Big data processing skills in C++

C is an efficient programming language that can handle various types of data. It is suitable for processing large amounts of data, but if proper techniques are not used to handle large data, the program can become very slow and unstable. In this article, we will introduce some techniques for working with big data in C.

1. Use dynamic memory allocation

In C, the memory allocation of variables can be static or dynamic. Static memory allocation allocates memory space before the program runs, while dynamic memory allocation allocates memory space as needed while the program is running.

When processing large amounts of data, using dynamic memory allocation can avoid wasting a large amount of memory space. There are two ways to implement dynamic memory allocation: using the new and delete operators, or using an STL container.

The following is a code example that uses the new and delete operators to implement dynamic memory allocation:

int* arr = new int[1000000]; // 动态分配1000000个整型变量的内存空间

// Do something

delete[] arr; // 释放内存

When using STL containers, you can use vector or list. The following is a code example of using vector to implement dynamic memory allocation:

#include <vector>

std::vector<int> arr(1000000); // 动态分配1000000个整型变量的内存空间

// Do something

2. Using bit operations

Bit operations are a technique for quickly processing big data. Bit operations include operations such as AND, OR, XOR, shift, and negation.

The following is a code example of using bit operations to process big data:

int x = 1000000;
int y = 2000000;

// 按位与运算
int z1 = x & y;

// 按位或运算
int z2 = x | y;

// 按位异或运算
int z3 = x ^ y;

// 左移动运算
int z4 = x << 2;

// 右移动运算
int z5 = x >> 2;

3. Using multi-threading

Multi-threading can allocate tasks to different threads for processing. Thereby speeding up the running speed of the program.

The following is a code example of using multi-threading to process big data:

#include <iostream>
#include <thread>
#include <vector>

void func(int start, int end, std::vector<int>& arr)
{
    for (int i = start; i < end; i++)
    {
        // Do something with arr[i]
    }
}

int main()
{
    std::vector<int> arr(1000000); // 要处理的数据

    int num_threads = 4;
    int batch_size = arr.size() / num_threads;

    std::vector<std::thread> threads;
    for (int i = 0; i < num_threads; i++)
    {
        int start = i * batch_size;
        int end = (i == num_threads - 1) ? arr.size() : (i + 1) * batch_size;
        threads.push_back(std::thread(func, start, end, std::ref(arr)));
    }

    // 等待所有线程完成工作
    for (auto& th : threads)
    {
        th.join();
    }

    return 0;
}

The above are three tips for processing big data in C. Use these tips to make your program run faster and make it more robust.

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