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Microsoft·Software Engineer·Onsite - Coding / Algorithms·Senior

SeniorPrefer not to say
Apr 2026

Summary

Microsoft SWE interview that went deep on caching internals. Started with a classic LRU implementation and then got pushed into concurrency territory pretty quickly, which I wasn't fully prepared for.

Questions Asked (2)

Q1

Implement an LRU cache that supports get and put operations in O(1) average time.

Algorithms & Data StructuresSystem Design
Author's notes

Knew this one cold.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements: O(1) get and put, capacity limit, and eviction of least recently used item. Then propose a hash map combined with a doubly linked list, explaining how each operation maintains O(1) time. 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 that you would consider thread-safety if the cache is used in a concurrent environment. This shows attention to robustness and real-world usage.

1. Clarify requirements and constraints

Confirm the expected operations (get, put), capacity behavior, and whether thread-safety is needed. Ask about the programming language and any specific API expectations.

2. Choose data structures

Explain that a hash map provides O(1) access to nodes, and a doubly linked list maintains recency order. Together they enable O(1) get and put.

3. Design the algorithm

Describe how get moves the accessed node to the front (most recently used), and put inserts or updates a node, evicting the least recently used (tail) if capacity is exceeded.

4. Handle edge cases

Discuss updating an existing key, evicting when at capacity, and handling capacity of 0 or 1. Mention using sentinel nodes to simplify list operations.

5. Analyze complexity and potential optimizations

State that both operations are O(1) average time and O(capacity) space. Optionally mention thread-safety using locks or concurrent data structures.

Key Points to Mention

  • Hash map for O(1) key lookup, mapping keys to nodes in the linked list.
  • Doubly linked list to maintain recency order, with most recently used at the head and least recently used at the tail.
  • On get: if key exists, move the corresponding node to the head and return its value; else return -1 (or null).
  • On put: if key exists, update value and move node to head; else create new node, add to head, and if capacity exceeded, remove tail node and delete its key from the map.
  • Use sentinel head and tail nodes to avoid null checks and simplify insertion/removal.
  • Time complexity: O(1) average 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.

Q2

How would you extend the LRU cache to be thread-safe under concurrent reads and writes? Walk through different locking strategies and their trade-offs.

System DesignTechnical Trade-offs
Author's notes

This is where I got a little turned around.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements: what level of concurrency, read/write ratio, and performance constraints. Then systematically compare locking strategies from coarse-grained to fine-grained, discussing trade-offs in throughput, latency, complexity, and correctness. Conclude with a recommendation based on typical usage patterns.

Pro tip: Mention that thread-safety isn't just about locks—consider using read-write locks or lock-free techniques like atomic operations and concurrent data structures, but always validate with stress tests and profiling.

1. Clarify Requirements and Assumptions

Ask about expected read/write ratio, number of threads, latency requirements, and whether strict LRU semantics are necessary. This sets the stage for choosing an appropriate strategy.

2. Describe a Basic Thread-Safe LRU Cache

Explain that a simple approach is to wrap all operations with a single mutex, ensuring correctness but limiting concurrency. Mention that this is easy to implement but can become a bottleneck.

3. Explore Locking Strategies

Discuss coarse-grained locking (single mutex), fine-grained locking (per-bucket or per-entry locks), and read-write locks. For each, outline how they work and their impact on concurrency.

4. Analyze Trade-offs

Compare strategies on throughput, latency, complexity, and scalability. Highlight that fine-grained locking improves concurrency but increases deadlock risk and overhead, while read-write locks favor read-heavy workloads.

5. Recommend and Justify

Based on the clarified requirements, recommend a strategy (e.g., read-write lock for read-heavy, or sharded locks for high concurrency) and justify it with trade-offs.

Key Points to Mention

  • Coarse-grained locking with a single mutex: simple but serializes all operations.
  • Fine-grained locking (e.g., per-bucket or per-entry): higher concurrency but complex and risk of deadlocks.
  • Read-write locks: allow concurrent reads but writes are exclusive; good for read-heavy workloads.
  • Lock-free or wait-free approaches using atomic operations (e.g., CAS) and concurrent data structures like ConcurrentHashMap.
  • Trade-offs: throughput vs. latency, complexity vs. maintainability, and correctness under contention.
  • Consider using existing thread-safe libraries (e.g., Java's ConcurrentHashMap) or adapting known concurrent LRU designs.

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