正文之前
感觉自己有一个世纪没写过文章了似的。不管了，今天看数据算法，那就拿这个开刀
另外，小生不才体真的超棒啊 ！！
小生不才，
冒昧地喜欢了姑娘这么久，
甚是打扰，望多包涵，
今日愿以山上一草一木为誓，
今日你我二人就此别过，
若有重逢，我必待你眉眼如初，岁月如故，
若姑娘与有缘人终成眷属，
小生独羡其幸，
愿他宠你入骨，惜你如命，
好圆小生半事幽梦，
事已至此，缘尽，人散去了罢……
正文
算法梗概：
import scale.Tuple2;import java.util.List;import java.util.TreeMap;import java.util.SortedMap;import <your-package>.T; //此处是指你用到的自己定义的数据结构类型static SortedMap<Integer, T> topN(List<Tuple<T,Integer>> L, int N){ if((L == NULL) ||(L.isEmpty())){ return null;
}
SortedMap<Integer, T> topN = new TreeMap<Integer, T>(); for(Tuple2<T,Integer> element : L){ // element._1 's type is T;
// element._2 's type is Integer;
topN.put(element._2,element._1); // only leave top N;
if(topN.size() > N){
topN.remove(topN.firstKey());
}
} return topN;
}这就是TopN算法的精华所在，对一个排序Map进行依次输入，当该Map的size等于我们需要的N时，再进来一个元素，那么就必须把整合该元素后的Map最小值删除掉。也就是上面的firstKey。
我昨天肝了一天的程序，结果最后卡在一个小BUG身上，真是要死要活。。。去他么的！！！！书里面也没说这个地方，要不是我瞎鸡儿机灵，还真的没法解决这个BUG。。他么原理压根看不懂好嚒！！
这是我整个的文件架构
// hadoopClear.java//import java.io.IOException;////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 hadoopClear {// //map将输入中的value复制到输出数据的key上，并直接输出// public static class Map extends Mapper<Object,Text,Text,Text>{// //每行数据// private static Text line=new Text();// //实现map函数// public void map(Object key,Text value,Context context) throws IOException,InterruptedException{// String line = value.toString();// String[] values = line.split(" ");// line = "";// for(int i=0; i<values.length -1 ;++i)// {// line += values[i];// }// context.write(new Text(line), new Text(""));// }// }// //reduce将输入中的key复制到输出数据的key上，并直接输出// public static class Reduce extends Reducer<Text,Text,Text,Text>{// //实现reduce函数// public void reduce(Text key,Iterable<Text> values,Context context)// throws IOException,InterruptedException{// context.write(key, new Text(""));// }// }//// public static void main(String[] args) throws Exception{// Configuration conf = new Configuration();// //这句话很关键// conf.set("mapred.job.tracker", "node61:9001");// String[] ioArgs=new String[]{"dedup_in","dedup_out"};// String[] otherArgs = new GenericOptionsParser(conf, ioArgs).getRemainingArgs();// if (otherArgs.length != 2) {// System.err.println("Usage: Data Deduplication <in> <out>");// System.exit(2);// }// Job job = new Job(conf, "Data Deduplication");// job.setJarByClass(hadoopClear.class);// //设置Map、Combine和Reduce处理类// job.setMapperClass(Map.class);// job.setCombinerClass(Reduce.class);// job.setReducerClass(Reduce.class);// //设置输出类型// job.setOutputKeyClass(Text.class);// job.setOutputValueClass(Text.class);// //设置输入和输出目录// FileInputFormat.addInputPath(job, new Path(args[0]));// FileOutputFormat.setOutputPath(job, new Path(args[1]));// System.exit(job.waitForCompletion(true) ? 0 : 1);// }//}////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.lib.input.FileInputFormat;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;/**
* Created by ZZB on 2018/6/10.
*/public class hadoopClear { public static void main(String[] args)throws Exception{ //创建配置对象
Configuration conf = new Configuration(); //创建job对象
Job job = Job.getInstance(conf,"hadoopClear"); //设置运行job的类
job.setJarByClass(hadoopClear.class); //设置mapper 类
job.setMapperClass(ZZB_Mapper.class); //设置reduce 类
job.setReducerClass(ZZB_Reducer.class); //设置map输出的key value
job.setMapOutputKeyClass(IntWritable.class);
job.setMapOutputValueClass(Text.class); //设置reduce 输出的 key value
job.setOutputKeyClass(IntWritable.class);
job.setOutputValueClass(Text.class); //设置输入输出的路径
FileInputFormat.setInputPaths(job, new Path(args[1]));
FileOutputFormat.setOutputPath(job, new Path(args[2])); //提交job
boolean b = job.waitForCompletion(true); if(!b){
System.out.println("wordcount task fail!");
}
}
}第二个Java代码：
//ZZB_Mapper.javaimport org.apache.hadoop.io.IntWritable;import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Mapper;import java.util.TreeMap;import java.util.SortedMap;import java.io.IOException;/**
* Created by ZZB on 2018/6/10.
*/public class ZZB_Mapper extends Mapper<LongWritable, Text, IntWritable,Text>{ private SortedMap<Double, Text> top10cats = new TreeMap<>(); private int N = 10; @Override
protected void setup(Context context) throws IOException, InterruptedException { super.setup(context);
} protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException{ //得到输入的每一行数据
String line = value.toString(); //通过空格分隔
String[] values = line.split(",");
Double weight = Double.parseDouble(values[0]);
Text x = new Text(line);
top10cats.put(weight,x); if (top10cats.size()>N){
top10cats.remove(top10cats.firstKey());
}
} @Override
protected void cleanup(Context context) throws IOException, InterruptedException { int s = 4; for (Text catAttr : top10cats.values()){
s++;
context.write(new IntWritable(s),catAttr);
}
}
}第三个Java代码：
//ZZB_Reducer.javaimport org.apache.hadoop.io.IntWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Reducer;import java.io.IOException;import java.util.*;/**
* Created by ZZB on 2018/6/10.
*/public class ZZB_Reducer extends Reducer<IntWritable, Text,IntWritable, Text> { private int N = 10; private int s = 0; private SortedMap<Double, Text> finaltop10 = new TreeMap<>(); protected void reduce(IntWritable key, Text catRecord, Context context) throws IOException, InterruptedException {
String cat = catRecord.toString();
String[] tokens = cat.split(",");
finaltop10.put(1.0, catRecord); if (finaltop10.size() > N) {
finaltop10.remove(finaltop10.firstKey());
} for (Text text : finaltop10.values()){
s++;
context.write(new IntWritable(s),text);
}
}
}其中的一个Bug就是，如下图所示的这样，如果我不给一个X做新的序列化字符串载体，那么直接put原本的value进入的话，就会显示是空字符串，报错是empty String。。
我他么也很绝望啊！！！为毛啊！！难道splits方法会要干掉原本的对象中的这个内容吗？？？我日哦！！
不过不管如何，最后我反正是成功了！！很欣慰！！
下面是过程:
[zbzhang@node61 ~]$ ./test.sh18/08/17 16:31:52 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable Deleted /output/result/_SUCCESS Deleted /output/result/part-r-0000018/08/17 16:31:53 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable18/08/17 16:31:55 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable18/08/17 16:31:56 INFO client.RMProxy: Connecting to ResourceManager at node61/11.11.0.61:803218/08/17 16:31:56 WARN mapreduce.JobResourceUploader: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this. 18/08/17 16:31:56 INFO input.FileInputFormat: Total input files to process : 1 18/08/17 16:31:57 INFO mapreduce.JobSubmitter: number of splits:1 18/08/17 16:31:57 INFO Configuration.deprecation: yarn.resourcemanager.system-metrics-publisher.enabled is deprecated. Instead, use yarn.system-metrics-publisher.enabled18/08/17 16:31:57 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1534317717839_003218/08/17 16:31:57 INFO impl.YarnClientImpl: Submitted application application_1534317717839_003218/08/17 16:31:57 INFO mapreduce.Job: The url to track the job: http://node61:8088/proxy/application_1534317717839_0032/ 18/08/17 16:31:57 INFO mapreduce.Job: Running job: job_1534317717839_003218/08/17 16:32:05 INFO mapreduce.Job: Job job_1534317717839_0032 running in uber mode : false18/08/17 16:32:05 INFO mapreduce.Job: map 0% reduce 0% 18/08/17 16:32:10 INFO mapreduce.Job: map 100% reduce 0% 18/08/17 16:32:15 INFO mapreduce.Job: map 100% reduce 100% 18/08/17 16:32:15 INFO mapreduce.Job: Job job_1534317717839_0032 completed successfully18/08/17 16:32:15 INFO mapreduce.Job: Counters: 49 File System Counters FILE: Number of bytes read=226 FILE: Number of bytes written=395195 FILE: Number of read operations=0 FILE: Number of large read operations=0 FILE: Number of write operations=0 HDFS: Number of bytes read=1669 HDFS: Number of bytes written=185 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)=2784 Total time spent by all reduces in occupied slots (ms)=3008 Total time spent by all map tasks (ms)=2784 Total time spent by all reduce tasks (ms)=3008 Total vcore-milliseconds taken by all map tasks=2784 Total vcore-milliseconds taken by all reduce tasks=3008 Total megabyte-milliseconds taken by all map tasks=2850816 Total megabyte-milliseconds taken by all reduce tasks=3080192 Map-Reduce Framework Map input records=100 Map output records=10 Map output bytes=200 Map output materialized bytes=226 Input split bytes=97 Combine input records=0 Combine output records=0 Reduce input groups=10 Reduce shuffle bytes=226 Reduce input records=10 Reduce output records=10 Spilled Records=20 Shuffled Maps =1 Failed Shuffles=0 Merged Map outputs=1 GC time elapsed (ms)=120 CPU time spent (ms)=1760 Physical memory (bytes) snapshot=505204736 Virtual memory (bytes) snapshot=5761363968 Total committed heap usage (bytes)=343932928 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=1572 File Output Format Counters Bytes Written=18518/08/17 16:32:16 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
下面是结果：
5 919,cat17,cat176 926,cat21,cat217 947,cat61,cat618 950,cat10,cat109 952,cat13,cat1310 958,cat52,cat5211 976,cat92,cat9212 977,cat83,cat8313 987,cat23,cat2314 993,cat39,cat39
可见还是很靠谱的。。。我下面把我的测试脚本以及测试数据都贴上来给大家伙瞧一瞧哈！！
[zbzhang@node61 ~]$ cat test.sh hdfs dfs -rm /output/result/* hdfs dfs -rmdir /output/result hadoop jar hadoopClear.jar hadoopClear /input/cat.txt /output/result hdfs dfs -cat /output/result/* rm result.txt hdfs dfs -get /output/result/part* result.txt
下面是一百只猫的数据，大概就是根据这一百只猫的随机体重进行排序。在数据量小的情况下当然可以直接读取，但是如果是过亿条数据？？我估计你的电脑直接会卡死。但是Hadoop就不会了对吧？！！肯定不会的撒！
说的我都想试试了！！
作者：HustWolf
链接：https://www.jianshu.com/p/4e46c6a45076