Is Task Scheduler easy or hard?

Task Scheduler is classified as a medium difficulty problem on LeetCode with an acceptance rate around 60%. The main challenge is recognizing the greedy scheduling pattern and deriving the formula based on the most frequent tasks.

Task Scheduler Python/Java solution

Both Python and Java solutions typically implement either the greedy counting formula or a priority queue simulation. Python commonly uses collections.Counter and heapq, while Java uses an int array for counts or a PriorityQueue. The greedy solution is preferred due to its O(n) time and constant space.

How to solve Task Scheduler in O(n)?

Count the frequency of each task using a fixed array of size 26. Let maxFreq be the highest frequency and countMax be the number of tasks with that frequency. Compute the schedule length using (maxFreq - 1) * (n + 1) + countMax, then return the maximum of this value and the total task count.

What is the best approach for Task Scheduler?

The greedy max-frequency formula is the most efficient approach. Count the frequency of each task, identify the most frequent one, and compute the minimal frame using (maxFreq - 1) * (n + 1) + countMax. The final answer is the maximum between this value and the total number of tasks. This runs in O(n) time with O(1) space.

Is Task Scheduler asked at Google/Amazon/Meta?

Task Scheduler is a common medium-level scheduling and greedy problem frequently reported in interviews at Amazon, Google, and Meta. It tests frequency counting, greedy reasoning, and understanding of priority queues. Many companies use it to evaluate how candidates optimize from simulation to a mathematical greedy solution.

What data structure is used in Task Scheduler?

Two main structures are used depending on the approach. The greedy formula solution relies on an array for frequency counting. The simulation approach uses a max heap (priority queue) along with a queue or temporary list to manage cooldown cycles.

What is the time complexity of Task Scheduler?

The optimal greedy solution runs in O(n) time because it scans the task list once to compute frequencies. Space complexity is O(1) since there are at most 26 uppercase task types. A heap-based simulation approach runs in O(n log k) time where k is the number of distinct tasks.

Description Hints 3 Solution Video 17:07

621. Task Scheduler

Medium64.0% acceptance24 companies

You are given an array of CPU tasks, each labeled with a letter from A to Z, and a number n. Each CPU interval can be idle or allow the completion of one task. Tasks can be completed in any order, but there's a constraint: there has to be a gap of at least n intervals between two tasks with the same label.

Return the minimum number of CPU intervals required to complete all tasks.

Example 1:

Input: tasks = ["A","A","A","B","B","B"], n = 2

Output: 8

Explanation: A possible sequence is: A -> B -> idle -> A -> B -> idle -> A -> B.

After completing task A, you must wait two intervals before doing A again. The same applies to task B. In the 3^rd interval, neither A nor B can be done, so you idle. By the 4^th interval, you can do A again as 2 intervals have passed.

Example 2:

Input: tasks = ["A","C","A","B","D","B"], n = 1

Output: 6

Explanation: A possible sequence is: A -> B -> C -> D -> A -> B.

With a cooling interval of 1, you can repeat a task after just one other task.

Example 3:

Input: tasks = ["A","A","A", "B","B","B"], n = 3

Output: 10

Explanation: A possible sequence is: A -> B -> idle -> idle -> A -> B -> idle -> idle -> A -> B.

There are only two types of tasks, A and B, which need to be separated by 3 intervals. This leads to idling twice between repetitions of these tasks.

Constraints: