MapReduce怎样实现TopK

今天就跟大家聊聊有关MapReduce怎样实现TopK，可能很多人都不太了解，为了让大家更加了解，小编给大家总结了以下内容，希望大家根据这篇文章可以有所收获。

需求: HTTP日志文件中全部流量前80%的记录, 按流量值降序排序

输出格式

HTTP日志文件:

1363157985066  13726230503 00-FD-07-A4-72-B8:CMCC 120.196.100.82 i02.c.aliimg.com  24 27 2481 24681 2001363157995052  13826544101 5C-0E-8B-C7-F1-E0:CMCC 120.197.40.4   4 0 264 0 2001363157991076  13926435656 20-10-7A-28-CC-0A:CMCC 120.196.100.99   2 4 132 1512 2001363154400022  13926251106 5C-0E-8B-8B-B1-50:CMCC 120.197.40.4   4 0 240 0 2001363157993044  18211575961 94-71-AC-CD-E6-18:CMCC-EASY 120.196.100.99 iface.qiyi.com 视频网站 15 12 1527 2106 2001363157995074  84138413 5C-0E-8B-8C-E8-20:7DaysInn 120.197.40.4 122.72.52.12  20 16 4116 1432 2001363157993055  13560439658 C4-17-FE-BA-DE-D9:CMCC 120.196.100.99   18 15 1116 954 2001363157995033  15920133257 5C-0E-8B-C7-BA-20:CMCC 120.197.40.4 sug.so.360.cn 信息安全 20 20 3156 2936 2001363157983019 13719199419 68-A1-B7-03-07-B1:CMCC-EASY 120.196.100.82   4 0 240 0 2001363157984041  13660577991 5C-0E-8B-92-5C-20:CMCC-EASY 120.197.40.4 s19.cnzz.com 站点统计 24 9 6960 690 2001363157973098  15013685858 5C-0E-8B-C7-F7-90:CMCC 120.197.40.4 rank.ie.sogou.com 搜索引擎 28 27 3659 3538 2001363157986029  15989002119 E8-99-C4-4E-93-E0:CMCC-EASY 120.196.100.99 www.umeng.com 站点统计 3 3 1938 180 2001363157992093  13560439658 C4-17-FE-BA-DE-D9:CMCC 120.196.100.99   15 9 918 4938 2001363157986041  13480253104 5C-0E-8B-C7-FC-80:CMCC-EASY 120.197.40.4   3 3 180 180 2001363157984040  13602846565 5C-0E-8B-8B-B6-00:CMCC 120.197.40.4 2052.flash3-http.qq.com 综合门户 15 12 1938 2910 2001363157995093  13922314466 00-FD-07-A2-EC-BA:CMCC 120.196.100.82 img.qfc.cn  12 12 3008 3720 2001363157982040  13502468823 5C-0A-5B-6A-0B-D4:CMCC-EASY 120.196.100.99 y0.ifengimg.com 综合门户 57 102 7335 110349 2001363157986072  18320173382 84-25-DB-4F-10-1A:CMCC-EASY 120.196.100.99 input.shouji.sogou.com 搜索引擎 21 18 9531 2412 2001363157990043  13925057413 00-1F-64-E1-E6-9A:CMCC 120.196.100.55 t3.baidu.com 搜索引擎 69 63 11058 48243 2001363157988072  13760778710 00-FD-07-A4-7B-08:CMCC 120.196.100.82   2 2 120 120 2001363157985066  13726238888 00-FD-07-A4-72-B8:CMCC 120.196.100.82 i02.c.aliimg.com  24 27 2481 24681 2001363157993055  13560436666 C4-17-FE-BA-DE-D9:CMCC 120.196.100.99   18 15 1116 954 200

定义FlowBean类,该类实现WritableComparable接口

实现write(), readFields(), compareTo()方法

public class FlowBean implements WritableComparable { private String phoneNB;// 号码 private long up_flow;// 上行流量 private long down_flow;// 下行流量 private long sum_flow;// 总流量 public String getPhoneNB() {  return phoneNB; } public void setPhoneNB(String phoneNB) {  this.phoneNB = phoneNB; } public long getUp_flow() {  return up_flow; } public void setUp_flow(long up_flow) {  this.up_flow = up_flow; } public long getDown_flow() {  return down_flow; } public void setDown_flow(long down_flow) {  this.down_flow = down_flow; } public long getSum_flow() {  return sum_flow; } public void setSum_flow(long sum_flow) {  this.sum_flow = sum_flow; } public FlowBean() { } public FlowBean(String phoneNB, long up_flow, long down_flow) {  this.phoneNB = phoneNB;  this.up_flow = up_flow;  this.down_flow = down_flow;  this.sum_flow = up_flow + down_flow; } /**  * up_flow + "\t" + down_flow + "\t" + sum_flow  */ @Override public String toString() {  return up_flow + "\t" + down_flow + "\t" + sum_flow; } /**  * 序列化, 序列化与反序列化各属性顺序一致  */ @Override public void write(DataOutput out) throws IOException {  out.writeUTF(phoneNB);  out.writeLong(up_flow);  out.writeLong(down_flow);  out.writeLong(sum_flow); } /**  * 反序列化, 反序列化与序列化各属性顺序一致  */ @Override public void readFields(DataInput in) throws IOException {  phoneNB = in.readUTF();  up_flow = in.readLong();  down_flow = in.readLong();  sum_flow = in.readLong(); } /**  * 按总流量降序排序, 但总流量相等时, 两个FlowBean对象内容并不相等  */ @Override public int compareTo(FlowBean o) {  if (sum_flow == o.sum_flow) {   return 1;  }  return -Long.compare(sum_flow, o.sum_flow); }}

定义Mapper类TopKFlowMapper

并重写map方法

 public class TopKFlowMapper extends Mapper {  // mapper输出格式:   @Override  protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {   String line = value.toString();   String[] data = StringUtils.split(line, "\t");   String phoneNB = data[1];   long up_flow = Long.parseLong(data[7]);   long down_flow = Long.parseLong(data[8]);   context.write(new Text(phoneNB), new FlowBean(phoneNB, up_flow, down_flow));  } }

定义Reducer类TopKFlowReducer

并实现reduce(), 重写cleanup()方法

 public class TopKFlowReducer extends Reducer {  // 利用TreeMap的排序功能, 将FlowBean对象按总流量降序排序  private Map treeMap = new TreeMap();  private double globalFlow = 0;// 全局流量计数器, 初值值为0  // reducer输入格式:   @Override  protected void reduce(Text key, Iterable values, Context context)    throws IOException, InterruptedException {   long up_sum = 0;   long down_sum = 0;   for (FlowBean bean : values) {    up_sum += bean.getUp_flow();    down_sum += bean.getDown_flow();   }   // 每求得一条phoneNB的总流量, 就累加到全局流量计数器globalCount中   globalFlow += (up_sum + down_sum);   // 利用TreeMap的排序功能, 将FlowBean对象按总流量降序排序   treeMap.put(new FlowBean("", up_sum, down_sum), key.toString());  }  // cleanup方法是在reduce阶段退出前被调用一次  @Override  protected void cleanup(Context context) throws IOException, InterruptedException {   double itemCount = 0;   for (Map.Entry item : treeMap.entrySet()) {    if (itemCount > globalFlow * 0.8) {     return;    }    // 只输出全局流量计数器globalCount前80%的记录    context.write(new Text(item.getValue()), new VLongWritable(item.getKey().getSum_flow()));    itemCount += item.getKey().getSum_flow();   }  } }

测试TopK

public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {  Job job = Job.getInstance(new Configuration());  job.setJarByClass(TopKFlowRunner.class);    // 设置job的主类  job.setMapperClass(TopKFlowMapper.class);    // 设置Mapper类  job.setReducerClass(TopKFlowReducer.class);    // 设置Reducer类  job.setMapOutputKeyClass(Text.class);    // 设置map阶段输出Key的类型  job.setMapOutputValueClass(FlowBean.class);    // 设置map阶段输出Value的类型  job.setOutputKeyClass(Text.class);    // 设置reduce阶段输出Key的类型  job.setOutputValueClass(VLongWritable.class);    // 设置reduce阶段输出Value的类型  // 设置job输入路径(从main方法参数args中获取)  FileInputFormat.setInputPaths(job, new Path(args[0]));  // 设置job输出路径(从main方法参数args中获取)  FileOutputFormat.setOutputPath(job, new Path(args[1]));  job.waitForCompletion(true);    // 提交job }

job输出的结果文件:

13726230503 27162
13726238888 27162
13925057413 11121
18320173382 9549
13502468823 7437
13660577991 6969
13922314466 6728
13560439658 6292

看完上述内容，你们对MapReduce怎样实现TopK有进一步的了解吗？如果还想了解更多知识或者相关内容，请关注行业资讯频道，感谢大家的支持。