Big data processing in C++ technology: How to use the MapReduce framework for distributed big data processing?

By using the Hadoop MapReduce framework in C, the following big data processing steps can be achieved: 1. Map data to key-value pairs; 2. Aggregate or process values ​​with the same key. The framework includes Mapper and Reducer classes to perform the mapping and aggregation phases respectively.

Big data processing in C technology: using the MapReduce framework to implement distributed big data processing

Introduction
In today’s era of explosive data growth, processing and analyzing large-scale data sets has become critical. MapReduce is a powerful programming model for processing big data in a distributed computing environment. This article explores how to use the MapReduce framework to perform distributed big data processing in C.

MapReduce Overview
MapReduce is a parallel programming paradigm developed by Google for processing massive data sets. It divides the data processing process into two main stages:

• Map stage: This stage maps the input data to a series of key-value pairs.
• Reduce stage: This stage summarizes or processes the associated values ​​of each key.

MapReduce Implementation in C
Hadoop is a popular open source MapReduce framework that provides bindings for multiple languages, including C. To use Hadoop in C, you need to include the following header file:

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

Practical Case
The following shows sample code for counting word frequencies in a text file using C and Hadoop MapReduce:

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;
}

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