (ms)=51196
Total time spent by all reduces in occupied slots (ms)=3018
Total time spent by all map tasks (ms)=51196
Total time spent by all reduce tasks (ms)=3018
Total vcore-seconds taken by all map tasks=51196
Total vcore-seconds taken by all reduce tasks=3018
Total megabyte-seconds taken by all map tasks=52424704
Total megabyte-seconds taken by all reduce tasks=3090432
Map-Reduce Framework
Map input records=8
Map output records=16
Map output bytes=344
Map output materialized bytes=394
Input split bytes=312
Combine input records=0
Combine output records=0
Reduce input groups=9
Reduce shuffle bytes=394
Reduce input records=16
Reduce output records=9
Spilled Records=32
Shuffled Maps =3
Failed Shuffles=0
Merged Map outputs=3
GC time elapsed (ms)=1077
CPU time spent (ms)=6740
Physical memory (bytes) snapshot=538701824
Virtual memory (bytes) snapshot=1450766336
Total committed heap usage (bytes)=379793408
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=90
File Output Format Counters
Bytes Written=138

运行结果如下：

hello-->a.txt 3
hello-->b.txt 2
hello-->c.txt 2
jerry-->a.txt 1
jerry-->b.txt 3
jerry-->c.txt 1
tom-->a.txt 2
tom-->b.txt 1
tom-->c.txt 1

执行指令：

hadoop jar ii.jar cn.darrenchan.hadoop.mr.ii.InverseIndexStepTwo /ii/output1 /ii/output2

打印运行信息：

17/03/01 18:03:31 INFO client.RMProxy: Connecting to ResourceManager at weekend110/192.168.230.134:8032
17/03/01 18:03:31 WARN mapreduce.JobSubmitter: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
17/03/01 18:03:31 INFO input.FileInputFormat: Total input paths to process : 1
17/03/01 18:03:31 INFO mapreduce.JobSubmitter: number of splits:1
17/03/01 18:03:32 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1488372977056_0003
17/03/01 18:03:32 INFO impl.YarnClientImpl: Submitted application application_1488372977056_0003
17/03/01 18:03:32 INFO mapreduce.Job: The url to track the job: http://weekend110:8088/proxy/application_1488372977056_0003/
17/03/01 18:03:32 INFO mapreduce.Job: Running job: job_1488372977056_0003
17/03/01 18:03:38 INFO mapreduce.Job: Job job_1488372977056_0003 running in uber mode : false
17/03/01 18:03:38 INFO mapreduce.Job: map 0% reduce 0%
17/03/01 18:03:43 INFO mapreduce.Job: map 100% reduce 0%
17/03/01 18:03:47 INFO mapreduce.Job: map 100% reduce 100%
17/03/01 18:03:48 INFO mapreduce.Job: Job job_1488372977056_0003 completed successfully
17/03/01 18:03:48 INFO mapreduce.Job: Counters: 49
File System Counters
FILE: Number of bytes read=162
FILE: Number of bytes written=185553
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=249
HDFS: Number of bytes written=112
HDFS: Number of read operations=6
HDFS: Number of large read operations=0
HDFS: Number of write operations=2
Job Counters
Launched map tasks=1
Launched reduce tasks=1
Data-local map tasks=1
Total time spent by all maps in occupied slots (ms)=2605
Total time spent by all reduces in occupied slots (ms)=2725
Total time spent by all map tasks (ms)=2605
Total time spent by all reduce tasks (ms)=2725
Total vcore-seconds taken by all map tasks=2605
Total vcore-seconds taken by all reduce tasks=2725
Total megabyte-seconds taken by all map tasks=2667520
Total megabyte-seconds taken by all reduce tasks=2790400
Map-Reduce Framework
Map input records=9
Map output records=9
Map output bytes=138
Map output materialized bytes=162
Input split bytes=111
Combine input records=0
Combine output records=0
Reduce input groups=3
Reduce shuffle bytes=162
Reduce input records=9
Reduce output records=3
Spilled Records=18
Shuffled Maps =1
Failed Shuffles=0
Merged Map outputs=1
GC time elapsed (ms)=138
CPU time spent (ms)=820
Physical memory (bytes) snapshot=218480640
Virtual memory (bytes) snapshot=726454272
Total committed heap usage (bytes)=137433088
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=138
File Output Format Counters
Bytes Written=112

运行结果如下：

hello c.txt-->2 b.txt-->2 a.txt-->3
jerry c.txt-->1 b.txt-->3 a.txt-->1
tom c.txt-->1 b.txt-->1 a.txt-->2