LinkedIn·Software Engineer·Onsite - System Design / Architecture·Staff

Staff

May 2026

Summary

LinkedIn EM interview with a system design question around building a ranked cache. Pretty open-ended, which I wasn't fully prepared for.

Questions Asked (1)

Design and implement a ranked cache system.

System DesignAlgorithms & Data StructuresTechnical Trade-offs

Author's notes

I jumped straight into LRU and they pushed back almost immediately, asking why ranking specifically.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a high-level design that combines a hash map for O(1) key lookup with a ranking mechanism (e.g., a heap or balanced tree) to efficiently retrieve top-ranked items. Discuss trade-offs between different data structures and algorithms, and outline how to handle updates, evictions, and concurrency.

Pro tip: Emphasize the importance of defining the ranking criteria and access patterns upfront, as they dictate the optimal data structure choice and can significantly impact performance and scalability.

1. Clarify Requirements

Ask questions to understand the cache size, ranking metric (e.g., frequency, recency, custom score), update frequency, and consistency requirements. This ensures the design meets the actual needs.

2. Choose Data Structures

Select a combination like a hash map for fast key access and a heap or balanced BST for maintaining order by rank. Consider alternatives like a skip list or a combination of hash map and doubly linked list for LRU.

3. Design Core Operations

Define how to implement get, put, update rank, and evict. Ensure operations like updating a rank are efficient (e.g., O(log n) with a heap) and handle edge cases like cache full.

4. Address Scalability and Concurrency

Discuss how to scale the cache (e.g., sharding) and handle concurrent access (e.g., locking, lock-free structures). Mention trade-offs between consistency and performance.

5. Analyze Trade-offs and Optimizations

Compare different approaches (e.g., heap vs. sorted list) in terms of time complexity, memory, and implementation complexity. Suggest optimizations like lazy updates or approximate ranking.

Key Points to Mention

Time complexity of operations: O(1) for lookup, O(log n) for rank updates with heap, O(n) for naive sorted list.
Space complexity and memory overhead of maintaining both a hash map and an ordered structure.
Handling rank updates efficiently: e.g., using a heap with lazy deletion or a balanced BST with parent pointers.
Eviction policy: how to remove the lowest-ranked item when cache is full, and how that interacts with the ranking structure.
Concurrency control: read-write locks, sharding, or lock-free data structures to support high throughput.
Real-world considerations: persistence, TTL, and integration with existing systems (e.g., LinkedIn's caching infrastructure).

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