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Design principles for scalable big data processing solutions in C technology: Parallelization: Leveraging multi-core processors and distributed system architectures for parallel processing. Memory management: Optimize data structures and algorithms to minimize memory consumption. Scalability: Design solutions that scale easily as data sets and processing needs grow.
Big Data Processing in C Technology: Designing Scalable Big Data Processing Solutions
In processing large and complex data sets In the era of massive data, scalability is crucial for big data processing solutions. C is known for its excellent performance and resource efficiency, making it ideal for big data processing.
Principles for designing scalable big data solutions
Practical Case: Parallelized Big Data Processing
#include <vector> #include <thread> #include <functional> using namespace std; int main() { // 创建一个包含 1 亿个整数的大型向量 vector<int> data(100000000); // 并行计算每个元素的平方 vector<thread> threads(thread::hardware_concurrency()); for (size_t i = 0; i < threads.size(); i++) { threads[i] = thread( [](vector<int>& data, size_t start, size_t end) { for (size_t j = start; j < end; j++) { data[j] = data[j] * data[j]; } }, ref(data), i * data.size() / threads.size(), (i + 1) * data.size() / threads.size()); } // 等待所有线程完成 for (auto& thread : threads) { thread.join(); } }
This example shows how to use C to parallelize big data processing. It splits the data set into chunks and uses multiple threads to process the chunks simultaneously, significantly improving processing efficiency.
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