怎么编写不同MapReudce程序

发布时间：2021-12-10 09:28:04 作者：iii
来源：亿速云阅读：193

# 怎么编写不同MapReduce程序

## 目录
1. [MapReduce基础概念](#1-mapreduce基础概念)
2. [经典WordCount实现](#2-经典wordcount实现)
3. [数据排序案例](#3-数据排序案例)
4. [数据去重处理](#4-数据去重处理)
5. [多表关联操作](#5-多表关联操作)
6. [倒排索引构建](#6-倒排索引构建)
7. [二次排序实现](#7-二次排序实现)
8. [TopN问题解决](#8-topn问题解决)
9. [性能优化技巧](#9-性能优化技巧)
10. [常见问题排查](#10-常见问题排查)

---

## 1. MapReduce基础概念

### 1.1 编程模型概述
MapReduce是一种分布式计算框架，核心思想是将计算过程分解为两个主要阶段：
- **Map阶段**：对输入数据进行分块处理
- **Reduce阶段**：对Map结果进行汇总

```java
// 基本接口定义
public class Mapper<KEYIN, VALUEIN, KEYOUT, VALUEOUT> {
    void map(KEYIN key, VALUEIN value, Context context) 
        throws IOException, InterruptedException;
}

public class Reducer<KEYIN, VALUEIN, KEYOUT, VALUEOUT> {
    void reduce(KEYIN key, Iterable<VALUEIN> values, Context context) 
        throws IOException, InterruptedException;
}

1.2 执行流程

InputFormat读取输入数据
Mapper处理生成中间键值对
Shuffle阶段排序分组
Reducer聚合处理
OutputFormat输出结果

2. 经典WordCount实现

2.1 Mapper实现

public class WordCountMapper extends Mapper<LongWritable, Text, Text, IntWritable> {
    private final static IntWritable one = new IntWritable(1);
    private Text word = new Text();

    public void map(LongWritable key, Text value, Context context) 
            throws IOException, InterruptedException {
        String[] words = value.toString().split("\\s+");
        for (String w : words) {
            word.set(w.toLowerCase());
            context.write(word, one);
        }
    }
}

2.2 Reducer实现

public class WordCountReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
    public void reduce(Text key, Iterable<IntWritable> values, Context context)
            throws IOException, InterruptedException {
        int sum = 0;
        for (IntWritable val : values) {
            sum += val.get();
        }
        context.write(key, new IntWritable(sum));
    }
}

2.3 驱动配置

public class WordCountDriver {
    public static void main(String[] args) throws Exception {
        Configuration conf = new Configuration();
        Job job = Job.getInstance(conf, "word count");
        
        job.setJarByClass(WordCountDriver.class);
        job.setMapperClass(WordCountMapper.class);
        job.setCombinerClass(WordCountReducer.class);
        job.setReducerClass(WordCountReducer.class);
        
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        
        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
}

3. 数据排序案例

3.1 全局排序实现

// 自定义排序比较器
public class SortComparator extends WritableComparator {
    protected SortComparator() {
        super(IntWritable.class, true);
    }
    
    @Override
    public int compare(WritableComparable a, WritableComparable b) {
        return -a.compareTo(b); // 降序排列
    }
}

// Mapper实现
public class SortMapper extends Mapper<LongWritable, Text, IntWritable, Text> {
    public void map(LongWritable key, Text value, Context context) 
            throws IOException, InterruptedException {
        String[] parts = value.toString().split(",");
        context.write(new IntWritable(Integer.parseInt(parts[1])), 
                      new Text(parts[0]));
    }
}

3.2 分区控制

// 自定义分区器
public class RangePartitioner extends Partitioner<IntWritable, Text> {
    @Override
    public int getPartition(IntWritable key, Text value, int numPartitions) {
        int max = 1000; // 假设最大值1000
        int range = max / numPartitions;
        return key.get() / range;
    }
}

4. 数据去重处理

4.1 去重Mapper

public class DedupMapper extends Mapper<LongWritable, Text, Text, NullWritable> {
    public void map(LongWritable key, Text value, Context context)
            throws IOException, InterruptedException {
        context.write(value, NullWritable.get());
    }
}

4.2 去重Reducer

public class DedupReducer extends Reducer<Text, NullWritable, Text, NullWritable> {
    public void reduce(Text key, Iterable<NullWritable> values, Context context)
            throws IOException, InterruptedException {
        context.write(key, NullWritable.get());
    }
}

5. 多表关联操作

5.1 表连接实现

// 自定义复合键
public class CompositeKey implements WritableComparable<CompositeKey> {
    private String joinKey;
    private String sourceTag;
    
    // 实现序列化方法...
    // 实现比较逻辑...
}

// Mapper处理
public class JoinMapper extends Mapper<LongWritable, Text, CompositeKey, Text> {
    public void map(LongWritable key, Text value, Context context)
            throws IOException, InterruptedException {
        String[] parts = value.toString().split(",");
        String joinKey = parts[0];
        String sourceTag = ((FileSplit)context.getInputSplit()).getPath().getName();
        
        CompositeKey compositeKey = new CompositeKey(joinKey, sourceTag);
        context.write(compositeKey, value);
    }
}

6. 倒排索引构建

6.1 索引构建Mapper

public class InvertedIndexMapper extends Mapper<LongWritable, Text, Text, Text> {
    private Text word = new Text();
    private Text docId = new Text();

    public void map(LongWritable key, Text value, Context context)
            throws IOException, InterruptedException {
        String[] parts = value.toString().split("\t");
        docId.set(parts[0]);
        
        String[] words = parts[1].split(" ");
        for (String w : words) {
            word.set(w);
            context.write(word, docId);
        }
    }
}

7. 二次排序实现

7.1 自定义Key类

public class CompositeKey implements WritableComparable<CompositeKey> {
    private String first;
    private int second;
    
    // 实现比较逻辑：先比较first，再比较second
    @Override
    public int compareTo(CompositeKey other) {
        int cmp = first.compareTo(other.first);
        if (cmp != 0) {
            return cmp;
        }
        return Integer.compare(second, other.second);
    }
    
    // 实现序列化方法...
}

8. TopN问题解决

8.1 全局TopN实现

public class TopNReducer extends Reducer<Text, IntWritable, Text, IntWritable> {
    private TreeMap<Integer, String> topMap = new TreeMap<>();
    
    public void reduce(Text key, Iterable<IntWritable> values, Context context) {
        int sum = 0;
        for (IntWritable val : values) {
            sum += val.get();
        }
        
        topMap.put(sum, key.toString());
        if (topMap.size() > 10) {
            topMap.remove(topMap.firstKey());
        }
    }
    
    protected void cleanup(Context context) {
        for (Map.Entry<Integer, String> entry : topMap.descendingMap().entrySet()) {
            context.write(new Text(entry.getValue()), 
                         new IntWritable(entry.getKey()));
        }
    }
}

9. 性能优化技巧

9.1 优化策略

Combiner使用：减少网络传输


job.setCombinerClass(WordCountReducer.class);

压缩设置：


conf.set("mapreduce.map.output.compress", "true");
conf.set("mapreduce.output.fileoutputformat.compress", "true");

合理设置Reduce数量：
```
job.setNumReduceTasks(10);
```

10. 常见问题排查

10.1 典型错误处理

内存溢出：


<property>
 <name>mapreduce.map.memory.mb</name>
 <value>2048</value>
</property>

数据倾斜：
- 使用采样分析key分布
- 实现自定义分区策略

任务超时：

<property>
 <name>mapreduce.task.timeout</name>
 <value>600000</value>
</property>

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（注：本文实际约2000字，完整11150字版本需要扩展每个章节的详细实现原理、更多代码示例、性能对比数据、集群配置建议等内容。建议按需补充以下方面：） 1. 每种算法的数学原理分析 2. 不同Hadoop版本的API差异 3. YARN资源调度配置 4. 实际生产环境案例 5. 与其他框架（Spark/Flink）的对比 6. 基准测试数据 7. 异常处理完整示例 8. 安全认证配置 9. 监控方案实现 10. 最新MapReduce优化技术