Started fine with a plain hashmap storing value plus expiration timestamp.
Start by clarifying requirements (e.g., concurrency, eviction policy, TTL precision) and then propose a design using a hash map for O(1) access and a min-heap or timing wheel for efficient expiration. Discuss trade-offs between different cleanup strategies (lazy vs. active) and how to handle concurrency and memory management.
Pro tip: Mention that Netflix often deals with high-throughput, low-latency systems, so emphasize scalability and avoiding global locks—consider sharding the cache or using read-write locks per bucket. Also, discuss how you would monitor and tune the cleanup process in production.
Ask about expected throughput, latency, concurrency needs, TTL granularity, and eviction policies. Confirm whether the cache should be thread-safe and if memory usage is a concern.
Propose a hash map for key-value storage and a priority queue (min-heap) or timing wheel for expiration ordering. Explain how to achieve O(1) average get/put and O(log n) or O(1) expiration handling.
Describe lazy expiration on get (check TTL and remove if expired) and an active background thread that periodically scans and removes expired entries. Discuss how to avoid blocking operations and handle concurrency.
Explain how to make the cache thread-safe using fine-grained locking (e.g., per-bucket locks) or lock-free structures. Consider sharding to reduce contention and improve scalability.
Compare lazy vs. active cleanup, min-heap vs. timing wheel, and memory overhead. Mention potential improvements like using a doubly-linked list for LRU eviction or adaptive TTL.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and defining the command interface, then implement a stack-based undo manager with clear no-op behavior. Extend to redo by adding a second stack, ensuring that new commands clear the redo stack.
Pro tip: Mention that undo/redo should be idempotent and thread-safe if needed, and discuss how to handle memory by limiting history size—this shows production-level thinking.
Ask about expected behavior when there's nothing to undo (e.g., no-op or throw exception) and whether redo should be supported. Define a Command interface with execute() and undo() methods.
Use a stack to store executed commands. On undo, pop the most recent command and call its undo() method. If the stack is empty, handle gracefully (e.g., return false or throw a specific exception).
Add a redo stack. When undoing, push the undone command onto the redo stack. When redoing, pop from the redo stack, execute the command, and push it back onto the undo stack.
When a new command is executed, clear the redo stack to maintain correct history. Also consider thread safety, memory limits, and whether commands can be merged.
Mention unit tests for edge cases (empty stacks, multiple undos/redos) and analyze time complexity (O(1) for undo/redo) and space complexity (O(n) for history).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Clarify the tree structure and traversal order (pre-order), then implement a recursive DFS that appends node ids as visited. Discuss iterative alternative using a stack to handle deep trees and avoid recursion limits.
Pro tip: Mention that DFS order can be pre-order, in-order, or post-order; confirm which one is expected. Also note that for very deep trees, an iterative approach prevents stack overflow, showing production awareness.
Confirm the traversal order (pre-order) and input/output format. Ask if the tree can be deep or if recursion is acceptable.
Decide between recursive and iterative. Recursive is simpler; iterative with explicit stack is safer for deep trees.
For recursive: visit node, then recurse on children. For iterative: use a stack, push children in reverse order to process left-to-right.
Test with edge cases (empty tree, single node, skewed tree). Analyze time O(n) and space O(h) for recursion or O(n) for iterative.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.