LinkedIn·Software Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Jun 2026

Summary

LinkedIn coding round, one question about implementing a cache with a custom eviction policy. Pretty focused session, no fluff.

Questions Asked (1)

You're given a cache interface where values implement a Rankable interface with a rank() method. Implement the cache with put and get operations such that when capacity is full, the entry with the lowest rank() gets evicted.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

The hashmap part was obvious but I fumbled for a bit on the eviction side.

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AI HintsAI Generated

Suggested Approach

Clarify the requirements and constraints first, then propose a data structure that supports efficient get, put, and eviction based on rank. Discuss the trade-offs between different implementations and consider edge cases like duplicate ranks and concurrency.

Pro tip: Mention that you would use a balanced BST or a heap combined with a hash map to achieve O(log n) operations, and discuss how to handle duplicate ranks by using a tie-breaker like insertion time or a unique ID.

1. Clarify Requirements

Ask about expected capacity, concurrency needs, and whether ranks can change after insertion. Confirm that eviction is based solely on rank and that ties are broken arbitrarily or by a secondary criterion.

2. Choose Data Structures

Propose using a hash map for O(1) key lookup and a balanced binary search tree (or a heap) keyed by rank for O(log n) insertion, deletion, and finding the minimum rank.

3. Handle Duplicate Ranks

Explain that if multiple entries have the same rank, you need a tie-breaker. Suggest using a composite key (rank, insertion timestamp) or a secondary data structure to ensure deterministic eviction.

4. Implement Operations

Detail the put and get logic: on put, if key exists, update value and rank; if new and capacity full, evict the entry with the lowest rank. On get, return the value and optionally update its rank if needed.

5. Discuss Trade-offs and Optimizations

Compare using a heap (O(log n) but lazy deletion) vs. a balanced BST (O(log n) with direct deletion). Mention potential concurrency strategies like locking or lock-free structures if needed.

Key Points to Mention

Time complexity: O(1) for get, O(log n) for put and eviction with a balanced BST or heap.
Space complexity: O(capacity) for storing entries.
Handling duplicate ranks: use a tie-breaker like insertion order or a unique ID.
Concurrency: discuss thread-safety using locks or concurrent data structures.
Edge cases: empty cache, capacity zero, updating existing key, rank changes.
Alternative approaches: using a priority queue with lazy deletion or a TreeMap in Java.

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