Kotlin's asynchronous programming capabilities allow developers to write non-blocking code, enhancing performance by executing multiple tasks concurrently. One of Kotlin's key features, especially within the realm of Kotlin Coroutines, is the use of `async` and `await` to manage asynchronous tasks effectively. Unlike traditional eager execution threads, these constructs allow for structured concurrency while adhering to a simple syntax. This article explores Kotlin's `async` and `await` with an emphasis on limited parallelism.

Understanding `async` and `await`

What are `async` and `await`?

In Kotlin, `async` starts a coroutine that executes asynchronously and returns a `Deferred`. The `Deferred` can be thought of as a non-blocking, cancellable future, representing a promise to provide a result at some later time.

• `async`: Launches a new coroutine concurrently, providing a non-blocking way to start operations.
• `await`: Used to retrieve the result of the coroutine, suspending the function without blocking if the result isn't available.

The Concept of Limited Parallelism

Limited parallelism refers to controlling the number of concurrent tasks to avoid overwhelming the system resources, which can be achieved through Kotlin's coroutines. This enables tasks to be executed concurrently, while still maintaining an upper limit to avoid potential performance bottlenecks.

Implementing Async Await with Limited Parallelism

Example Overview

The following code snippet demonstrates using `async` and `await` with limited parallelism. Our goal is to orchestrate multiple network requests without allowing the system to execute more than a predefined number of parallel requests.

Code Example

• Chunking: The list of URLs is divided into smaller chunks to control the number of parallel fetches. Here, `chunkSize = 2` indicates that no more than two requests are processed at the same time.
• `async(Dispatchers.IO)`: Each chunk is processed asynchronously. Using `Dispatchers.IO` ensures that network requests run in a proper thread pool optimized for IO processing.
• `await` Result Retrieval: We wait for each `Deferred` to complete with `await()`. It suspends the coroutine without blocking current threads.
• Performance vs. Resources: Use limited parallelism to balance system resource usage without sacrificing performance. Unbounded parallelism can lead to thread starvation and resource saturation, harming overall performance.
• Error Handling: Implement appropriate error handling within coroutines to manage exceptions, potentially using constructs such as `try-catch` or custom exception handlers.
• Coroutine Scope: Ensure optimal use of coroutine scopes to manage lifecycle and cancellation of coroutines within your application.