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Given a positive integer millis, write an asynchronous function that sleeps for millis milliseconds. It can resolve any value.

Note that minor deviation from millis in the actual sleep duration is acceptable.

Example 1:

Input: millis = 100
Output: 100
Explanation: It should return a promise that resolves after 100ms.
let t = Date.now();
sleep(100).then(() => {
  console.log(Date.now() - t); // 100
});

Example 2:

Input: millis = 200
Output: 200
Explanation: It should return a promise that resolves after 200ms.

Constraints:

  • 1 <= millis <= 1000

Solution


Clarifying Questions

When you get asked this question in a real-life environment, it will often be ambiguous (especially at FAANG). Make sure to ask these questions in that case:

  1. What is the expected unit of time for the sleep duration (e.g., seconds, milliseconds, nanoseconds)?
  2. Is the sleep duration guaranteed to be non-negative?
  3. What should happen if the sleep is interrupted by a signal or an exception?
  4. Are we aiming for high precision sleep (accurate to the millisecond or microsecond), or is approximate sleep sufficient?
  5. Should the function return anything, and if so, what should it return (e.g., success/failure indicator, actual sleep duration)?

Brute Force Solution

Approach

The brute force approach to the 'Sleep' problem is like manually testing every possible delay value to see if it works. We try each possible delay, one at a time. We keep trying until we find a delay that causes the program to behave as expected.

Here's how the algorithm would work step-by-step:

  1. Start with a very small delay value, like zero.
  2. Run the program with that delay.
  3. Check if the program's output or behavior is correct after that delay.
  4. If it's not correct, try a slightly larger delay.
  5. Keep increasing the delay by a small amount each time, and repeat the process of running the program and checking the output.
  6. Continue until you find a delay value that produces the correct output or behavior for the program.
  7. If you reach a very large delay value without finding a solution, the brute force approach might not be effective, or a solution may not exist.

Code Implementation

def find_sleep_delay_brute_force():
    smallestDelayValue = 0
    delayIncrement = 0.1
    maximumDelayValue = 10

    currentDelay = smallestDelayValue

    while currentDelay <= maximumDelayValue:
        # Simulate running the program with the current delay
        programOutput = simulate_program_with_delay(currentDelay)

        # Check if the program's output is correct
        if is_program_output_correct(programOutput):
            return currentDelay

            # Increment delay if output is not correct
        currentDelay += delayIncrement

        #Brute force approach failed, no solution
    return None

def simulate_program_with_delay(delay):
    # This function should simulate running the program
    # with the given delay.  For demonstration purposes,
    # it returns a string.  In a real scenario, this would
    # interact with the actual program.
    if delay > 5:
        return "CorrectOutput"
    else:
        return "IncorrectOutput"

def is_program_output_correct(output):
    # This function checks if the program's output is correct.
    # This is a placeholder; a real implementation would
    # perform a more thorough check.
    return output == "CorrectOutput"

# This is needed to start the brute force algorithm
# with an initial small delay value (zero).

# If a solution is not found within the
# maximum allowed delay, return None.

Big(O) Analysis

Time Complexity
O(infinity)The brute force approach involves iteratively increasing the delay value and checking if the program behaves correctly. In the worst-case scenario, we might have to test an infinite number of delay values before finding the correct one (or determining that no solution exists). Because the number of iterations is potentially limitless, the time complexity is essentially unbounded, and thus is O(infinity). Realistically, the search will terminate or be cut off at some extremely large value, but in theory, there is no defined bound.
Space Complexity
O(1)The brute force approach iterates through delay values but doesn't store them or create data structures dependent on the input. It only uses a constant amount of extra memory to hold the current delay being tested. Therefore, the auxiliary space complexity is independent of the input size and is O(1).

Optimal Solution

Approach

The goal is to pause the program's execution for a specified amount of time. The most efficient way to achieve this is to use the system's built-in functionality designed specifically for pausing execution, avoiding busy-waiting or inefficient looping.

Here's how the algorithm would work step-by-step:

  1. Get the desired duration of the pause as an input.
  2. Use the system's dedicated sleep function, passing the duration as an argument. This tells the system to suspend the program's execution.
  3. Once the specified time has elapsed, the sleep function will automatically return, and your program will resume its execution from the next line of code.
  4. This avoids keeping the processor busy checking the time repeatedly and allows the system to handle the pausing efficiently.

Code Implementation

import time

def sleep_function(sleep_duration_seconds):
    # Convert duration to seconds.
    time_to_sleep = float(sleep_duration_seconds)

    # Suspend execution for the specified duration.
    time.sleep(time_to_sleep)

def main():
    # Get the desired sleep duration from the user.
    sleep_duration_input = input("Enter sleep duration in seconds: ")
    sleep_duration = float(sleep_duration_input)

    print(f"Sleeping for {sleep_duration} seconds...")
    sleep_function(sleep_duration)

    # Execution resumes here after the sleep duration.
    print("Woke up!")

if __name__ == "__main__":
    main()

Big(O) Analysis

Time Complexity
O(1)The algorithm utilizes the system's sleep function, which is a blocking operation handled by the operating system. The duration of the sleep is specified as input, but the sleep function itself does not iterate or perform any operations dependent on the size of any input data. The operating system handles the pausing of the thread, and the time spent sleeping does not affect the algorithm's time complexity. Therefore, the time complexity is constant, or O(1).
Space Complexity
O(1)The sleep function itself does not allocate any extra space that depends on the duration of the pause. The algorithm simply takes the input duration and passes it to the system's sleep function. No additional data structures or variables are created that scale with the input, therefore the space complexity is constant.

Edge Cases

Sleep duration is zero.
How to Handle:
If the sleep duration is zero, the program should effectively do nothing and return immediately.
Sleep duration is negative.
How to Handle:
The program should handle this case by either throwing an exception or treating the input as zero, documenting the chosen behavior.
Sleep duration is a very large number that could cause an overflow.
How to Handle:
The program needs to prevent possible integer overflow by either throwing exception or clamping to an upper limit that the OS supports and documenting this limitation.
The system clock is adjusted during sleep.
How to Handle:
The program should account for clock changes by comparing the start and end times rather than simply waiting the duration.
The program receives a signal during sleep.
How to Handle:
The program should catch and handle signals appropriately, potentially interrupting the sleep and returning early.
Sleep duration is a floating-point number, and precision is limited.
How to Handle:
The program needs to manage floating-point precision to approximate the sleep time as closely as possible using available system calls.
The system has limited resources and cannot sleep for the requested duration.
How to Handle:
The program should detect and handle potential errors reported by the OS due to resource limitations during the sleep operation and perhaps retry.
Sleep interrupted by system events that are not a signal, but a scheduler yielding the process.
How to Handle:
The program should ensure that total time elapsed is at least the requested time (by tracking elapsed time and sleeping the remaining time) to fulfill the promise of the function.