Salesforce·Software Engineer·Technical Phone Screen·Senior

SeniorPrefer not to say

May 2026Remote

Summary

Salesforce coding round, got hit with the LFU cache problem. Classic hard-tier design question that looks manageable until you remember the O(1) constraint applies to both operations.

Questions Asked (1)

Design and implement an LFU (Least Frequently Used) cache class with get and put operations, both running in O(1) average time. On capacity overflow, evict the least frequently used key, and break ties by recency.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I knew the LRU version cold, so I figured this would be a small step up.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and edge cases, then propose a design using a combination of a hash map and a doubly linked list of frequency buckets. Explain how each operation achieves O(1) average time by maintaining pointers and updating frequencies, and finally discuss trade-offs and potential optimizations.

Pro tip: Mention that LFU with tie-breaking by recency is essentially a combination of LFU and LRU, and that using a min-heap would not achieve O(1) operations, so a bucket-based approach is necessary.

1. Clarify Requirements and Edge Cases

Ask about capacity constraints, expected operations, and tie-breaking rules. Confirm that both get and put must be O(1) average time and that ties are broken by recency (LRU among least frequently used).

2. Design Data Structures

Propose using a hash map for key-to-node mapping and a doubly linked list of frequency buckets, where each bucket contains a doubly linked list of nodes with the same frequency. This allows O(1) access, update, and eviction.

3. Detail Operations

Explain how get increments the frequency and moves the node to the appropriate bucket, and how put inserts or updates, evicting the least frequently used node (and least recently used within that frequency) when capacity is exceeded.

4. Analyze Complexity and Trade-offs

Discuss why the design achieves O(1) average time for both operations, and compare with alternative approaches like using a min-heap (O(log n)) or a simple LRU cache. Mention space complexity and potential optimizations.

5. Test with Examples

Walk through a few test cases, including capacity overflow and tie-breaking scenarios, to demonstrate correctness and validate the design.

Key Points to Mention

Use a hash map for O(1) key lookup and a doubly linked list of frequency buckets to maintain order.
Each node stores key, value, frequency, and pointers to prev/next within its frequency list.
Maintain a pointer to the minimum frequency bucket for O(1) eviction.
On get, increment frequency and move node to the next frequency bucket, creating it if necessary.
On put, if key exists, update value and frequency; if new, insert with frequency 1 and evict if over capacity.
Tie-breaking by recency: within the same frequency, evict the least recently used node (tail of the list).

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.