How do you solve Execute Asynchronous Functions in Parallel on LeetCode?

Execute Asynchronous Functions in Parallel (LeetCode #2721) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(n) space complexity. Key insight: Understanding how promises work in parallel is crucial for efficient asynchronous programming.

What is the brute force solution for Execute Asynchronous Functions in Parallel?

The brute force approach for Execute Asynchronous Functions in Parallel is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force approach involves executing each asynchronous function one by one and collecting their results. This method is straightforward but inefficient as it does not take advantage of the parallel execution capabilities of promises. The optimal Optimal Solution approach improves this to O(n).

What are the interview tips for Execute Asynchronous Functions in Parallel?

Always clarify the requirements before starting to code, especially regarding error handling. Think about edge cases, such as what happens if no functions are provided or if all functions reject. Practice explaining your thought process while coding, as communication is key in interviews.

What are common mistakes when solving Execute Asynchronous Functions in Parallel?

Forgetting to handle promise rejection properly, leading to unhandled promise rejections. Not using the correct data structure to store results, which can lead to incorrect ordering.

#2721

Execute Asynchronous Functions in Parallel

Medium

Promise HandlingAsynchronous Programming

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n)
Space	O(1)	O(n)

💡

Intuition

Time O(n)Space O(n)

The optimal approach leverages the ability to execute all promises in parallel and uses a single promise to track their completion. This is efficient and ensures we handle both resolution and rejection correctly.

⚙️

Algorithm

5 steps

1Step 1: Create an array to hold the results and a counter for completed promises.
2Step 2: Return a new promise that will resolve or reject based on the results of the async functions.
3Step 3: Loop through each function, call it, and attach .then() and .catch() to handle results.
4Step 4: On resolution, store the result and check if all promises are resolved. If so, resolve the main promise with the results array.
5Step 5: On rejection, immediately reject the main promise with the error.

solution.py19 lines

1def promiseAll(functions):
2    results = []
3    completed = 0
4    def resolver(index):
5        nonlocal completed
6        def inner(promise):
7            try:
8                result = promise()
9                results[index] = result
10                completed += 1
11                if completed == len(functions):
12                    return Promise.resolve(results)
13            except Exception as e:
14                return Promise.reject(e)
15        return inner
16    for i, func in enumerate(functions):
17        results.append(None)
18        resolver(i)(func)
19    return Promise.reject('Error')

ℹ

Complexity note: The time complexity is O(n) because we are handling each promise independently in parallel. The space complexity is O(n) due to the results array that stores the resolved values.

1Understanding how promises work in parallel is crucial for efficient asynchronous programming.
2Handling both resolution and rejection properly is key to building robust asynchronous functions.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.