LeetCode 1882 | Process Tasks Using Servers | Java | Medium | Dual Heaps Simulation | CodeByTushu
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In today’s video, we solve LeetCode 1882 — Process Tasks Using Servers using Dual Heaps, Priority Queue & Event-Driven Simulation in Java.
In this problem, we are given n servers with assigned weights and m tasks with execution durations. Tasks arrive one by one at each second j starting from second 0. Whenever a task is ready, it must be assigned to an available free server with the smallest weight, breaking ties with the smallest server index. If all servers are occupied, incoming tasks wait in queue until a server finishes its workload. Our goal is to determine the assigned server index for every task.
We model this system using an optimal dual priority queue architecture. One min-heap maintains available free servers ordered by weight and index, while a second min-heap tracks busy servers ordered by their completion timestamps. As simulation time advances, finished servers are released from the busy heap into the free heap. When all servers are busy, we fast-forward the simulation clock directly to the earliest completion time, avoiding costly second-by-second increments. This guarantees optimal O((N + M) log N) time complexity and O(N + M) auxiliary space.
🔥 Problem Highlights
✅ Dual priority queue architecture cleanly separates free and busy server states
✅ Free server min-heap enforces priority by lowest weight and breaks ties by lowest index
✅ Busy server min-heap tracks active tasks by earliest completion timestamp
✅ Fast-forwarding time directly to earliest completion avoids Time Limit Exceeded
✅ Amortized efficiency: each task and server pushes and pops at most a constant number of times
✅ Optimal O((N + M) log N) time complexity with O(N + M) auxiliary space
⏱️ TIMESTAMPS
00:00 Intro & Day 105 Streak Celebration
00:32 Problem Understanding & Constraints Analysis
01:32 Key Breakthrough: Dual-Heap Event-Driven Simulation
02:22 5-Step Dual Priority Queue Pipeline
03:25 Optimal Java 17 Solution Walkthrough Line by Line
04:37 Test Cases Walkthrough & Day 105 Streak Celebration
05:05 Time Complexity Analysis (O((N + M) log N))
05:42 Space Complexity Analysis (O(N + M) Auxiliary Space)
06:10 Summary & Outro!
📂 Problem Folder & Detailed Documentation:
https://github.com/CodeByTushu/CodeByTushu-LeetCode-Solutions/tree/main/solutions/November/07-November/1882-Process-Tasks-Using-Servers
☕ Java Solution:
https://github.com/CodeByTushu/CodeByTushu-LeetCode-Solutions/blob/main/solutions/November/07-November/1882-Process-Tasks-Using-Servers/Solution.java
🐍 Python Solution:
https://github.com/CodeByTushu/CodeByTushu-LeetCode-Solutions/blob/main/solutions/November/07-November/1882-Process-Tasks-Using-Servers/solution.py
⚡ C++ Solution:
https://github.com/CodeByTushu/CodeByTushu-LeetCode-Solutions/blob/main/solutions/November/07-November/1882-Process-Tasks-Using-Servers/solution.cpp
🌐 JavaScript Solution:
https://github.com/CodeByTushu/CodeByTushu-LeetCode-Solutions/blob/main/solutions/November/07-November/1882-Process-Tasks-Using-Servers/solution.js
🔗 LeetCode Source Code
📌 https://codebytushu.com/leetcode/problem/process-tasks-using-servers
🌐 All LeetCode Solutions
📌 https://codebytushu.com/Leetcode/
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🔥 Perfect For
LeetCode Medium Problems
Java DSA Preparation
Dual Priority Queue
Event-Driven Simulation
Task Scheduling Algorithms
Time Complexity Optimization
Coding Interview Preparation
Problem Solving
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💬 Question of the Day
When all servers are busy and a new task arrives, why is it optimal to jump time t directly to the earliest server free time instead of incrementing second-by-second?
1️⃣ It prevents TLE by skipping long idle intervals where no server status changes
2️⃣ It reduces the space complexity of the priority queue to O(1)
3️⃣ It guarantees that tasks are executed out of order
4️⃣ It avoids sorting servers by their weights
#LeetCode1882 #LeetCode #ProcessTasksUsingServers #Java #JavaDSA #PriorityQueue #Heap #Simulation #TaskScheduling #DualHeaps #DSA #CodingInterview #ProblemSolving #LeetCodeMedium #DailyLeetCode #JavaProgramming #CodeByTushu
👇 Comment the answer below! 🔥 Close description
In today’s video, we solve LeetCode 1882 — Process Tasks Using Servers using Dual Heaps, Priority Queue & Event-Driven Simulation in Java.
In this problem, we are given n servers with assigned weights and m tasks with execution durations. Tasks arrive one by one at each second j starting from second 0. Whenever a task is ready, it must be assigned to an available free server with the smallest weight, breaking ties with the smallest server index. If all servers are occupied, incoming tasks wait in queue until a server finishes its workload. Our goal is to determine the assigned server index for every task.
We model this system using an optimal dual priority queue architecture. One min-heap maintains available free servers ordered by weight and index, while a second min-heap tracks busy servers ordered by their completion timestamps. As simulation time advances, finished servers are released from the busy heap into the free heap. When all servers are busy, we fast-forward the simulation clock directly to the earliest completion time, avoiding costly second-by-second increments. This guarantees optimal O((N + M) log N) time complexity and O(N + M) auxiliary space.
🔥 Problem Highlights
✅ Dual priority queue architecture cleanly separates free and busy server states
✅ Free server min-heap enforces priority by lowest weight and breaks ties by lowest index
✅ Busy server min-heap tracks active tasks by earliest completion timestamp
✅ Fast-forwarding time directly to earliest completion avoids Time Limit Exceeded
✅ Amortized efficiency: each task and server pushes and pops at most a constant number of times
✅ Optimal O((N + M) log N) time complexity with O(N + M) auxiliary space
⏱️ TIMESTAMPS
00:00 Intro & Day 105 Streak Celebration
00:32 Problem Understanding & Constraints Analysis
01:32 Key Breakthrough: Dual-Heap Event-Driven Simulation
02:22 5-Step Dual Priority Queue Pipeline
03:25 Optimal Java 17 Solution Walkthrough Line by Line
04:37 Test Cases Walkthrough & Day 105 Streak Celebration
05:05 Time Complexity Analysis (O((N + M) log N))
05:42 Space Complexity Analysis (O(N + M) Auxiliary Space)
06:10 Summary & Outro!
📂 Problem Folder & Detailed Documentation:
https://github.com/CodeByTushu/CodeByTushu-LeetCode-Solutions/tree/main/solutions/November/07-November/1882-Process-Tasks-Using-Servers
☕ Java Solution:
https://github.com/CodeByTushu/CodeByTushu-LeetCode-Solutions/blob/main/solutions/November/07-November/1882-Process-Tasks-Using-Servers/Solution.java
🐍 Python Solution:
https://github.com/CodeByTushu/CodeByTushu-LeetCode-Solutions/blob/main/solutions/November/07-November/1882-Process-Tasks-Using-Servers/solution.py
⚡ C++ Solution:
https://github.com/CodeByTushu/CodeByTushu-LeetCode-Solutions/blob/main/solutions/November/07-November/1882-Process-Tasks-Using-Servers/solution.cpp
🌐 JavaScript Solution:
https://github.com/CodeByTushu/CodeByTushu-LeetCode-Solutions/blob/main/solutions/November/07-November/1882-Process-Tasks-Using-Servers/solution.js
🔗 LeetCode Source Code
📌 https://codebytushu.com/leetcode/problem/process-tasks-using-servers
🌐 All LeetCode Solutions
📌 https://codebytushu.com/Leetcode/
📱 Join CodeByTushu on WhatsApp
Get coding updates, LeetCode solutions, daily questions and more.
https://whatsapp.com/channel/0029Vb8k5mCDeON2Xwlkxe40
❤️ Donate
📌 https://codebytushu.com/Leetcode/donate.php
🔥 Perfect For
LeetCode Medium Problems
Java DSA Preparation
Dual Priority Queue
Event-Driven Simulation
Task Scheduling Algorithms
Time Complexity Optimization
Coding Interview Preparation
Problem Solving
🌐 Follow CodeByTushu
▶️ YouTube: https://www.youtube.com/@codebytushu
📸 Instagram: https://instagram.com/codebytushu/
💼 LinkedIn: https://www.linkedin.com/company/codebytushu/
🌍 Website: https://codebytushu.com
🐙 GitHub: https://github.com/CodeByTushu
❤️ Don’t Forget To
👍 Like | 💬 Comment | 🔔 Subscribe
💬 Question of the Day
When all servers are busy and a new task arrives, why is it optimal to jump time t directly to the earliest server free time instead of incrementing second-by-second?
1️⃣ It prevents TLE by skipping long idle intervals where no server status changes
2️⃣ It reduces the space complexity of the priority queue to O(1)
3️⃣ It guarantees that tasks are executed out of order
4️⃣ It avoids sorting servers by their weights
#LeetCode1882 #LeetCode #ProcessTasksUsingServers #Java #JavaDSA #PriorityQueue #Heap #Simulation #TaskScheduling #DualHeaps #DSA #CodingInterview #ProblemSolving #LeetCodeMedium #DailyLeetCode #JavaProgramming #CodeByTushu
👇 Comment the answer below! 🔥 Close description
