C++技术中的大数据处理：如何使用MapReduce框架进行分布式大数据处理？

#include <hadoop/Config.hh>
#include <hadoop/MapReduce.hh>

class WordCountMapper : public hadoop::Mapper<hadoop::String, hadoop::String, hadoop::String, hadoop::Int> {
public:
  hadoop::Int map(const hadoop::String& key, const hadoop::String& value) override {
    // 分割文本并映射单词为键，值设为 1
    std::vector<std::string> words = split(value.str());
    for (const auto& word : words) {
      return hadoop::make_pair(hadoop::String(word), hadoop::Int(1));
    }
  }
};

class WordCountReducer : public hadoop::Reducer<hadoop::String, hadoop::Int, hadoop::String, hadoop::Int> {
public:
  hadoop::Int reduce(const hadoop::String& key, hadoop::Sequence<hadoop::Int>& values) override {
    // 汇总相同单词出现的次数
    int sum = 0;
    for (const auto& value : values) {
      sum += value.get();
    }
    return hadoop::make_pair(key, hadoop::Int(sum));
  }
};

int main(int argc, char** argv) {
  // 创建一个 MapReduce 作业
  hadoop::Job job;
  job.setJar("/path/to/wordcount.jar");

  // 设置 Mapper 和 Reducer
  job.setMapper<WordCountMapper>();
  job.setReducer<WordCountReducer>();

  // 运行作业
  int success = job.waitForCompletion();
  if (success) {
    std::cout << "MapReduce 作业成功运行。" << std::endl;
  } else {
    std::cerr << "MapReduce 作业失败。" << std::endl;
  }

  return 0;
}