MapReduce分区和排序
一、排序
排序： 需求：根据用户每月使用的流量按照使用的流量多少排序 接口-->WritableCompareable 排序操作在hadoop中属于默认的行为。默认按照字典殊勋排序。 排序的分类： 1）部分排序 2）全排序 3）辅助排序 4）二次排序 Combiner 合并 父类Reducer 局部汇总 ，减少网络传输量 ，进而优化程序。 注意：求平均值？ 3 5 7 2 6 mapper: (3 + 5 + 7)/3 = 5 (2 + 6)/2 = 4 reducer:(5+4)/2 只能应用在不影响最终业务逻辑的情况下
二、分区和排序实例
1.Mapper类
package com.css.flowsort; import java.io.IOException; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Mapper; public class FlowSortMapper extends Mapper<LongWritable, Text, FlowBean, Text>{ @Override protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { // 1.获取一行数据 String line = value.toString(); // 2.切割 String[] fields = line.split("\t"); // 3.取出关键字段 long upFlow = Long.parseLong(fields[1]); long dfFlow = Long.parseLong(fields[2]); // 4.写出到reducer阶段 context.write(new FlowBean(upFlow, dfFlow), new Text(fields[0])); } }
2.Reducer类
package com.css.flowsort; import java.io.IOException; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Reducer; public class FlowSortReducer extends Reducer<FlowBean, Text, Text, FlowBean>{ @Override protected void reduce(FlowBean key, Iterable<Text> value, Context context) throws IOException, InterruptedException { context.write(value.iterator().next(), key); } }
3.封装类
package com.css.flowsort; import java.io.DataInput; import java.io.DataOutput; import java.io.IOException; import org.apache.hadoop.io.WritableComparable; // 封装类 直接完成排序 public class FlowBean implements WritableComparable<FlowBean> { // 定义属性 private long upFlow; private long dfFlow; private long flowSum; // 无参构造 public FlowBean() { } // 有参构造 public FlowBean(long upFlow,long dfFlow){ this.upFlow = upFlow; this.dfFlow = dfFlow; this.flowSum = upFlow + dfFlow; } public long getUpFlow() { return upFlow; } public void setUpFlow(long upFlow) { this.upFlow = upFlow; } public long getDfFlow() { return dfFlow; } public void setDfFlow(long dfFlow) { this.dfFlow = dfFlow; } public long getFlowSum() { return flowSum; } public void setFlowSum(long flowSum) { this.flowSum = flowSum; } // 反序列化 @Override public void readFields(DataInput in) throws IOException { upFlow = in.readLong(); dfFlow = in.readLong(); flowSum = in.readLong(); } // 序列化 @Override public void write(DataOutput out) throws IOException { out.writeLong(upFlow); out.writeLong(dfFlow); out.writeLong(flowSum); } @Override public String toString() { return upFlow + "\t" + dfFlow + "\t" + flowSum; } // 排序 @Override public int compareTo(FlowBean o) { // 倒序 return this.flowSum > o.getFlowSum() ? -1 : 1; } }
4.自定义分区类
package com.css.flowsort; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Partitioner; public class FlowSortPartitioner extends Partitioner<FlowBean, Text>{ // 根据手机号前三位进行分区 @Override public int getPartition(FlowBean key, Text value, int numPartitions) { // 获取手机号前三位 String phoneNum = value.toString().substring(0, 3); // 分区 int partitioner = 4; if ("135".equals(phoneNum)) { return 0; }else if ("137".equals(phoneNum)) { return 1; }else if ("138".equals(phoneNum)) { return 2; }else if ("139".equals(phoneNum)) { return 3; } return partitioner; } }
5.Driver类
package com.css.flowsort; import java.io.IOException; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.Path; 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; public class FlowSortDriver { public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException { // 1.获取job信息 Configuration conf = new Configuration(); Job job = Job.getInstance(conf); // 2.获取jar包 job.setJarByClass(FlowSortDriver.class); // 3.获取自定义的mapper与reducer类 job.setMapperClass(FlowSortMapper.class); job.setReducerClass(FlowSortReducer.class); // 4.设置map输出的数据类型 job.setMapOutputKeyClass(FlowBean.class); job.setMapOutputValueClass(Text.class); // 5.设置reduce输出的数据类型（最终的数据类型） job.setOutputKeyClass(Text.class); job.setOutputValueClass(FlowBean.class); //添加自定义分区 job.setPartitionerClass(FlowSortPartitioner.class); job.setNumReduceTasks(5); // 6.设置输入存在的路径与处理后的结果路径 FileInputFormat.setInputPaths(job, new Path("c:/flow1024/in")); FileOutputFormat.setOutputPath(job, new Path("c:/flow1024/out1")); // 7.提交任务 boolean rs = job.waitForCompletion(true); System.out.println(rs ? 0 : 1); } }
6.输入的文件part-r-00000
13480253104 120 1320 1440 13502468823 735 11349 12084 13510439658 1116 954 2070 13560436326 1136 94 1230 13560436666 1136 94 1230 13560439658 918 4938 5856 13602846565 198 910 1108 13660577991 660 690 1350 13719199419 240 0 240 13726130503 299 681 980 13726238888 2481 24681 27162 13760778710 120 120 240 13822544101 264 0 264 13884138413 4116 1432 5548 13922314466 3008 3720 6728 13925057413 11058 4243 15301 13926251106 240 0 240 13926435656 132 1512 1644 15013685858 369 338 707 15889002119 938 380 1318 15920133257 316 296 612 18212575961 1527 2106 3633 18320173382 9531 212 9743
7.如果第5步Driver类中的红色部分去掉，则输出全局排序后的文件part-r-00000
13726238888 2481 24681 27162 13925057413 11058 4243 15301 13502468823 735 11349 12084 18320173382 9531 212 9743 13922314466 3008 3720 6728 13560439658 918 4938 5856 13884138413 4116 1432 5548 18212575961 1527 2106 3633 13510439658 1116 954 2070 13926435656 132 1512 1644 13480253104 120 1320 1440 13660577991 660 690 1350 15889002119 938 380 1318 13560436326 1136 94 1230 13560436666 1136 94 1230 13602846565 198 910 1108 13726130503 299 681 980 15013685858 369 338 707 15920133257 316 296 612 13822544101 264 0 264 13760778710 120 120 240 13719199419 240 0 240 13926251106 240 0 240
8.如果第5步Driver类中的红色部分不去掉，则输出分区加排序后的文件
（1）part-r-00000 13502468823 735 11349 12084 13560439658 918 4938 5856 13510439658 1116 954 2070 13560436666 1136 94 1230 13560436326 1136 94 1230 （2）part-r-00001 13726238888 2481 24681 27162 13726130503 299 681 980 13760778710 120 120 240 13719199419 240 0 240 （3）part-r-00002 13884138413 4116 1432 5548 13822544101 264 0 264 （4）part-r-00003 13925057413 11058 4243 15301 13922314466 3008 3720 6728 13926435656 132 1512 1644 13926251106 240 0 240 （5）part-r-00004 18320173382 9531 212 9743 18212575961 1527 2106 3633 13480253104 120 1320 1440 13660577991 660 690 1350 15889002119 938 380 1318 13602846565 198 910 1108 15013685858 369 338 707 15920133257 316 296 612
