默认分区:HashPartition
影响因素:key.hashcode()、NumReducerTask
一、基础
1、目的
Reducer处理的结果按不同的条件,存储在不同的文件中
2、语法
a、自定义分区,继承Partitioner
b、分区在mapper后reducer前,因此数据类型和mapper一致
c、在driver中,job配置自定义分区和设置reducer数量
3、reducer数量
reducer数量为 1 ,分不分区都一样
reducer数量 > 1 < 分区的数量 报错
reducer数量 > 分区数量 浪费资源(reducer)
注意:分区号要从0开始,并逐一累加
二、案例
1、需求
手机号136、137、138、139开头都分别放到一个独立的4个文件中,其他开头的放到一个文件中
2、文件
1 13736230513 192.196.100.1 www.atguigu.com 2481 24681 200 2 13846544121 192.196.100.2 264 0 200 3 13956435636 192.196.100.3 132 1512 200 4 13966251146 192.168.100.1 240 0 404 5 18271575951 192.168.100.2 www.atguigu.com 1527 2106 200 6 84188413 192.168.100.3 www.atguigu.com 4116 1432 200 7 13590439668 192.168.100.4 1116 954 200 8 15910133277 192.168.100.5 www.hao123.com 3156 2936 200 9 13729199489 192.168.100.6 240 0 200 10 13630577991 192.168.100.7 www.shouhu.com 6960 690 200 11 15043685818 192.168.100.8 www.baidu.com 3659 3538 200 12 15959002129 192.168.100.9 www.atguigu.com 1938 180 500 13 13560439638 192.168.100.10 918 4938 200 14 13470253144 192.168.100.11 180 180 200 15 13682846555 192.168.100.12 www.qq.com 1938 2910 200 16 13992314666 192.168.100.13 www.gaga.com 3008 3720 200 17 13509468723 192.168.100.14 www.qinghua.com 7335 110349 404 18 18390173782 192.168.100.15 www.sogou.com 9531 2412 200 19 13975057813 192.168.100.16 www.baidu.com 11058 48243 200 20 13768778790 192.168.100.17 120 120 200 21 13568436656 192.168.100.18 www.alibaba.com 2481 24681 200 22 13568436656 192.168.100.19 1116 954 200
3、回顾自定义Hadoop序列化
a、自定义序列化类实现Writable接口
b、自定义属性
c、无参构造函数
d、get和set
e、tostring 连接使用 \t
f、序列化
g、反序列化
注意:序列化和反序列化的顺序相同,相当于队列
三、代码
1、自定义Hadoop序列化类
package com.flow2; import org.apache.hadoop.io.Writable; import java.io.DataInput; import java.io.DataOutput; import java.io.IOException; public class FlowBean implements Writable { private long upFlow; private long downFlow; private long sunFlow; public FlowBean() { } 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 getSunFlow() { return sunFlow; } public void setSunFlow(long sunFlow) { this.sunFlow = sunFlow; } public void setSum(long upFlow, long downFlow){ this.upFlow = upFlow; this.downFlow = downFlow; this.sunFlow = upFlow + downFlow; } @Override public String toString() { return upFlow + "\t" + downFlow + "\t" + sunFlow; } public void write(DataOutput out) throws IOException { out.writeLong(upFlow); out.writeLong(downFlow); out.writeLong(sunFlow); } public void readFields(DataInput in) throws IOException { this.upFlow = in.readLong(); this.downFlow = in.readLong(); this.sunFlow = in.readLong(); } }
2、Mapper
package com.flow2; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Mapper; import java.io.IOException; public class FlowMapper extends Mapper<LongWritable, Text, Text, FlowBean> { FlowBean v = new FlowBean(); Text k = new Text(); @Override protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { // 1. 行 String line = value.toString(); // 2. split String[] words = line.split("\t"); // 3.flow long upFlow = Long.parseLong(words[words.length -3]); long downFlow = Long.parseLong(words[words.length - 2]); v.setUpFlow(upFlow); v.setDownFlow(downFlow); // 4.key String phone = words[1]; k.set(phone); // 5.写入 context.write(k, v); } }
3、Reducer
package com.flow2; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Reducer; import java.io.IOException; public class FlowReducer extends Reducer<Text,FlowBean,Text,FlowBean> { FlowBean v = new FlowBean(); @Override protected void reduce(Text key, Iterable<FlowBean> values, Context context) throws IOException, InterruptedException { // 1. 累加,一个手机号有多条记录 long sumUp = 0; long sumDown = 0; for (FlowBean value : values) { sumUp += value.getUpFlow(); sumDown += value.getDownFlow(); } // 2. 设置 v v.setSum(sumUp, sumDown); // 3. 写入 context.write(key, v); } }
4、Driver
package com.flow2; 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; import java.io.IOException; public class FlowDriver { public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException { args = new String[]{"E:\\a\\input", "E:\\a\\output"}; // 1. job Configuration conf = new Configuration(); Job job = Job.getInstance(conf); // 2. 设置jar job.setJarByClass(FlowDriver.class); // 3. 设置 mapper 和 reducer类型 job.setMapperClass(FlowMapper.class); job.setReducerClass(FlowReducer.class); // 4. 设置 mapper输出 k v job.setMapOutputKeyClass(Text.class); job.setMapOutputValueClass(FlowBean.class); // 5. 设置 输出 k, v job.setOutputKeyClass(Text.class); job.setOutputValueClass(FlowBean.class); // 8. 设置 分区 和 Reduce 数量 job.setPartitionerClass(PhonePartition.class); job.setNumReduceTasks(5); // 6. 设置 输入输出 FileInputFormat.setInputPaths(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); // 7. 提交 job boolean wait = job.waitForCompletion(true); System.exit(wait? 0: 1); } }
注意:第8步,是后面补充的
// 8. 设置 分区 和 Reduce 数量 job.setPartitionerClass(PhonePartition.class); job.setNumReduceTasks(5);
5、Partition
package com.flow2; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Partitioner; public class PhonePartition extends Partitioner<Text, FlowBean> { /** * 1. 分区号 从 0 开始 * 2. partition数据类型 与 mapper的输出类型一致,partition 在 mapper后 reducer 前 * @param text * @param flowBean * @param numPartitions * @return */ public int getPartition(Text text, FlowBean flowBean, int numPartitions) { // 核心业务逻辑 // 1. 获取手机号 String phone = text.toString(); // 2. 判断 int partition; String substring = phone.substring(0, 3); if("136".equals(substring)){ partition = 0; }else if("137".equals(substring)){ partition = 1; }else if ("138".equals(substring)){ partition = 2; }else if ("139".equals(substring)){ partition = 3; }else { partition = 4; } return partition; } }