統計每一個使用者(手機號)所耗費的上行流量,下行流量,總流量
阿新 • • 發佈：2018-11-20
假設從資料運營商可以獲取使用者(通過手機號來區分)的上網資訊:
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 視訊網站 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 資訊保安 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 站點統計 24 9 6960 690 200 1363157973098 15013685858 5C-0E-8B-C7-F7-90:CMCC 120.197.40.4 rank.ie.sogou.com 搜尋引擎 28 27 3659 3538 200 1363157986029 15989002119 E8-99-C4-4E-93-E0:CMCC-EASY 120.196.100.99 www.umeng.com 站點統計 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.flash2-http.qq.com 綜合門戶 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 綜合門戶 57 102 7335 110349 200 1363157986072 18320173382 84-25-DB-4F-10-1A:CMCC-EASY 120.196.100.99 input.shouji.sogou.com 搜尋引擎 21 18 9531 2412 200 1363157990043 13925057413 00-1F-64-E1-E6-9A:CMCC 120.196.100.55 t3.baidu.com 搜尋引擎 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
上面的這些資料,第二列的資料代表的是手機號(通過手機號來區分使用者),從右邊數,右邊第三列代表上行流量,右邊第二列代表下行流量
我們來寫MapReduce程式來統計每個手機號的上行流量,下行流量,以及總流量,由於我們需要的是三個資料,所以我們可以將這三個資料封裝成一個Bean,這個Bean必須要實現hadoop的序列化介面.
package com.thp.bigdata.flowsum; import java.io.DataInput; import java.io.DataOutput; import java.io.IOException; import org.apache.hadoop.io.Writable; public class FlowBean implements Writable { private long upFlow; // 上行流量 private long downFlow; // 下行流量 private long sumFlow; // 總流量 // 反序列化時,需要反射呼叫空參建構函式,所以要顯式定義一個 public FlowBean() {} public FlowBean(long upFlow, long downFlow) { this.upFlow = upFlow; this.downFlow = downFlow; this.sumFlow = upFlow + downFlow; } public long getUpFlow() { return upFlow; } public void setUpFlow(long upFlow) { this.upFlow = upFlow; } public long getDownFlow() { return downFlow; } public void setDownFlow(long downFlow) { this.downFlow = downFlow; } public long getSumFlow() { return sumFlow; } public void setSumFlow(long sumFlow) { this.sumFlow = sumFlow; } /** * 序列化方法 */ @Override public void write(DataOutput out) throws IOException { out.writeLong(upFlow); out.writeLong(downFlow); out.writeLong(sumFlow); } /** * 反序列化方法: * 注意 : 反序列化的順序跟序列化的順序完全一致 */ @Override public void readFields(DataInput in) throws IOException { upFlow = in.readLong(); downFlow = in.readLong(); sumFlow = in.readLong(); } // 輸出列印的時候呼叫的是toString() 方法 @Override public String toString() { return upFlow + "\t" + downFlow + "\t" + sumFlow; } }
主程式,將Map task 跟 reduce task 全部寫在同一個類中,作為靜態內部類
package com.thp.bigdata.flowsum; import java.io.IOException; import org.apache.hadoop.conf.Configuration; 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 FlowCount { static class FlowCountMapper extends Mapper<LongWritable, Text, Text, FlowBean> { @Override protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { // 將一行內容轉換成string String line = value.toString(); // 切分欄位 String[] fields = line.split("\t"); // 取出手機號 String phoneNumber = fields[1]; // 取出上行流量和下行流量 long upFlow = Long.parseLong(fields[fields.length - 3]); long downFlow = Long.parseLong(fields[fields.length - 2]); context.write(new Text(phoneNumber), new FlowBean(upFlow, downFlow)); } } static class FlowCountReducer extends Reducer<Text, FlowBean, Text, FlowBean> { @Override protected void reduce(Text key, Iterable<FlowBean> values, Context context) throws IOException, InterruptedException { long sum_upFlow = 0; long sum_downFlow = 0; // 遍歷所有的bean,將其中的上行流量,下行流量分別累加 for(FlowBean bean : values) { sum_upFlow += bean.getUpFlow(); sum_downFlow += bean.getDownFlow(); } FlowBean resultBean = new FlowBean(sum_upFlow, sum_downFlow); context.write(key, resultBean); } } public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException { Configuration conf = new Configuration(); Job job = Job.getInstance(conf); job.setJarByClass(FlowCount.class); job.setMapperClass(FlowCountMapper.class); job.setReducerClass(FlowCountReducer.class); job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(FlowBean.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(FlowBean.class); FileInputFormat.setInputPaths(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); boolean res = job.waitForCompletion(true); System.exit(res ? 0 : 1); } }
將資料上傳到hadoop的hdfs檔案系統
將寫的整個專案打成jar包放在hadoop叢集上.
hadoop jar mapReduce.jar com.thp.bigdata.flowsum.FlowCount /flowsum/input /flowsum/output
最後生成的檔案:
MapTask並行度決定機制:
maptask的並行度決定map階段的任務處理併發度,進而影響到整個job的處理速度,那麼,maptask並行例項是否越多越好呢?其並行度又是如何決定呢?