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 <phoneNB,sum_flow>
HTTP日志文件:
计算
安全
数据库
网络和加速
企业服务
1363157985066 13726230503 00-FD-07-A4-72-B8:CMCC 120.196.100.82 i02.c.aliimg.com 24 27 2481 24681 200
1363157995052 13826544101 5C-0E-8B-C7-F1-E0:CMCC 120.197.40.4 4 0 264 0 200
1363157991076 13926435656 20-10-7A-28-CC-0A:CMCC 120.196.100.99 2 4 132 1512 200
1363154400022 13926251106 5C-0E-8B-8B-B1-50:CMCC 120.197.40.4 4 0 240 0 200
1363157993044 18211575961 94-71-AC-CD-E6-18:CMCC-EASY 120.196.100.99 iface.qiyi.com 视频网站 15 12 1527 2106 200
1363157995074 84138413 5C-0E-8B-8C-E8-20:7DaysInn 120.197.40.4 122.72.52.12 20 16 4116 1432 200
1363157993055 13560439658 C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18 15 1116 954 200
1363157995033 15920133257 5C-0E-8B-C7-BA-20:CMCC 120.197.40.4 sug.so.360.cn 信息安全 20 20 3156 2936 200
1363157983019 13719199419 68-A1-B7-03-07-B1:CMCC-EASY 120.196.100.82 4 0 240 0 200
1363157984041 13660577991 5C-0E-8B-92-5C-20:CMCC-EASY 120.197.40.4 s19.cnzz.com 站点统计 24 9 6960 690 200
1363157973098 15013685858 5C-0E-8B-C7-F7-90:CMCC 120.197.40.4 rank.ie.sogou.com 搜索引擎 28 27 3659 3538 200
1363157986029 15989002119 E8-99-C4-4E-93-E0:CMCC-EASY 120.196.100.99 www.umeng.com 站点统计 3 3 1938 180 200
1363157992093 13560439658 C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 15 9 918 4938 200
1363157986041 13480253104 5C-0E-8B-C7-FC-80:CMCC-EASY 120.197.40.4 3 3 180 180 200
1363157984040 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 200
1363157995093 13922314466 00-FD-07-A2-EC-BA:CMCC 120.196.100.82 img.qfc.cn 12 12 3008 3720 200
1363157982040 13502468823 5C-0A-5B-6A-0B-D4:CMCC-EASY 120.196.100.99 y0.ifengimg.com 综合门户 57 102 7335 110349 200
1363157986072 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 200
1363157990043 13925057413 00-1F-64-E1-E6-9A:CMCC 120.196.100.55 t3.baidu.com 搜索引擎 69 63 11058 48243 200
1363157988072 13760778710 00-FD-07-A4-7B-08:CMCC 120.196.100.82 2 2 120 120 200
1363157985066 13726238888 00-FD-07-A4-72-B8:CMCC 120.196.100.82 i02.c.aliimg.com 24 27 2481 24681 200
1363157993055 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<FlowBean> {
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<LongWritable, Text, Text, FlowBean> {
// mapper输出格式: <phoneNB,{bean,bean,bean,.......}>
@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<Text, FlowBean, Text, VLongWritable> {
// 利用TreeMap的排序功能, 将FlowBean对象按总流量降序排序
private Map<FlowBean, String> treeMap = new TreeMap<FlowBean, String>();
private double globalFlow = 0;// 全局流量计数器, 初值值为0
// reducer输入格式: <phoneNB,{bean,bean,bean,.......}>
@Override
protected void reduce(Text key, Iterable<FlowBean> 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<FlowBean, String> 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，请关注亿速云行业资讯频道，感谢大家的支持。
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原文链接：https://my.oschina.net/u/2503731/blog/661135
