仿分词统计的MapReduce 程序。

HDFS 数据格式 ： 

举例单条数据：02-26 08:01:56 [qtp512249001-42] INFO  async-statistics - class com.spring.aop.StorageManagerStatAspect${"method":"com.systoon.scloud.master.controller.ImageController.download","ip":"172.28.6.131","port":"38001","father":"sun.reflect.GeneratedMethodAccessor8.invoke/null/-1","requestIp":"106.39.33.246","argsMap":{"org.eclipse.jetty.server.Request:0":{"requestURI":"/f/KZ0wxxbvFz924VaHS8JN1Fk42jV9OBMCHYoLtuc9sAkfF.jpg"},"org.eclipse.jetty.server.Response:1":1462183982},"processTime":50,"time":1456444916225,"retValMap":{":":"this object is null"}}

是写出的一行日志。  日志结构是时间 + 打印的类 + JSON

那么现在是要进行一个统计 MR 分析。

那么开始上代码：

        

import com.alibaba.fastjson.JSON;
import com.alibaba.fastjson.JSONObject;
import com.rocky.util.TimeUtils;
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.mapred.*;
import org.apache.hadoop.mapred.lib.MultipleOutputFormat;
import org.apache.hadoop.util.Progressable;
import java.io.IOException;
import java.net.URI;
import java.util.Iterator;
public class MulOutput {
    public static final String clazz = "com.spring.aop.StorageManagerStatAspect";
    public static final String m_download = "com.systoon.scloud.master.controller.ImageController.download";
    public static final String m_upload   = "com.systoon.scloud.master.controller.DirectUploadFile.directUploadFile";
    public static class Map extends MapReduceBase implements Mapper<LongWritable, Text, Text, IntWritable>{
        private final static IntWritable one = new IntWritable(1);
        Text word = new Text();
        @Override
        public void map(LongWritable key, Text value, OutputCollector<Text, IntWritable> output,
                        Reporter reporter) throws IOException {
            String line = value.toString();
            if(line.contains(clazz)){
                if(line.contains(m_download)){
                    String tempObject = line.split(clazz)[1];
                    String tmp = tempObject.substring(1,tempObject.length());
                    JSONObject jsonObject = JSON.parseObject(tmp);
                    String method = jsonObject.get("method").toString();
                    if( method.equals(m_download) ){
                        word.set("download");
                        output.collect(word, one);
                    }
                } else if(line.contains(m_upload)) {
                    String tempObject = line.split(clazz)[1];
                    String tmp = tempObject.substring(1,tempObject.length());
                    JSONObject jsonObject = JSON.parseObject(tmp);
                    String method = jsonObject.get("method").toString();
                    if( method.equals(m_upload) ){
                        word.set("upload");
                        output.collect(word, one);
                    }
                } else {
                    word.set("debug");
                    output.collect(word,one);
                }
            } else {
                word.set("others");
                output.collect(word, one);
            }
        }
    }
    public static class Reduce extends MapReduceBase
            implements Reducer<Text, IntWritable, Text, IntWritable> {
        public void reduce(Text key, Iterator<IntWritable> values,
                           OutputCollector<Text, IntWritable> output, Reporter reporter)
        throws IOException{
            int sum = 0;
            while (values.hasNext()) {
                sum += values.next().get();
            }
            output.collect(key, new IntWritable(sum));
        }
    }
    public static void main(String[] args) throws Exception{
        JobConf jobConf = new JobConf(MulOutput.class);
        jobConf.setJobName("rocky_test");
        String outPath = "/test/mapReduce/statis"+TimeUtils.getStringDate();
        final FileSystem filesystem = FileSystem.get(new URI(outPath), jobConf);
        if(filesystem.exists(new Path(outPath))){
            filesystem.delete(new Path(outPath), true);
        }
        jobConf.setMapperClass(Map.class);         //为job设置Mapper类
        jobConf.setMapOutputKeyClass(Text.class);   //输出数据设置Key类
        jobConf.setMapOutputValueClass(IntWritable.class);  //输出数据设置Key类
        jobConf.setCombinerClass(Reduce.class);             // 为job设置Combiner类
        jobConf.setReducerClass(Reduce.class);              // 为job设置Reduce类
        jobConf.setOutputKeyClass(Text.class);              // 输出数据设置Key类
        jobConf.setOutputValueClass(IntWritable.class);     // 输出数据设置Key类
        FileInputFormat.setInputPaths(jobConf, new Path("/test/mapReduce/statistics.log.2016-02-26"));
//        // 扫描组合path
//        FileInputFormat.addInputPath();
        jobConf.setOutputFormat(MyMultipleFilesTextOutputFormat.class);
        FileOutputFormat.setOutputPath(jobConf, new Path(outPath));
        JobClient.runJob(jobConf);         //运行一个job
    }
}

简单来讲就是 Map 按行读， Reduce 进行汇总。   也是统计中最最常用的。  轻松解决问题。

来自为知笔记(Wiz)

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