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Palo Alto Networks·Software Engineer·Onsite - Coding / Algorithms·Intermediate

Intermediate
May 2026

Summary

Palo Alto Networks coding round for a Software Engineer role. One meaty design-and-implement question that took up the whole session, walking through data structures, a whiteboard sketch, a dry run, and complexity analysis.

Questions Asked (1)

Q1

Design and implement an in-memory LRU cache with a fixed capacity. It should support get(key) and put(key, value), both in average O(1) time. Walk through your data structures, sketch it out, do a dry run on a specific sequence of operations, and explain how you'd handle edge cases like capacity zero, duplicate keys, and hot keys.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs
Author's notes

I knew it was an LRU cache the second they finished the sentence, which was both reassuring and a trap because I rushed into code before properly explaining the structure.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and constraints, then propose a hash map combined with a doubly linked list to achieve O(1) operations. Walk through the design, dry run a sequence, and discuss edge cases and trade-offs.

Pro tip: Mention that you'd use a sentinel head and tail to simplify edge cases in the linked list, and discuss thread-safety considerations if the cache might be accessed concurrently.

1. Clarify requirements and constraints

Ask about expected capacity, concurrency, and whether keys/values are generic. Confirm that get and put must be O(1) average time.

2. Propose data structures

Explain that a hash map provides O(1) access to nodes, and a doubly linked list maintains recency order. The map stores key -> node, and the list stores nodes with key-value pairs.

3. Detail operations

For get: if key exists, move node to front (most recently used) and return value; else return -1. For put: if key exists, update value and move to front; else create new node, add to front, and if capacity exceeded, remove least recently used (tail) and delete from map.

4. Dry run a sequence

Choose a small capacity (e.g., 2) and walk through operations like put(1,1), put(2,2), get(1), put(3,3), get(2), etc., showing how the list and map change.

5. Discuss edge cases and trade-offs

Address capacity zero (either throw error or no-op), duplicate keys (update and refresh), hot keys (frequently accessed stay in cache), and potential concurrency issues (e.g., use locks or concurrent data structures).

Key Points to Mention

  • Hash map for O(1) lookup and doubly linked list for O(1) insertion/deletion and recency tracking.
  • Use of sentinel nodes (dummy head and tail) to avoid null checks and simplify edge cases.
  • Time complexity: O(1) average for both get and put; space complexity: O(capacity).
  • Handling capacity zero: either throw IllegalArgumentException or make put a no-op and get always return -1.
  • Duplicate keys: update value and move node to front to mark as recently used.
  • Hot keys: frequently accessed keys remain at the front, ensuring they are not evicted.
  • Thread-safety: if needed, use synchronized methods or ConcurrentHashMap with additional synchronization.

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