Implementing an LRU Cache in Python: Step-by-Step Guide with O(1) Time Complexity
Learn how to implement a Least Recently Used (LRU) Cache in Python using Doubly Linked Lists and Hash Maps with O(1) get and put operations.
Senior Principal Engineer & Computer Science Fellow
Dr. Alistair Vance holds a PhD in Computer Science from Imperial College London. With over 14 years of experience in low-latency systems and algorithmic research, he has authored 18 peer-reviewed papers on distributed data structures and memory optimization algorithms.
Learn how to implement a Least Recently Used (LRU) Cache in Python using Doubly Linked Lists and Hash Maps with O(1) get and put operations.
Step-by-step implementation of Dijkstra algorithm for weighted graphs using heapq in O((V + E) log V) time complexity.
Understand the Global Interpreter Lock (GIL) in Python 3.12, and learn when to choose multithreading vs multiprocessing for CPU-bound tasks.
Learn how Min-Heap and Max-Heap array representations maintain heap invariants with swim (sift-up) and sink (sift-down) in C++.
Implement a Trie (Prefix Tree) in Python for fast O(L) string insertions, prefix lookups, and auto-complete search bars.
In-depth analysis of OS CPU scheduling algorithms including Round Robin quantum sizing and Linux Completely Fair Scheduler (CFS) red-black tree virtual runtime.
Explore virtual memory paging, page faults, Belady anomaly in FIFO, and Second-Chance Clock algorithm in modern operating systems.