#3408

Medium

LeetCode Problem Workspace

Design Task Manager

Design a Task Manager that can efficiently handle task management operations such as adding, editing, executing, and removing tasks based on priorities.

hash table design heap priority queue ordered set

Problem Statement

You are tasked with designing a TaskManager system that handles a list of tasks. Each task is associated with a user ID, task ID, and priority. Your system should support four operations: adding tasks, editing task priorities, removing tasks, and executing the highest priority task. The system must be able to manage a large number of tasks efficiently.

Implement the TaskManager class with methods to add, edit, remove, and execute tasks. For each operation, the system must handle a range of tasks and users with varying priorities. Ensure that the tasks are efficiently stored and accessed, with a focus on optimizing the execution of the highest priority task.

Examples

Example 1

Input: ["TaskManager", "add", "edit", "execTop", "rmv", "add", "execTop"] [[[[1, 101, 10], [2, 102, 20], [3, 103, 15]]], [4, 104, 5], [102, 8], [], [101], [5, 105, 15], []]

Output: [null, null, null, 3, null, null, 5] Explanation

Example details omitted.

Constraints

1 <= tasks.length <= 105
0 <= userId <= 105
0 <= taskId <= 105
0 <= priority <= 109
0 <= newPriority <= 109
At most 2 * 105 calls will be made in total to add, edit, rmv, and execTop methods.
The input is generated such that taskId will be valid.

Solution Approach

Use Hash Table for Task Storage

To efficiently store and access tasks, use a Hash Table. The key will be the task ID, and the value will be the task details, such as the user ID, task ID, and priority. This ensures O(1) time complexity for adding, editing, and removing tasks.

Implement Heap for Priority Management

Use a Min-Heap or Max-Heap to efficiently manage tasks by priority. When executing tasks, the highest or lowest priority task can be accessed in O(log n) time, ensuring that task execution is optimized. Each task’s priority will be tracked, and the heap will be adjusted when priorities are edited.

Efficiently Handle Task Operations

For each operation, adjust the data structures accordingly. Adding a task involves inserting into the hash table and heap. Editing a task’s priority involves updating both the hash table and re-adjusting the heap. Removing tasks can be done by deleting them from both the hash table and heap. Executing the top priority task should be done in logarithmic time by accessing the heap.

Complexity Analysis

Metric	Value
Time	Depends on the final approach
Space	Depends on the final approach

The time complexity of each operation is determined by the data structures used. Adding and removing tasks from the hash table takes O(1). For the heap, adding and removing tasks takes O(log n), and executing the highest priority task also takes O(log n). Therefore, the overall time complexity for each operation is O(log n) due to heap adjustments.

What Interviewers Usually Probe

Understanding how to combine a hash table for storage and a heap for priority management will be crucial.
The candidate should be able to explain how the operations affect the time complexity.
Efficient memory management while performing these operations should be discussed, especially with large inputs.

Common Pitfalls or Variants

Common pitfalls

Failing to update the heap properly when task priorities change can lead to incorrect execution order.
Not considering edge cases such as executing or removing tasks from an empty system.
Overcomplicating the data structures or operations instead of using a simple Hash Table and Heap combination.

Follow-up variants

Implementing a Task Manager that handles multiple users with different priorities for tasks.
Expanding the system to support additional operations such as querying the number of tasks left with certain priorities.
Optimizing the Task Manager to handle larger datasets with reduced time complexity for each operation.

FAQ

What is the primary data structure used in the Design Task Manager?

The primary data structures used in this problem are a Hash Table for storing tasks and a Heap (Priority Queue) for managing task priorities.

How do I efficiently manage task priorities in this problem?

To efficiently manage task priorities, use a heap to track the highest or lowest priority task and adjust the heap as tasks are added or priorities are updated.

What is the time complexity of each operation in the Task Manager?

The time complexity for adding, editing, and removing tasks is O(log n) due to heap operations, while accessing the hash table takes O(1) for most tasks.

How does the Task Manager handle edge cases such as empty task lists?

The system should handle edge cases by ensuring that it doesn't attempt to execute or remove tasks when no tasks are present in the system.

How can GhostInterview assist with solving this problem efficiently?

GhostInterview helps by guiding candidates through the use of appropriate data structures (hash table and heap) and explaining how to manage large inputs with optimal time complexity.

terminal

Solution

Solution 1: Hash Map + Ordered Set

We use a hash map $\text{d}$ to store task information, where the key is the task ID and the value is a tuple $(\text{userId}, \text{priority})$ representing the user ID and the priority of the task.

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class TaskManager:

    def __init__(self, tasks: List[List[int]]):
        self.d = {}
        self.st = SortedList()
        for task in tasks:
            self.add(*task)

    def add(self, userId: int, taskId: int, priority: int) -> None:
        self.d[taskId] = (userId, priority)
        self.st.add((-priority, -taskId))

    def edit(self, taskId: int, newPriority: int) -> None:
        userId, priority = self.d[taskId]
        self.st.discard((-priority, -taskId))
        self.d[taskId] = (userId, newPriority)
        self.st.add((-newPriority, -taskId))

    def rmv(self, taskId: int) -> None:
        _, priority = self.d[taskId]
        self.d.pop(taskId)
        self.st.remove((-priority, -taskId))

    def execTop(self) -> int:
        if not self.st:
            return -1
        taskId = -self.st.pop(0)[1]
        userId, _ = self.d[taskId]
        self.d.pop(taskId)
        return userId


# Your TaskManager object will be instantiated and called as such:
# obj = TaskManager(tasks)
# obj.add(userId,taskId,priority)
# obj.edit(taskId,newPriority)
# obj.rmv(taskId)
# param_4 = obj.execTop()

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