How do you solve Design Cancellable Function on LeetCode?

Design Cancellable Function (LeetCode #2650) 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 two-way communication between generators and the calling code is crucial.

What is the time complexity of Design Cancellable Function?

The optimal solution for Design Cancellable Function runs in O(n) time and uses O(n) space. This complexity is due to the linear execution of promises and the generator, allowing for efficient handling of each step.

What is the brute force solution for Design Cancellable Function?

The brute force approach for Design Cancellable Function is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force approach involves executing the generator step-by-step, waiting for each promise to resolve or reject. If cancellation is requested, we throw an error back to the generator. This approach is straightforward but inefficient. The optimal Optimal Solution approach improves this to O(n).

What are the interview tips for Design Cancellable Function?

Clarify the requirements and edge cases before jumping into coding. Think about how to handle asynchronous operations and cancellations effectively. Practice explaining your thought process as you code, as communication is key in interviews.

What are common mistakes when solving Design Cancellable Function?

Failing to handle promise rejections properly, leading to unhandled promise errors. Not managing the generator state correctly, which can cause unexpected behaviors.

#2650

Design Cancellable Function

Hard

Asynchronous ProgrammingPromise HandlingGenerator Functions

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 efficiently manages the generator's execution and cancellation by using a promise chain and handling errors directly. This reduces unnecessary waiting and allows for immediate cancellation.

⚙️

Algorithm

6 steps

1Step 1: Create an iterator from the generator function.
2Step 2: Define a cancel function that sets an exception in the promise.
3Step 3: Use a recursive function to handle the promise resolution and generator communication.
4Step 4: If a promise resolves, send the value back to the generator.
5Step 5: If the cancel function is called, reject the promise with 'Cancelled'.
6Step 6: Handle completion and errors appropriately.

solution.py16 lines

1def cancellable(gen):
2    it = iter(gen)
3    def cancel():
4        raise 'Cancelled'
5    promise = None
6    def step(value):
7        nonlocal promise
8        try:
9            promise = next(it)
10            resolved_value = yield from promise
11            it.send(resolved_value)
12        except StopIteration as e:
13            return e.value
14        except Exception as e:
15            it.throw(e)
16    return [cancel, step]  # Adjusted for async handling

ℹ

Complexity note: This complexity is due to the linear execution of promises and the generator, allowing for efficient handling of each step.

1Understanding two-way communication between generators and the calling code is crucial.
2Efficient error handling can significantly improve the performance and reliability of asynchronous tasks.

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