C++ 빅데이터 개발에서 데이터 추천 효율성을 높이는 방법은 무엇입니까?

#include <iostream>
#include <unordered_map>

int main() {
  std::unordered_map<int, std::string> data;

  // 插入数据
  data[1] = "Apple";
  data[2] = "Banana";
  data[3] = "Orange";

  // 查找数据
  int key = 2;
  auto it = data.find(key);
  if (it != data.end()) {
    std::cout << "Key " << key << " found: " << it->second << std::endl;
  } else {
    std::cout << "Key " << key << " not found!" << std::endl;
  }

  return 0;
}

#include <iostream>
#include <vector>
#include <omp.h>

int main() {
  std::vector<int> data = {1, 2, 3, 4, 5};

  int sum = 0;
  #pragma omp parallel for reduction(+:sum)
  for (int i = 0; i < data.size(); i++) {
    sum += data[i];
  }

  std::cout << "Sum: " << sum << std::endl;

  return 0;
}

#include <iostream>
#include <vector>

class Object {
public:
  Object() {}
  ~Object() {}

  // 对象池创建对象
  void* operator new(size_t size) {
    if (m_objects.empty()) {
      // 创建新对象
      return ::operator new(size);
    } else {
      // 从对象池中获取对象
      void* p = m_objects.back();
      m_objects.pop_back();
      return p;
    }
  }

  // 对象池释放对象
  static void operator delete(void* p, size_t size) {
    // 将对象放回对象池中
    m_objects.push_back(p);
  }

private:
  static std::vector<void*> m_objects;
};

std::vector<void*> Object::m_objects;

int main() {
  Object* obj1 = new Object();
  Object* obj2 = new Object();

  // 使用对象...

  // 释放对象
  delete obj1;
  delete obj2;

  return 0;
}