How to deal with the data deduplication problem in C++ big data development?

How to deal with the data deduplication problem in C big data development?

Introduction: In the C big data development process, data deduplication is a common problem. This article will introduce several methods to efficiently handle big data deduplication problems in C and provide corresponding code examples.

1. Use hash table for deduplication

Hash table is a commonly used data structure that can quickly find and store data. In the problem of data deduplication, we can use a hash table to store data that has already appeared. Every time new data is read, first check whether it exists in the hash table. If it does not exist, add the data to the hash table. in the Greek table and mark it as having already appeared.

#include <iostream>
#include <unordered_set>
#include <vector>

void duplicateRemoval(std::vector<int>& data) {
    std::unordered_set<int> hashSet;
    for (auto iter = data.begin(); iter != data.end();) {
        if (hashSet.find(*iter) != hashSet.end()) {
            iter = data.erase(iter);
        } else {
            hashSet.insert(*iter);
            ++iter;
        }
    }
}

int main() {
    std::vector<int> data = {1, 2, 3, 4, 5, 4, 3, 2, 1};
    duplicateRemoval(data);
    // 输出去重后的数据
    for (auto val : data) {
        std::cout << val << " ";
    }
    std::cout << std::endl;
    return 0;
}

2. Use bitmaps for deduplication

When we face a very large amount of data, using a hash table may take up a lot of memory space. At this point, we can use bitmaps to perform deduplication operations. Bitmap is a very compact data structure that can represent a large number of Boolean values. We can use the value of each data as the subscript of the bitmap and mark the position where the data appears as 1. When encountering a marked position, it means that the data has been repeated and can be deleted from the original data.

#include <iostream>
#include <vector>

void duplicateRemoval(std::vector<int>& data) {
    const int MAX_NUM = 1000000; // 假设数据的范围在0至1000000之间
    std::vector<bool> bitmap(MAX_NUM, false);
    for (auto iter = data.begin(); iter != data.end();) {
        if (bitmap[*iter]) {
            iter = data.erase(iter);
        } else {
            bitmap[*iter] = true;
            ++iter;
        }
    }
}

int main() {
    std::vector<int> data = {1, 2, 3, 4, 5, 4, 3, 2, 1};
    duplicateRemoval(data);
    // 输出去重后的数据
    for (auto val : data) {
        std::cout << val << " ";
    }
    std::cout << std::endl;
    return 0;
}

3. Use sorting to deduplicate

If there is no memory limit on the original data and the data has been sorted, we can use the sorting algorithm to perform deduplication. The sorting algorithm can make the same data in adjacent positions, and then we only need to traverse the data once and delete the duplicate data.

#include <iostream>
#include <algorithm>
#include <vector>

void duplicateRemoval(std::vector<int>& data) {
    data.erase(std::unique(data.begin(), data.end()), data.end());
}

int main() {
    std::vector<int> data = {1, 2, 3, 4, 5, 4, 3, 2, 1};
    std::sort(data.begin(), data.end());
    duplicateRemoval(data);
    // 输出去重后的数据
    for (auto val : data) {
        std::cout << val << " ";
    }
    std::cout << std::endl;
    return 0;
}

Summary: In C big data development, data deduplication is a common problem. This article introduces three methods for efficiently handling big data deduplication problems and provides corresponding code examples. Choosing the appropriate method according to the actual situation can greatly improve the speed and efficiency of data processing.

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