10 Async Await Interview Questions and Answers

Async/Await has become a crucial feature in modern programming, enabling developers to write asynchronous code that is easier to read and maintain. This paradigm shift allows for more efficient execution of tasks, particularly in I/O-bound and high-latency operations, by enabling non-blocking code execution. Understanding how to effectively implement and troubleshoot async/await can significantly enhance the performance and responsiveness of applications.

This article provides a curated selection of interview questions focused on async/await, designed to help you deepen your understanding and demonstrate your proficiency in this area. By familiarizing yourself with these questions and their answers, you will be better prepared to tackle technical interviews and showcase your expertise in asynchronous programming.

Async Await Interview Questions and Answers

1. Explain the purpose of async and await in JavaScript.

In JavaScript, the async keyword defines an asynchronous function that returns a Promise, while await pauses execution until the Promise resolves, allowing for a more synchronous flow of code.

Example:

async function fetchData() {
    try {
        let response = await fetch('https://api.example.com/data');
        let data = await response.json();
        console.log(data);
    } catch (error) {
        console.error('Error fetching data:', error);
    }
}

fetchData();

In this example, fetchData is an async function. The await keyword is used to wait for the fetch call to complete and for the response to be converted to JSON. Errors are caught and logged to the console.

2. Write a simple function using async/await that fetches data from an API and logs it to the console.

To fetch data from an API using async/await in Python, you can use the aiohttp library for asynchronous HTTP requests.

First, install the aiohttp library:

pip install aiohttp

Here is a simple function that fetches data from an API and logs it:

import aiohttp
import asyncio

async def fetch_data(url):
    async with aiohttp.ClientSession() as session:
        async with session.get(url) as response:
            data = await response.json()
            print(data)

url = 'https://api.example.com/data'
asyncio.run(fetch_data(url))

3. How would you handle errors in an async function? Provide a code example.

Error handling in an async function uses try-except blocks to catch exceptions and handle them appropriately, ensuring the program can manage errors without crashing.

Example:

import asyncio

async def fetch_data():
    try:
        await asyncio.sleep(1)
        raise ValueError("An error occurred while fetching data")
    except ValueError as e:
        print(f"Caught an error: {e}")
    finally:
        print("Cleaning up resources")

async def main():
    await fetch_data()

asyncio.run(main())

4. Convert a function that uses Promises into one that uses async/await.

Async/await in JavaScript allows you to work with asynchronous code in a more readable manner. It is built on Promises and provides a way to write asynchronous code that looks like synchronous code.

Here is an example of a function using Promises:

function fetchData() {
    return new Promise((resolve, reject) => {
        setTimeout(() => {
            resolve('Data fetched');
        }, 1000);
    });
}

fetchData()
    .then(data => {
        console.log(data);
    })
    .catch(error => {
        console.error(error);
    });

Converted to async/await:

async function fetchData() {
    return new Promise((resolve, reject) => {
        setTimeout(() => {
            resolve('Data fetched');
        }, 1000);
    });
}

async function getData() {
    try {
        const data = await fetchData();
        console.log(data);
    } catch (error) {
        console.error(error);
    }
}

getData();

5. How can you execute multiple async functions in parallel and wait for all of them to complete?

In Python, asynchronous programming allows for concurrent task execution, improving the efficiency of I/O-bound operations. Use asyncio.gather() to execute multiple async functions in parallel and wait for all to complete.

Example:

import asyncio

async def async_task(task_id, delay):
    await asyncio.sleep(delay)
    return f'Task {task_id} completed'

async def main():
    tasks = [
        async_task(1, 2),
        async_task(2, 1),
        async_task(3, 3)
    ]
    results = await asyncio.gather(*tasks)
    for result in results:
        print(result)

asyncio.run(main())

In this example, three async functions are executed in parallel with different delays. asyncio.gather() runs these tasks concurrently, and results are printed once all tasks are completed.

6. Write a function that executes multiple async functions sequentially.

To execute multiple async functions sequentially in Python, use the await keyword to ensure each function completes before the next starts.

Example:

import asyncio

async def async_function_1():
    print("Function 1 start")
    await asyncio.sleep(1)
    print("Function 1 end")

async def async_function_2():
    print("Function 2 start")
    await asyncio.sleep(1)
    print("Function 2 end")

async def main():
    await async_function_1()
    await async_function_2()

asyncio.run(main())

7. How would you use async/await inside a loop to process an array of items?

To process an array of items using async/await in Python, use the asyncio library. Create asynchronous functions and use await to call them. Use asyncio.gather to run tasks concurrently.

Example:

import asyncio

async def process_item(item):
    await asyncio.sleep(1)
    print(f'Processed item: {item}')

async def main(items):
    tasks = [process_item(item) for item in items]
    await asyncio.gather(*tasks)

items = [1, 2, 3, 4, 5]
asyncio.run(main(items))

In this example, process_item simulates an I/O-bound operation. The main function creates a list of tasks and uses asyncio.gather to run them concurrently.

8. Handling multiple dependent async operations.

Handling multiple dependent async operations in Python can be managed using async and await. Await the result of one operation before proceeding to the next.

Example:

import asyncio

async def fetch_data():
    await asyncio.sleep(1)
    return "data"

async def process_data(data):
    await asyncio.sleep(1)
    return f"processed {data}"

async def main():
    data = await fetch_data()
    result = await process_data(data)
    print(result)

asyncio.run(main())

In this example, fetch_data and process_data are asynchronous functions. The main function awaits fetch_data before passing its result to process_data.

9. How would you implement a retry mechanism for an async function that fails intermittently?

A retry mechanism is useful for handling transient failures in asynchronous programming. Implementing retries can improve application robustness.

Example:

import asyncio
import random

async def unreliable_function():
    if random.random() < 0.7:
        raise Exception("Transient error")
    return "Success"

async def retry_async_function(func, retries=3, delay=1):
    for attempt in range(retries):
        try:
            result = await func()
            return result
        except Exception as e:
            if attempt < retries - 1:
                await asyncio.sleep(delay)
            else:
                raise e

async def main():
    try:
        result = await retry_async_function(unreliable_function)
        print(result)
    except Exception as e:
        print(f"Function failed after retries: {e}")

asyncio.run(main())

In this example, retry_async_function attempts to execute a function and retries if an exception is raised, up to the specified number of retries.

10. Describe how you might implement cancellation for an async operation.

Cancellation in asynchronous programming stops an ongoing async operation before it completes. In Python, use the asyncio.CancelledError exception for task cancellation.

Example:

import asyncio

async def long_running_task():
    try:
        print("Task started")
        await asyncio.sleep(10)
        print("Task completed")
    except asyncio.CancelledError:
        print("Task was cancelled")
        raise

async def main():
    task = asyncio.create_task(long_running_task())
    await asyncio.sleep(1)
    task.cancel()
    try:
        await task
    except asyncio.CancelledError:
        print("Main caught task cancellation")

asyncio.run(main())

In this example, a task is cancelled after a short delay, demonstrating how to handle task cancellation.