大數據技術之流量匯總案例
7.2 流量匯總程序案例
7.2.1 需求1:統計手機號耗費的總上行流量、下行流量、總流量(序列化)
1)需求: 統計每一個手機號耗費的總上行流量、下行流量、總流量
2)數據準備 phone_date.txt
13726230503 00-FD-07-A4-72-B8:CMCC 120.196.100.82 i02.c.aliimg.com 24 27 2481 24681 200 13826544101 5C-0E-8B-C7-F1-E0:CMCC 120.197.40.4 4 0 264 0200 13926435656 20-10-7A-28-CC-0A:CMCC 120.196.100.99 2 4 132 1512 200 13926251106 5C-0E-8B-8B-B1-50:CMCC 120.197.40.4 4 0 240 0 200 18211575961 94-71-AC-CD-E6-18:CMCC-EASY 120.196.100.99 iface.qiyi.com 視頻網站 15 12 1527 2106200 84138413 5C-0E-8B-8C-E8-20:7DaysInn 120.197.40.4 122.72.52.12 20 16 4116 1432 200 13560439658 C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18 15 1116 954 200 15920133257 5C-0E-8B-C7-BA-20:CMCC 120.197.40.4 sug.so.360.cn 信息安全 20 20 31562936 200 13719199419 68-A1-B7-03-07-B1:CMCC-EASY 120.196.100.82 4 0 240 0 200 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 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 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 13560439658 C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 15 9 918 4938 200 13480253104 5C-0E-8B-C7-FC-80:CMCC-EASY 120.197.40.4 3 3 180 180 200 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 13922314466 00-FD-07-A2-EC-BA:CMCC 120.196.100.82 img.qfc.cn 12 12 3008 3720 200 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 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 13925057413 00-1F-64-E1-E6-9A:CMCC 120.196.100.55 t3.baidu.com 搜索引擎 69 63 11058 48243 200 13760778710 00-FD-07-A4-7B-08:CMCC 120.196.100.82 2 2 120 120 200 13560436666 00-FD-07-A4-72-B8:CMCC 120.196.100.82 i02.c.aliimg.com 24 27 2481 24681 200 13560436666 C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18 15 1116 954 200
輸入數據格式:
1363157993055 13560436666 C4-17-FE-BA-DE-D9:CMCC 120.196.100.99 18 15 1116 954 200
手機號碼 上行流量 下行流量
輸出數據格式
13560436666 1116 954 2070
手機號碼 上行流量 下行流量 總流量
3)分析
基本思路:
Map階段:
(1)讀取一行數據,切分字段
(2)抽取手機號、上行流量、下行流量
(3)以手機號為key,bean對象為value輸出,即context.write(手機號,bean);
Reduce階段:
(1)累加上行流量和下行流量得到總流量。
(2)實現自定義的bean來封裝流量信息,並將bean作為map輸出的key來傳輸
(3)MR程序在處理數據的過程中會對數據排序(map輸出的kv對傳輸到reduce之前,會排序),排序的依據是map輸出的key
所以,我們如果要實現自己需要的排序規則,則可以考慮將排序因素放到key中,讓key實現接口:WritableComparable。
然後重寫key的compareTo方法。
4)編寫mapreduce程序
(1)編寫流量統計的bean對象
package com.xyg.mr.flowsum; import java.io.DataInput; import java.io.DataOutput; import java.io.IOException; import org.apache.hadoop.io.Writable; // bean對象要實例化 public class FlowBean implements Writable { private long upFlow; private long downFlow; private long sumFlow; // 反序列化時,需要反射調用空參構造函數,所以必須有 public FlowBean() { super(); } public FlowBean(long upFlow, long downFlow) { super(); this.upFlow = upFlow; this.downFlow = downFlow; this.sumFlow = upFlow + downFlow; } public long getSumFlow() { return sumFlow; } public void setSumFlow(long sumFlow) { this.sumFlow = sumFlow; } 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; } /** * 序列化方法 * * @param out * @throws IOException */ @Override public void write(DataOutput out) throws IOException { out.writeLong(upFlow); out.writeLong(downFlow); out.writeLong(sumFlow); } /** * 反序列化方法 註意反序列化的順序和序列化的順序完全一致 * * @param in * @throws IOException */ @Override public void readFields(DataInput in) throws IOException { upFlow = in.readLong(); downFlow = in.readLong(); sumFlow = in.readLong(); } @Override public String toString() { return upFlow + "\t" + downFlow + "\t" + sumFlow; } }
(2)編寫mapreduce主程序
package com.xyg.mr.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 { // 1 將一行內容轉成string String ling = value.toString(); // 2 切分字段 String[] fields = ling.split("\t"); // 3 取出手機號碼 String phoneNum = fields[1]; // 4 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]); // 5 寫出數據 context.write(new Text(phoneNum), 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; // 1 遍歷所用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(); } // 2 封裝對象 FlowBean resultBean = new FlowBean(sum_upFlow, sum_downFlow); context.write(key, resultBean); } } public static void main(String[] args) throws Exception { // 1 獲取配置信息,或者job對象實例 Configuration configuration = new Configuration(); Job job = Job.getInstance(configuration); // 6 指定本程序的jar包所在的本地路徑 job.setJarByClass(FlowCount.class); // 2 指定本業務job要使用的mapper/Reducer業務類 job.setMapperClass(FlowCountMapper.class); job.setReducerClass(FlowCountReducer.class); // 3 指定mapper輸出數據的kv類型 job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(FlowBean.class); // 4 指定最終輸出的數據的kv類型 job.setOutputKeyClass(Text.class); job.setOutputValueClass(FlowBean.class); // 5 指定job的輸入原始文件所在目錄 FileInputFormat.setInputPaths(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); // 7 將job中配置的相關參數,以及job所用的java類所在的jar包, 提交給yarn去運行 boolean result = job.waitForCompletion(true); System.exit(result ? 0 : 1); } }
(3)將程序打成jar包,然後拷貝到hadoop集群中。
(4)啟動hadoop集群(3)將程序打成jar包,然後拷貝到hadoop集群中。
(5)執行flowcount程序
[root@node21 ~]$ hadoop jar flowcount.jar com.xyg.mr.flowsum.FlowCount /user/root/flowcount/input/ /user/root/flowcount/output
(6)查看結果
[root@node21 ~]$ hadoop fs -cat /user/root/flowcount/output/part-r-00000
13480253104 FlowBean [upFlow=180, downFlow=180, sumFlow=360]
13502468823 FlowBean [upFlow=7335, downFlow=110349, sumFlow=117684]
13560436666 FlowBean [upFlow=1116, downFlow=954, sumFlow=2070]
13560439658 FlowBean [upFlow=2034, downFlow=5892, sumFlow=7926]
13602846565 FlowBean [upFlow=1938, downFlow=2910, sumFlow=4848]
。。。
7.2.2 需求2:將統計結果按照手機歸屬地不同省份輸出到不同文件中(Partitioner)
0)需求:將統計結果按照手機歸屬地不同省份輸出到不同文件中(分區)
1)數據準備 phone_date.txt
2)分析
(1)Mapreduce中會將map輸出的kv對,按照相同key分組,然後分發給不同的reducetask。默認的分發規則為:根據key的hashcode%reducetask數來分發
(2)如果要按照我們自己的需求進行分組,則需要改寫數據分發(分組)組件Partitioner
自定義一個CustomPartitioner繼承抽象類:Partitioner
(3)在job驅動中,設置自定義partitioner: job.setPartitionerClass(CustomPartitioner.class)
3)在需求1的基礎上,增加一個分區類
package com.xyg.mr.partitioner; import java.util.HashMap; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Partitioner; /** * K2 V2 對應的是map輸出kv類型 * @author Administrator */ public class ProvincePartitioner extends Partitioner<Text, FlowBean> { @Override public int getPartition(Text key, FlowBean value, int numPartitions) { // 1 獲取電話號碼的前三位 String preNum = key.toString().substring(0, 3); int partition = 4; // 2 判斷是哪個省 if ("136".equals(preNum)) { partition = 0; }else if ("137".equals(preNum)) { partition = 1; }else if ("138".equals(preNum)) { partition = 2; }else if ("139".equals(preNum)) { partition = 3; } return partition; } }
2)在驅動函數中增加自定義數據分區設置和reduce task設置
public static void main(String[] args) throws Exception { // 1 獲取配置信息,或者job對象實例 Configuration configuration = new Configuration(); Job job = Job.getInstance(configuration); // 6 指定本程序的jar包所在的本地路徑 job.setJarByClass(FlowCount.class); // 8 指定自定義數據分區 job.setPartitionerClass(ProvincePartitioner.class); // 9 同時指定相應數量的reduce task job.setNumReduceTasks(5); // 2 指定本業務job要使用的mapper/Reducer業務類 job.setMapperClass(FlowCountMapper.class); job.setReducerClass(FlowCountReducer.class); // 3 指定mapper輸出數據的kv類型 job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(FlowBean.class); // 4 指定最終輸出的數據的kv類型 job.setOutputKeyClass(Text.class); job.setOutputValueClass(FlowBean.class); // 5 指定job的輸入原始文件所在目錄 FileInputFormat.setInputPaths(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); // 7 將job中配置的相關參數,以及job所用的java類所在的jar包, 提交給yarn去運行 boolean result = job.waitForCompletion(true); System.exit(result ? 0 : 1); }
3)將程序打成jar包,然後拷貝到hadoop集群中。
4)啟動hadoop集群
5)執行flowcountPartitionser程序
[root@node21 ~]$ hadoop jar flowcountPartitionser.jar com.xyg.mr.partitioner.FlowCount /user/root/flowcount/input /user/root/flowcount/output
6)查看結果
[root@node21 ~]]$ hadoop fs -lsr /
/user/root/flowcount/output/part-r-00000
/user/root/flowcount/output/part-r-00001
/user/root/flowcount/output/part-r-00002
/user/root/flowcount/output/part-r-00003
/user/root/flowcount/output/part-r-00004
7.2.3 需求3:將統計結果按照總流量倒序排序(全排序)
0)需求 根據需求1產生的結果再次對總流量進行排序。
1)數據準備 phone_date.txt
2)分析
(1)把程序分兩步走,第一步正常統計總流量,第二步再把結果進行排序
(2)context.write(總流量,手機號)
(3)FlowBean實現WritableComparable接口重寫compareTo方法
@Override
public int compareTo(FlowBean o) {
// 倒序排列,從大到小
return this.sumFlow > o.getSumFlow() ? -1 : 1;
}
package com.xyg.mr.sort; import java.io.DataInput; import java.io.DataOutput; import java.io.IOException; import org.apache.hadoop.io.WritableComparable; public class FlowBean implements WritableComparable<FlowBean> { private long upFlow; private long downFlow; private long sumFlow; // 反序列化時,需要反射調用空參構造函數,所以必須有 public FlowBean() { super(); } public FlowBean(long upFlow, long downFlow) { super(); this.upFlow = upFlow; this.downFlow = downFlow; this.sumFlow = upFlow + downFlow; } public void set(long upFlow, long downFlow) { this.upFlow = upFlow; this.downFlow = downFlow; this.sumFlow = upFlow + downFlow; } public long getSumFlow() { return sumFlow; } public void setSumFlow(long sumFlow) { this.sumFlow = sumFlow; } 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; } /** * 序列化方法 * @param out * @throws IOException */ @Override public void write(DataOutput out) throws IOException { out.writeLong(upFlow); out.writeLong(downFlow); out.writeLong(sumFlow); } /** * 反序列化方法 註意反序列化的順序和序列化的順序完全一致 * @param in * @throws IOException */ @Override public void readFields(DataInput in) throws IOException { upFlow = in.readLong(); downFlow = in.readLong(); sumFlow = in.readLong(); } @Override public String toString() { return upFlow + "\t" + downFlow + "\t" + sumFlow; } @Override public int compareTo(FlowBean o) { // 倒序排列,從大到小 return this.sumFlow > o.getSumFlow() ? -1 : 1; } }
4)Map方法優化為一個對象,reduce方法則直接輸出結果即可,驅動函數根據輸入輸出重寫配置即可。3)FlowBean對象在在需求1基礎上增加了比較功能
package com.xyg.mr.sort; 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 FlowCountSort { static class FlowCountSortMapper extends Mapper<LongWritable, Text, FlowBean, Text>{ FlowBean bean = new FlowBean(); Text v = new Text(); @Override protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { // 1 拿到的是上一個統計程序輸出的結果,已經是各手機號的總流量信息 String line = value.toString(); // 2 截取字符串並獲取電話號、上行流量、下行流量 String[] fields = line.split("\t"); String phoneNbr = fields[0]; long upFlow = Long.parseLong(fields[1]); long downFlow = Long.parseLong(fields[2]); // 3 封裝對象 bean.set(upFlow, downFlow); v.set(phoneNbr); // 4 輸出 context.write(bean, v); } } static class FlowCountSortReducer extends Reducer<FlowBean, Text, Text, FlowBean>{ @Override protected void reduce(FlowBean bean, Iterable<Text> values, Context context) throws IOException, InterruptedException { context.write(values.iterator().next(), bean); } } public static void main(String[] args) throws Exception { // 1 獲取配置信息,或者job對象實例 Configuration configuration = new Configuration(); Job job = Job.getInstance(configuration); // 6 指定本程序的jar包所在的本地路徑 job.setJarByClass(FlowCountSort.class); // 2 指定本業務job要使用的mapper/Reducer業務類 job.setMapperClass(FlowCountSortMapper.class); job.setReducerClass(FlowCountSortReducer.class); // 3 指定mapper輸出數據的kv類型 job.setMapOutputKeyClass(FlowBean.class); job.setMapOutputValueClass(Text.class); // 4 指定最終輸出的數據的kv類型 job.setOutputKeyClass(Text.class); job.setOutputValueClass(FlowBean.class); // 5 指定job的輸入原始文件所在目錄 FileInputFormat.setInputPaths(job, new Path(args[0])); Path outPath = new Path(args[1]); // FileSystem fs = FileSystem.get(configuration); // if (fs.exists(outPath)) { // fs.delete(outPath, true); // } FileOutputFormat.setOutputPath(job, outPath); // 7 將job中配置的相關參數,以及job所用的java類所在的jar包, 提交給yarn去運行 boolean result = job.waitForCompletion(true); System.exit(result ? 0 : 1); } }
5)將程序打成jar包,然後拷貝到hadoop集群中。
6)啟動hadoop集群5)將程序打成jar包,然後拷貝到hadoop集群中。
7)執行flowcountsort程序
[root@node21 module]$ hadoop jar flowcountsort.jar com.xyg.mr.sort.FlowCountSort /user/root/flowcount/output /user/root/flowcount/output_sort
8)查看結果
[root@node21 module]$ hadoop fs -cat /user/flowcount/output_sort/part-r-00000
13502468823 7335 110349 117684
13925057413 11058 48243 59301
13726238888 2481 24681 27162
13726230503 2481 24681 27162
18320173382 9531 2412 11943
7.2.4 需求4:不同省份輸出文件內部排序(部分排序)
1)需求 要求每個省份手機號輸出的文件中按照總流量內部排序。
2)分析 基於需求3,增加自定義分區類即可。
3)案例實操
(1)增加自定義分區類
package com.xyg.reduce.flowsort; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Partitioner; public class FlowSortPartitioner extends Partitioner<FlowBean, Text> { @Override public int getPartition(FlowBean key, Text value, int numPartitions) { int partition = 0; String preNum = value.toString().substring(0, 3); if (" ".equals(preNum)) { partition = 5; } else { if ("136".equals(preNum)) { partition = 1; } else if ("137".equals(preNum)) { partition = 2; } else if ("138".equals(preNum)) { partition = 3; } else if ("139".equals(preNum)) { partition = 4; } } return partition; } }
(2)在驅動類中添加分區類
job.setPartitionerClass(FlowSortPartitioner.class);
job.setNumReduceTasks(5);
大數據技術之流量匯總案例