MongoDB·Software Engineer·Technical Phone Screen·Senior

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Jun 2026Remote

Summary

MongoDB coding round, pretty much one meaty question that sprawled into a whole systems conversation. The LRU implementation itself was fine but the thread-safety follow-up is where things got interesting and a little uncomfortable.

Questions Asked (2)

Implement an LRU cache with O(1) get and put operations using a hash map and doubly linked list.

Algorithms & Data Structures

Author's notes

Felt pretty solid here.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements: O(1) get and put, capacity limit, and eviction policy (least recently used). Then explain that a hash map provides O(1) access to nodes, while a doubly linked list maintains the usage order, allowing O(1) removal and insertion. Finally, walk through the implementation details, including edge cases like updating existing keys and handling capacity overflow.

Pro tip: Mention that you would use a doubly linked list with sentinel head and tail nodes to simplify edge cases and avoid null checks, and discuss how this design ensures thread safety if needed (e.g., using locks or concurrent data structures).

1. Clarify requirements and constraints

Confirm the capacity, expected operations (get, put), and eviction policy (LRU). Ask about concurrency requirements and whether the cache needs to be thread-safe.

2. Design the data structures

Explain that a hash map (key -> node) provides O(1) access, and a doubly linked list maintains the order of usage. The list should have sentinel head and tail nodes to simplify operations.

3. Define node structure and helper methods

Describe the node with key, value, prev, and next pointers. Outline helper methods like addToHead, removeNode, moveToHead, and removeTail to encapsulate list operations.

4. Implement get and put operations

For get: if key exists, move node to head and return value; else return -1. For put: if key exists, update value and move to head; else create new node, add to head, and if capacity exceeded, remove tail and delete from map.

5. Analyze complexity and edge cases

State that both operations are O(1) time and O(capacity) space. Discuss edge cases: capacity 0 or 1, updating existing key, and handling null keys/values if applicable.

Key Points to Mention

Hash map provides O(1) lookup by key, mapping to nodes in the linked list.
Doubly linked list allows O(1) removal and insertion, maintaining LRU order.
Sentinel head and tail nodes eliminate edge cases for empty list and boundary operations.
On get, move accessed node to head (most recently used).
On put, if capacity exceeded, remove tail node (least recently used) and delete its key from map.
Time complexity: O(1) for both get and put; space complexity: O(capacity).

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

How would you make your LRU cache thread-safe under concurrent access? Walk through the trade-offs of different locking strategies.

System DesignTechnical Trade-offs

Author's notes

This is where I started sweating a bit.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements: expected read/write ratio, cache size, and latency constraints. Then present a progression of locking strategies from coarse-grained to fine-grained, discussing trade-offs in throughput, latency, complexity, and correctness. Conclude with a recommendation based on typical MongoDB workloads.

Pro tip: Mention that the choice depends on the read/write ratio: read-heavy workloads benefit from read-write locks or lock striping, while write-heavy or mixed workloads may need finer-grained locking or lock-free approaches. Also, highlight that LRU inherently requires updating recency on every access, which complicates lock-free designs.

1. Clarify Requirements and Assumptions

Ask about expected concurrency level, read/write ratio, cache size, and latency goals. State assumptions to guide the discussion.

2. Present Coarse-Grained Locking

Describe using a single mutex for the entire cache. Discuss simplicity and correctness, but highlight poor scalability due to contention.

3. Explore Fine-Grained Locking Strategies

Introduce read-write locks, lock striping (e.g., partitioning the cache into segments with separate locks), and per-node locking. Compare their trade-offs in throughput, latency, and implementation complexity.

4. Discuss Lock-Free and Optimistic Approaches

Mention lock-free data structures (e.g., using atomic operations) and optimistic concurrency (e.g., versioning). Note challenges like ABA problem and the difficulty of maintaining LRU order without locks.

5. Recommend and Justify

Based on the clarified requirements, recommend a strategy (e.g., lock striping for read-heavy workloads) and justify it by summarizing the trade-offs.

Key Points to Mention

Read-write locks allow concurrent reads but serialize writes, improving throughput for read-heavy workloads.
Lock striping (e.g., partitioning the cache into N segments with independent locks) reduces contention but complicates global LRU eviction and may lead to suboptimal eviction decisions.
Per-node locking (locking individual entries) offers high concurrency but introduces deadlock risks and overhead from acquiring multiple locks.
Lock-free approaches using atomic operations (e.g., CAS) can avoid locks but are complex, especially for maintaining LRU order; often require approximate LRU or alternative eviction policies.
Trade-offs include throughput vs. latency, simplicity vs. performance, and correctness guarantees (e.g., strict LRU vs. approximate).
Consider using existing concurrent data structures (e.g., ConcurrentHashMap in Java) as a building block, but note that they don't provide LRU ordering.

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