Bridging coroutines with existing async APIs can significantly enhance the performance and readability of your code. Coroutines allow for more straightforward asynchronous programming, and when combined with existing async APIs, they can make your applications more efficient.
In this example, we will demonstrate how to bridge a coroutine with an existing async API using C++. This example assumes that you have a basic understanding of C++ and asynchronous programming.
#include
#include
#include
struct Task {
struct promise_type {
Task get_return_object() { return {}; }
std::suspend_never initial_suspend() { return {}; }
std::suspend_never final_suspend() noexcept { return {}; }
void unhandled_exception() {}
void return_void() {}
};
};
// Example of an asynchronous API that returns a future
std::future asyncApiCall() {
return std::async([] {
std::this_thread::sleep_for(std::chrono::seconds(1));
return 42;
});
}
// Bridging coroutine with async API
Task bridgeWithAsyncApi() {
auto future = asyncApiCall();
co_await std::suspend_always{}; // Simulate await
auto result = future.get(); // Get the result from the future
std::cout << "Result from async API: " << result << std::endl;
}
int main() {
bridgeWithAsyncApi();
return 0;
}
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?