Data pipelines are an essential component in many software systems, allowing the efficient processing and transformation of data streams. Traditionally, implementing data pipelines in C++ involved complex state management and callback functions. However, with the introduction of coroutines in C++20, building data pipelines has become more intuitive and readable. In this blog post, we will explore how to leverage C++ coroutines to build efficient and elegant data pipelines.

What are Coroutines?

Coroutines are a new language feature introduced in C++20 that enables cooperative multitasking. They provide an elegant way to define asynchronous operations and manage their execution flow. Coroutines simplify the implementation of complex asynchronous code by allowing you to write procedural-style code that appears synchronous, while still executing asynchronously.

Building a Data Pipeline

Let’s suppose we have a large dataset that needs to be processed by multiple stages in a pipeline - such as filtering, transforming, and aggregating the data. Traditionally, this would involve passing data between callback functions or managing complex state machines. However, with coroutines, we can create a more straightforward and structured pipeline.

To begin, let’s define our pipeline as a generator function using C++ coroutines. We can represent each stage in the pipeline as a coroutine. Here’s an example implementation:

#include <iostream>
#include <coroutine>

// Define the data structure
struct Data {
    int value;
};

// Define the pipeline stages
coroutine<Data> producer() {
    co_yield Data{ 1 };
    co_yield Data{ 2 };
    co_yield Data{ 3 };
}

coroutine<Data> filter(coroutine<Data> input) {
    for co_await (auto data : input) {
        if (data.value % 2 == 0) {
            co_yield data;
        }
    }
}

coroutine<Data> transformer(coroutine<Data> input) {
    for co_await (auto data : input) {
        co_yield Data{ data.value * 2 };
    }
}

coroutine<void> consumer(coroutine<Data> input) {
    for co_await (auto data : input) {
        std::cout << data.value << " ";
    }
    std::cout << std::endl;
}

In this example, we define four coroutines - producer, filter, transformer, and consumer. The producer coroutine generates a sequence of Data objects. The filter and transformer coroutines perform filtering and transformation respectively on the input data. Finally, the consumer coroutine outputs the processed data.

To compose the pipeline stages and execute the data pipeline, we can use the following main function:

int main() {
    auto pipeline = consumer(transformer(filter(producer())));
    pipeline(); // Executes the entire pipeline
    
    return 0;
}

Executing the main function will output the processed data: 4 6 (as the initial sequence is filtered to contain only even numbers and then transformed by multiplying each value by 2).

Conclusion

Using C++ coroutines, we can construct elegant and efficient data pipelines that are easy to read, write, and maintain. By representing each stage as a coroutine, we can express the pipeline logic in a procedural manner, simplifying the code and reducing the complexity traditionally associated with building data pipelines in C++. By embracing coroutines, you can take advantage of a powerful language feature that makes developing data pipelines a breeze.

#cpp #coroutines