Category: dsa Date: 2026-02-13
Merge K Sorted Lists System Design Discussion
1. Requirements (Functional + Non-functional)
2. High-Level Architecture
The high-level architecture of the system consists of the following components:
3. Database Design
Assuming an RDBMS (Relational Database Management System) like MySQL or PostgreSQL:
4. Scaling Strategy
To scale the system, we can:
5. Bottlenecks
Potential bottlenecks:
6. Trade-offs
Trade-offs:
Solution using the First Principle of System Design: Separation of Concerns
The first principle of system design is Separation of Concerns. In the context of the Merge K Sorted Lists problem, we can separate the concerns of:
By separating these concerns, we can design a modular system that is easier to maintain, scale, and extend.
Pseudocode
class ListMergingNode:
def merge(self, list1, list2):
# Merge two sorted lists
result = []
while list1 and list2:
if list1.val <= list2.val:
result.append(list1.val)
list1 = list1.next
else:
result.append(list2.val)
list2 = list2.next
result.extend(list1 or list2)
return result
class KListMerger:
def merge_k_lists(self, lists):
# Merge K sorted lists
result = []
priority_queue = []
for list in lists:
# Push each list onto the priority queue
heapq.heappush(priority_queue, (list[0], list))
while priority_queue:
# Pop the smallest element from the priority queue
val, list = heapq.heappop(priority_queue)
result.append(val)
# Push the next element from the list onto the priority queue
if list.next:
heapq.heappush(priority_queue, (list.next.val, list.next))
return result
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