淮南 搭建一个企业展示网站,建设一个网站要学什么,做的比较简约的网站,成功的网络营销案例ppt先简单介绍一下partitioner 和 combiner
Partitioner类
用于在Map端对key进行分区 默认使用的是HashPartitioner 获取key的哈希值使用key的哈希值对Reduce任务数求模决定每条记录应该送到哪个Reducer处理自定义Partitioner 继承抽象类Partitioner#xff0c;重写getPartiti…先简单介绍一下partitioner 和 combiner
Partitioner类
用于在Map端对key进行分区 默认使用的是HashPartitioner 获取key的哈希值使用key的哈希值对Reduce任务数求模决定每条记录应该送到哪个Reducer处理自定义Partitioner 继承抽象类Partitioner重写getPartition方法job.setPartitionerClass(MyPartitioner.class)
Combiner类
Combiner相当于本地化的Reduce操作 在shuffle之前进行本地聚合用于性能优化可选项输入和输出类型一致Reducer可以被用作Combiner的条件 符合交换律和结合律实现Combiner job.setCombinerClass(WCReducer.class) 我们进入案例来看这两个知识点
一 案例需求
一个存放电话号码的文本我们需要136 137138 139和其它开头的号码分开存放统计其每个数字开头的号码个数 效果 二 PhoneMapper 类
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;import java.io.IOException;public class PhoneMapper extends MapperLongWritable, Text,Text, IntWritable {Overrideprotected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {String phone value.toString();Text text new Text(phone);IntWritable intWritable new IntWritable(1);context.write(text,intWritable);}
}三 PhoneReducer 类
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;import java.io.IOException;public class PhoneReducer extends ReducerText, IntWritable,Text,IntWritable {Overrideprotected void reduce(Text key, IterableIntWritable values, Context context) throws IOException, InterruptedException {int count 0;for (IntWritable intWritable : values){count intWritable.get();}context.write(key, new IntWritable(count));}
}四 PhonePartitioner 类
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Partitioner;public class PhonePartitioner extends PartitionerText, IntWritable {Overridepublic int getPartition(Text text, IntWritable intWritable, int i) {//136,137 138,139 其它号码放一起if(136.equals(text.toString().substring(0,3)) || 137.equals(text.toString().substring(0,3))){return 0;}else if (138.equals(text.toString().substring(0,3)) || 139.equals(text.toString().substring(0,3))){return 1;}else {return 2;}}
}五 PhoneCombiner 类
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;import java.io.IOException;public class PhoneCombiner extends ReducerText, IntWritable,Text,IntWritable {Overrideprotected void reduce(Text key, IterableIntWritable values, Context context) throws IOException, InterruptedException {int count 0;for(IntWritable intWritable : values){count intWritable.get();}context.write(new Text(key.toString().substring(0,3)), new IntWritable(count));}
}六 PhoneDriver 类
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
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 PhoneDriver {public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {Configuration conf new Configuration();Job job Job.getInstance(conf);job.setJarByClass(PhoneDriver.class);job.setMapperClass(PhoneMapper.class);job.setMapOutputKeyClass(Text.class);job.setMapOutputValueClass(IntWritable.class);job.setCombinerClass(PhoneCombiner.class);job.setPartitionerClass(PhonePartitioner.class);job.setNumReduceTasks(3);job.setReducerClass(PhoneReducer.class);job.setOutputKeyClass(Text.class);job.setOutputValueClass(IntWritable.class);Path inPath new Path(in/demo4/phone.csv);FileInputFormat.setInputPaths(job, inPath);Path outPath new Path(out/out6);FileSystem fs FileSystem.get(outPath.toUri(),conf);if (fs.exists(outPath)){fs.delete(outPath, true);}FileOutputFormat.setOutputPath(job, outPath);job.waitForCompletion(true);}
}七 小结
该案例新知识点在于分区partition和结合combine
这次代码的流程是
driver——》mapper——》partitioner——》combiner——》reducer
map 每处理一条数据都经过一次 partitioner 分区然后存到环形缓存区中去然后map再去处理下一条数据以此反复直至所有数据处理完成
combine 则是将环形缓存区溢出的缓存文件合并并提前进行一次排序和计算对每个溢出文件计算后再合并最后将一个大的文件给到 reducer这样大大减少了 reducer 的计算负担
