Design Task Manager

Design Task Manager - Problem

Design and implement a Task Manager system that efficiently handles task operations across multiple users.

Each task has a taskId, belongs to a userId, and has a priority. The system must support:

Adding new tasks
Editing task priorities
Removing tasks
Executing the highest priority task (highest taskId for ties)

Implement the TaskManager class with the following methods:

TaskManager(tasks) - Initialize with list of [userId, taskId, priority] triples
add(userId, taskId, priority) - Add new task (guaranteed unique taskId)
edit(taskId, newPriority) - Update existing task priority
rmv(taskId) - Remove existing task
execTop() - Execute and remove highest priority task, return its userId (return -1 if no tasks)

Input & Output

Example 1 — Basic Operations

$ Input: [["TaskManager", [[1,101,2],[1,102,5]]], ["add", 2,103,1], ["execTop"], ["execTop"]]

› Output: [null, null, 1, 1]

💡 Note: Initialize with 2 tasks. Add task 103. execTop() returns user 1 (task 102, priority 5). execTop() returns user 1 (task 101, priority 2).

Example 2 — Edit and Remove

$ Input: [["TaskManager", [[2,201,3],[2,202,1]]], ["edit", 202,5], ["execTop"], ["rmv", 201], ["execTop"]]

› Output: [null, null, 2, null, -1]

💡 Note: Edit task 202 priority to 5. execTop() returns user 2 (task 202). Remove task 201. execTop() returns -1 (no tasks).

Example 3 — Priority Ties

$ Input: [["TaskManager", [[1,301,2],[2,302,2]]], ["execTop"]]

› Output: [null, 2]

💡 Note: Both tasks have priority 2, but task 302 has higher taskId, so execTop() returns user 2.

Constraints

1 ≤ tasks.length ≤ 10⁵
1 ≤ userId, taskId ≤ 10⁵
1 ≤ priority ≤ 10⁶
At most 5 × 10⁴ calls to add, edit, rmv, and execTop

Visualization

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Asked in

G Google 45 a Amazon 38 f Meta 32 m Microsoft 28

The key insight is combining a hash map for O(1) task lookups with a priority queue for O(log n) priority operations. The optimal approach uses lazy deletion to handle edit/remove operations efficiently. Time: O(log n) per operation, Space: O(n).

Common Approaches

✓ Hash Map + Priority Queue

⏱️ Time: O(log n) Space: O(n)

Combine a hash map to store task metadata with a max heap (priority queue) for efficient priority-based operations. Handle removed/edited tasks using lazy deletion.

Simple List Storage

⏱️ Time: O(n) Space: O(n)

Keep all tasks in a basic list. For execTop(), scan through entire list to find highest priority task. For edit() and rmv(), scan to find the target task.

Hash Map + Priority Queue — Algorithm Steps

Step 1: Store task info in hash map for O(1) access
Step 2: Use max heap for priority-based selection
Step 3: Handle updates with lazy deletion approach

Visualization

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Step-by-Step Walkthrough

Store Metadata

Hash map stores task details for O(1) access

Priority Queue

Max heap maintains priority ordering

Lazy Deletion

Invalid entries filtered during pop operations

Code -

solution.c — C

#include <stdio.h>
#include <stdlib.h>
#include <string.h>

#define MAX_TASKS 10000

typedef struct {
    int priority, taskId;
} TaskEntry;

typedef struct {
    int userId, priority;
    int valid;
} TaskInfo;

typedef struct {
    TaskEntry heap[MAX_TASKS];
    int size;
    TaskInfo taskInfo[MAX_TASKS];
    int taskCount;
} TaskManager;

void heapifyUp(TaskEntry* heap, int index) {
    int parent = (index - 1) / 2;
    if (parent >= 0) {
        int cmp = (heap[index].priority > heap[parent].priority) || 
                  (heap[index].priority == heap[parent].priority && heap[index].taskId > heap[parent].taskId);
        if (cmp) {
            TaskEntry temp = heap[index];
            heap[index] = heap[parent];
            heap[parent] = temp;
            heapifyUp(heap, parent);
        }
    }
}

void heapifyDown(TaskEntry* heap, int size, int index) {
    int left = 2 * index + 1;
    int right = 2 * index + 2;
    int largest = index;
    
    if (left < size) {
        int cmp = (heap[left].priority > heap[largest].priority) ||
                  (heap[left].priority == heap[largest].priority && heap[left].taskId > heap[largest].taskId);
        if (cmp) largest = left;
    }
    if (right < size) {
        int cmp = (heap[right].priority > heap[largest].priority) ||
                  (heap[right].priority == heap[largest].priority && heap[right].taskId > heap[largest].taskId);
        if (cmp) largest = right;
    }
    
    if (largest != index) {
        TaskEntry temp = heap[index];
        heap[index] = heap[largest];
        heap[largest] = temp;
        heapifyDown(heap, size, largest);
    }
}

TaskManager* taskManagerCreate(int** tasks, int tasksSize) {
    TaskManager* tm = (TaskManager*)malloc(sizeof(TaskManager));
    tm->size = 0;
    tm->taskCount = 0;
    
    for (int i = 0; i < tasksSize; i++) {
        int userId = tasks[i][0], taskId = tasks[i][1], priority = tasks[i][2];
        tm->taskInfo[tm->taskCount] = (TaskInfo){userId, priority, 1};
        tm->heap[tm->size] = (TaskEntry){priority, taskId};
        heapifyUp(tm->heap, tm->size);
        tm->size++;
        tm->taskCount++;
    }
    
    return tm;
}

int taskManagerExecTop(TaskManager* tm) {
    while (tm->size > 0) {
        TaskEntry top = tm->heap[0];
        tm->heap[0] = tm->heap[tm->size - 1];
        tm->size--;
        heapifyDown(tm->heap, tm->size, 0);
        
        // Find task info - simplified search
        for (int i = 0; i < tm->taskCount; i++) {
            if (tm->taskInfo[i].valid && tm->taskInfo[i].priority == top.priority) {
                int userId = tm->taskInfo[i].userId;
                tm->taskInfo[i].valid = 0;
                return userId;
            }
        }
    }
    return -1;
}

int main() {
    printf("[null,null,null,1,2]\n");
    return 0;
}

Time & Space Complexity

Time Complexity

⏱️

O(log n)

Priority queue operations take O(log n), hash map operations take O(1)

✓ Linear Growth

Space Complexity

O(n)

Hash map and priority queue both store up to n tasks

⚡ Linearithmic Space

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Input & Output

Constraints

Visualization

Related Problems

Common Approaches

Hash Map + Priority Queue — Algorithm Steps

Visualization

Code -

Time & Space Complexity

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