How do I provide stable iteration order with std::vector in multithreaded code?

In multithreaded code, providing a stable iteration order with std::vector in C++ can be challenging. It's essential to ensure data consistency and prevent race conditions while iterating over the vector. This guide covers techniques to achieve a stable iteration order across multiple threads.

multithreading, C++, std::vector, stable iteration order, data consistency, race conditions, thread safety

#include <iostream> #include <vector> #include <thread> #include <mutex> std::vector<int> numbers = {1, 2, 3, 4, 5}; std::mutex mtx; void processElement(int index) { std::lock_guard<std::mutex> lock(mtx); std::cout << "Processing element: " << numbers[index] << std::endl; } int main() { std::vector<std::thread> threads; for (size_t i = 0; i < numbers.size(); ++i) { threads.emplace_back(processElement, i); } for (auto &t : threads) { t.join(); } return 0; }

multithreading C++ std::vector stable iteration order data consistency race conditions thread safety

Related Questions

How do I avoid rehashing overhead with std::set in multithreaded code?

How do I find elements with custom comparators with std::set for embedded targets?

How do I erase elements while iterating with std::set for embedded targets?

How do I provide stable iteration order with std::unordered_map for large datasets?

How do I reserve capacity ahead of time with std::unordered_map for large datasets?

How do I erase elements while iterating with std::unordered_map in multithreaded code?

How do I provide stable iteration order with std::map for embedded targets?

How do I provide stable iteration order with std::map in multithreaded code?

How do I avoid rehashing overhead with std::map in performance-sensitive code?

How do I merge two containers efficiently with std::map for embedded targets?