#146Hard~50 min
LRU Cache
Time O(1) · Space O(k) · Official statement on LeetCode
Solutions
// Time: O(1), per operation.
// Space: O(k), k is the capacity of cache.
#include <list>
class LRUCache {
public:
LRUCache(int capacity) : capa_(capacity) {
}
int get(int key) {
if (!map_.count(key)) {
return -1;
}
// It key exists, update it.
const auto value = map_[key]->second;
update(key, value);
return value;
}
void put(int key, int value) {
if (capa_ <= 0) {
return;
}
// If cache is full while inserting, remove the last one.
if (!map_.count(key) && list_.size() == capa_) {
auto del = list_.front(); list_.pop_front();
map_.erase(del.first);
}
update(key, value);
}
private:
list<pair<int, int>> list_; // key, value
unordered_map<int, list<pair<int, int>>::iterator> map_; // key, list iterator
int capa_;
// Update (key, iterator of (key, value)) pair
void update(int key, int value) {
auto it = map_.find(key);
if (it != map_.end()) {
list_.erase(it->second);
}
list_.emplace_back(key, value);
map_[key] = prev(end(list_));
}
};
// Time: O(1), per operation.
// Space: O(k), k is the capacity of cache.
class LRUCache2 {
public:
LRUCache2(int capacity) : capa_(capacity) {
}
int get(int key) {
if (!map_.count(key)) {
return -1;
}
// It key exists, update it.
const auto value = map_[key]->second;
update(key, value);
return value;
}
void put(int key, int value) {
if (capa_ <= 0) {
return;
}
// If cache is full while inserting, remove the last one.
if (!map_.count(key) && list_.size() == capa_) {
auto del = list_.back(); list_.pop_back();
map_.erase(del.first);
}
update(key, value);
}
private:
list<pair<int, int>> list_; // key, value
unordered_map<int, list<pair<int, int>>::iterator> map_; // key, list iterator
int capa_;
// Update (key, iterator of (key, value)) pair
void update(int key, int value) {
auto it = map_.find(key);
if (it != map_.end()) {
list_.erase(it->second);
}
list_.emplace_front(key, value);
map_[key] = list_.begin();
}
};
Beginner Explanation
What is LRU Cache?
LRU Cache (LeetCode #146) is a Hard problem that primarily trains design.
How to think about it
- Restate the goal in your own words before coding.
- Work a tiny example by hand so the invariant becomes obvious.
- Identify the pattern — this problem aligns with general problem-solving.
- Only then translate the idea into code.
Why this problem matters
Hard problems force you to combine patterns and prove complexity carefully — interview gold. Official solution notes mention: OrderedDict.
AlgoForge explanations are original teaching notes. Always open the official problem statement on LeetCode for constraints and examples.
Interview Walkthrough
Interview approach for LRU Cache
Opening (30–60 seconds)
- Clarify inputs/outputs and edge cases (empty input, single element, duplicates, overflow).
- State a brute force so the interviewer knows you can solve it naively.
- Propose the optimal direction tied to general problem-solving.
Core solution narrative
- Define the state you track (pointers, DP cell, set membership, stack top, etc.).
- Explain the transition when you process the next element.
- Call out time (O(1)) and space (O(k)) before coding.
- Code cleanly; narrate variable names.
What interviewers listen for
- Correctness on edge cases
- Complexity honesty
- Ability to discuss trade-offs (e.g., hash map space vs. sort + two pointers)
Follow-up questions they may ask
- Can you solve it with less memory?
- What if the input stream is infinite / doesn't fit in RAM?
- How would tests look for adversarial inputs?
Optimized Approach
Optimized solution notes
The reference solutions on AlgoForge target O(1) time and O(k) space.
Pattern focus: general problem-solving
Use the pattern as a checklist:
- Identify the dominant pattern and stick to one clear invariant
Multiple methods appear in the source solutions — compare them and explain when each is preferable.
Implementation tips
- Prefer readable names over micro-optimizations in interviews.
- Extract helpers only when they clarify (e.g., expand-around-center, DFS visit).
- After AC-level logic, re-scan for off-by-one and null checks.
Complexity Analysis
Complexity
| Measure | Bound |
|---|---|
| Time | O(1) |
| Space | O(k) |
How to justify this in an interview
- Time: count loops, map/set operations, and recursive branching; state average vs worst case if relevant.
- Space: include hash maps, recursion stack, and output allocation when the problem asks for it.
If your implementation differs from the reference, re-derive big-O from your code — never memorize a complexity you cannot defend.
Common Mistakes
Common mistakes on LRU Cache
- Skipping edge cases — empty collections, single-element inputs, max constraints.
- Wrong invariant for general problem-solving — updating state too early or too late.
- Mutating input unexpectedly when the problem forbids it.
- Off-by-one in windows, ranges, or binary search bounds.
- Ignoring overflow / precision for integer arithmetic problems.
- Overengineering — jumping to an advanced structure when a simpler approach works.
Alternative Approaches
Alternatives
The source file includes more than one method. Compare:
- Primary optimized path — best complexity for typical interviews.
- Secondary approach — often brute force, sorting-based, or space-optimized variant.
Practice articulating when you would pick each (constraints, readability, follow-ups).
Edge Cases
Edge cases checklist
- Minimum input size
- Maximum input size / time limits
- Duplicates and already-sorted input
- Negative numbers / zeros (if applicable)
- Disconnected structures (graphs/trees)
- Single path vs branching recursion depth
Pattern Recognition
Spotting this pattern
Signal phrases that point to general problem-solving:
- Sorted input or ability to sort without changing the answer class
- Need for contiguous subarray / substring → consider sliding window
- Need for O(1) membership → hash set/map
- Optimal substructure + overlapping subproblems → DP
- Connectivity / components → graph DFS/BFS or Union-Find
Primary topics: design.
Follow-up Interview Questions
Follow-ups
- How does the solution change if the input is a stream?
- Can you solve it in-place?
- What if duplicates must be handled differently?
- How would you parallelize the approach?
- Design tests that would break a buggy implementation.
Practice Recommendations
What to practice next
- Re-solve LRU Cache in a second language (cpp, python).
- Drill 3–5 more problems tagged design.
- Teach the solution out loud in under 5 minutes.
- Add this problem to your revision calendar in 3 days and 14 days.
Visualization
Conceptual diagram for LRU Cache: show input structure (design), highlight the moving parts of the general problem-solving approach, and annotate each step with the maintained invariant and complexity.
Study checklist
- Read the official problem statement on LeetCode
- Solve on paper / whiteboard first
- Implement the general problem-solving approach
- Verify edge cases from the checklist
- State time and space complexity aloud
- Compare with the AlgoForge reference solution
- Schedule a revision session
Revision notes
LRU Cache (#146) — Hard. Pattern: general problem-solving. Complexity: O(1) time / O(k) space. Re-derive the invariant before coding.
FAQs
What is the time complexity of LRU Cache?+
The reference solutions aim for O(1) time and O(k) space. Always re-derive complexity from the code you write in the interview.
What pattern does LRU Cache use?+
It primarily maps to general problem-solving, within the broader topic of design.
Is LRU Cache good for interviews?+
Yes — as a Hard problem it is a solid practice target. Pair it with related problems in the same pattern family for spaced repetition.
Where can I read the official statement?+
Open the official LeetCode page for constraints and examples: https://leetcode.com/problems/lru-cache/