ChainMapper/ChainReducer实现原理及案例分析

怼烎@ 2022-06-10 12:51 159阅读 0赞

# ChainMapper/ChainReducer实现原理及案例分析 #

## ChainMapper/ChainReducer的实现原理 ##

ChainMapper/ChainReducer主要为了解决线性链式Mapper而提出的。也就是说，在Map或者Reduce阶段存在多个Mapper，这些Mapper像linux管道一样，前一个Mapper的输出结果直接重定向到下一个Mapper的输入，形成一个流水线，形式类似于`[MAP + REDUCE MAP*]`。下图展示了一个典型的ChainMapper/ChainReducer的应用场景。  
在Map阶段，数据依次经过Mapper1和Mapper2处理；在Reducer阶段，数据经过shuffle和sort排序后，交给对应的Reduce处理，但Reducer处理之后还可以交给其它的Mapper进行处理，最终产生的结果写入到hdfs输出目录上。

![hadoop_ChainMapper_ChainReducer_1.png][]

<table> 
 <thead> 
  <tr> 
   <th align="left"><strong>注意</strong>：对于任意一个MapReduce作业，Map和Reduce阶段可以有无限多个Mapper，但是Reducer只能有一个。</th> 
  </tr> 
 </thead> 
</table>

通过链式MapReducer模式可以有效的减少网络间传输数据的带宽，因为大量的计算基本都是在本地进行的。如果通过迭代作业的方式实现多个MapReduce作业组合的话就会在网络间传输大量的数据，这样会非常的耗时。

## ChainMapper官方说明 ##

ChainMapper类允许使用多个Map子类作为一个Map任务。  
  
这些map子类的执行与liunx的管道命令十分相似，第一个map的输出会成为第二个map的输入，第二个map的输出也会变成第三个map的输入，以此类推，直到最后一个map的输出会变成整个mapTask的输出。  
  
该特性的关键功能是链中的Mappers不需要知道它们是在链中执行的。这使具有可重用的专门的映射器可以组合起来，在单个任务中执行组合操作。  
  
注意:在创建链式是每个Mapper的键/值的输出是链中下一个Mapper或Reducer的输入。它假定所有的映射器和链中的Reduce都使用匹配输出和输入键和值类，因为没有对链接代码进行转换。

### 使用方法 ###

...
    Job = Job.getInstance(conf);
    
    Configuration mapAConf = new Configuration(false);
    ...
    ChainMapper.addMapper(job, AMap.class, LongWritable.class, Text.class,
     Text.class, Text.class, true, mapAConf);
    
    Configuration mapBConf = new Configuration(false);
    ...
    ChainMapper.addMapper(job, BMap.class, Text.class, Text.class,
     LongWritable.class, Text.class, false, mapBConf);
    
    ...
    
    job.waitForComplettion(true);
       ...

### addMapper函数的参数说明 ###

static void addMapper(Job job, Class<? extends Mapper> klass,
      Class<?> inputKeyClass, Class<?> inputValueClass,
      Class<?> outputKeyClass, Class<?> outputValueClass,
      Configuration mapperConf)
    ## 参数的含义如下
    # 1. job
    # 2. 此map的class
    # 3. 此map的输入的key类型
    # 4. 此map的输入的value类型
    # 5. 此map的输出的key类型
    # 6. 此map的输出的value类型
    # 7. 此map的配置文件类conf

## ChainReducer官方说明 ##

ChainReducer类允许多个map在reduce执行完之后执行在一个reducerTask中，  
reducer的每一条输出，都被作为输入给ChainReducer类设置的第一个map，然后第一个map的输出作为第二个map的输入，以此类推，最后一个map的输出会作为整个reducerTask的输出，写到磁盘上。

### 使用方法 ###

...
    Job = new Job(conf);
    ....
    
    Configuration reduceConf = new Configuration(false);
    ...
    ChainReducer.setReducer(job, XReduce.class, LongWritable.class, Text.class,
      Text.class, Text.class, true, reduceConf);
    
    ChainReducer.addMapper(job, CMap.class, Text.class, Text.class,
      LongWritable.class, Text.class, false, null);
    
    ChainReducer.addMapper(job, DMap.class, LongWritable.class, Text.class,
      LongWritable.class, LongWritable.class, true, null);
    
    ...
    
    job.waitForCompletion(true);
    ...

### setReducer函数的参数说明 ###

static void setReducer(Job job, Class<? extends Reducer> klass,
     Class<?> inputKeyClass, Class<?> inputValueClass,
      Class<?> outputKeyClass, Class<?> outputValueClass,
       Configuration reducerConf)
    ## 参数的含义如下
    # 1. job
    # 2. 此reducer的class
    # 3. 此reducer的输入的key类型
    # 4. 此reducer的输入的value类型
    # 5. 此reducer的输出的key类型
    # 6. 此reducer的输出的value类型
    # 7. 此reducer的配置文件类conf

## 案例 ##

### 案例描述 ###

统计出一篇文章的高频词汇（只收集出现次数大于3的单词），去除谓词，并且过滤掉敏感词汇。

### 实现方法 ###

在MapTask中有三个子Mapper，分别命名为M1,M2,M3，在ReduceTask阶段有一个Reduce命名为R1和一个Mpaaer命名为RM1。

**MapTask阶段**

> M1负责将文本内容按行切分每个单词，M2负责将M1输出的单词进行谓词过滤，M3将M2输出的内容进行敏感词过滤。

**ReduceTask阶段**

> Reduce过程中R1负责将shuffle阶段中的单词进行统计，统计好之后将结果交给RM1处理，RM1主要是将单词数量大于5的单词进行输出。

上述方法只是为了展示ChainMapper/ReducerMapper的使用过程，所以观者勿喷。

### 代码 ###

#### Mapper1 ####

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 Mapper1 extends Mapper<LongWritable, Text, Text, IntWritable> { 
        @Override
        protected void setup(Context context) throws IOException, InterruptedException {
            System.out.println("Mapper1 setup===========");
        }
    
        @Override
        protected void map(LongWritable key, Text value, Context context)
        throws IOException, InterruptedException {
            System.out.println("map1===========" + value.toString());
            String line = value.toString() ;
            String[] strArr = line.split(" ") ;
    
            for (String w: strArr) {
                context.write(new Text(w), new IntWritable(1));
            }
        }
    
        @Override
        protected void cleanup(Context context) throws IOException, InterruptedException {
            System.out.println("Mapper1 cleanup===========");
        }
    }

#### Mapper2 ####

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;
    
    /** * 该Mapper是用于过滤谓词，但是过滤单词不是本文的关键，所以为了演示方便 * 这里只过滤一个单词‘of’ */
    public class Mapper2 extends Mapper<Text, IntWritable, Text, IntWritable> { 
        protected void setup(Context context) throws IOException, InterruptedException {
            System.out.println("Mapper2 setup===========");
        }
        @Override
        protected void map(Text key, IntWritable value, Context context)
        throws IOException, InterruptedException {
            System.out.println("map2==================" + key.toString() + ":" + value.toString());
            //过滤单词'of'
            if (! key.toString().equals("of")){
                context.write(key, value);
            }
        }
    
        @Override
        protected void cleanup(Context context) throws IOException, InterruptedException {
            System.out.println("Mapper2 cleanup===========");
        }
    }

#### Mapper3 ####

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;
    
    /** * 该Mapper是用于过滤敏感词汇，但是过滤单词不是本文的关键，所以为了演示方便 * 这里只过滤一个单词‘xxx’ */
    public class Mapper3 extends Mapper<Text, IntWritable, Text, IntWritable> { 
        protected void setup(Context context) throws IOException, InterruptedException {
            System.out.println("Mapper3 setup===========");
        }
        @Override
        protected void map(Text key, IntWritable value, Context context)
        throws IOException, InterruptedException {
            System.out.println("map3==================" + key.toString() + ":" + value.toString());
            //过滤单词'google'
            if (! key.toString().equals("xxx")){
                context.write(key, value);
            }
        }
    
        @Override
        protected void cleanup(Context context) throws IOException, InterruptedException {
            System.out.println("Mapper3 cleanup===========");
        }
    }

#### Reducer1 ####

import org.apache.hadoop.io.IntWritable;
    import org.apache.hadoop.io.Text;
    import org.apache.hadoop.mapreduce.Reducer;
    
    import java.io.IOException;
    
    /** * Created by yanzhe on 2017/8/18. */
    public class Reducer1 extends Reducer<Text, IntWritable, Text, IntWritable> { 
        @Override
        protected void setup(Context context) throws IOException, InterruptedException {
            System.out.println("Reducer1 setup===========");
        }
    
        @Override
        protected void reduce(Text key, Iterable<IntWritable> values, Context context)
        throws IOException, InterruptedException {
            int count = 0 ;
            for (IntWritable iw: values) {
                count += iw.get();
            }
            context.write(key, new IntWritable(count));
            System.out.println("reduce=========" + key.toString() + ":" + count);
        }
    
        @Override
        protected void cleanup(Context context) throws IOException, InterruptedException {
            System.out.println("Reducer1 cleanup===========");
        }
    }

#### ReduceMapper ####

import org.apache.hadoop.io.IntWritable;
    import org.apache.hadoop.io.Text;
    import org.apache.hadoop.mapreduce.Mapper;
    
    import java.io.IOException;
    
    /** * Created by yanzhe on 2017/8/18. */
    public class ReducerMapper1 extends Mapper<Text, IntWritable, Text, IntWritable> { 
        @Override
        protected void setup(Context context) throws IOException, InterruptedException {
            System.out.println("ReducerMapper1 setup===========");
        }
    
        @Override
        protected void map(Text key, IntWritable value, Context context)
        throws IOException, InterruptedException {
            if (value.get() > 5)
                context.write(key, value);
    
            System.out.println("reduceMap======" + key.toString() + ":" + value.toString());
        }
    
        @Override
        protected void cleanup(Context context) throws IOException, InterruptedException {
            System.out.println("ReducerMapper1 cleanup===========");
        }
    }

#### App ####

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.LongWritable;
    import org.apache.hadoop.io.Text;
    import org.apache.hadoop.mapreduce.Job;
    import org.apache.hadoop.mapreduce.lib.chain.ChainMapper;
    import org.apache.hadoop.mapreduce.lib.chain.ChainReducer;
    import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
    import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
    
    import java.io.IOException;
    
    /** * Created by yanzhe on 2017/8/18. */
    public class App { 
        public static void main(String[] args) throws Exception {
    
            args = new String[]{
       "d:/java/mr/data/data.txt", "d:/java/mr/out"} ;
    
            Configuration conf = new Configuration();
    
            FileSystem fs = FileSystem.get(conf) ;
    
            Path outPath = new Path(args[1]) ;
            if (fs.exists(outPath)){
                fs.delete(outPath,true) ;
            }
    
            Job job = Job.getInstance(conf) ;
    
            ChainMapper.addMapper(job,Mapper1.class, LongWritable.class, Text.class, Text.class, IntWritable.class,job.getConfiguration());
    
            ChainMapper.addMapper(job,Mapper2.class, Text.class,IntWritable.class, Text.class, IntWritable.class,job.getConfiguration());
    
            ChainMapper.addMapper(job,Mapper3.class, Text.class,IntWritable.class, Text.class, IntWritable.class,job.getConfiguration());
    
            ChainReducer.setReducer(job, Reducer1.class, Text.class, IntWritable.class, Text.class, IntWritable.class,job.getConfiguration());
    
            ChainReducer.addMapper(job, ReducerMapper1.class, Text.class,
                    IntWritable.class, Text.class, IntWritable.class, job.getConfiguration());
    
            FileInputFormat.addInputPath(job,new Path(args[0]));
    
            FileOutputFormat.setOutputPath(job,outPath);
    
            job.setNumReduceTasks(2);
            job.setCombinerClass(Combiner1.class);
            job.setPartitionerClass(MyPartitioner.class);
    
            job.waitForCompletion(true) ;
    
        }
    }

[hadoop_ChainMapper_ChainReducer_1.png]: /images/20220610/41c23e8349824781b3692fad5f2fa933.png