← Anthropic Interview Insights
Classic hashmap plus doubly linked list setup.
Start by clarifying the requirements and constraints, then propose a hash map combined with a doubly linked list to achieve O(1) operations. Implement the class with careful pointer manipulation, and analyze the time and space complexity.
Pro tip: Mention that you would use a doubly linked list because it allows O(1) removal from the middle, and consider using dummy head and tail nodes to simplify edge cases. Also, discuss potential thread-safety if the cache might be accessed concurrently.
Ask about expected capacity, whether operations need to be thread-safe, and if the cache should support additional methods like clear or size. Confirm that get and put must be O(1).
Explain that a hash map provides O(1) access to nodes, and a doubly linked list maintains the order of usage. The hash map maps keys to nodes in the linked list.
For get: if key exists, move the node to the front (most recently used) and return its value. For put: if key exists, update value and move to front; if not, create a new node, add to front, and if capacity exceeded, remove the node at the tail (least recently used) and delete its key from the map.
Write clean code with a Node class (key, value, prev, next) and an LRUCache class with dummy head and tail for easier insertion/removal. Handle edge cases like capacity 0 or 1.
State that both get and put are O(1) time and O(capacity) space. Walk through a small example to verify correctness, and mention potential optimizations or variations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The base implementation is straightforward, just map content to a list of paths.
Start by clarifying the input format and constraints, then propose a hash-based grouping solution: compute a hash (e.g., SHA-256) for each file's content, group paths by hash, and optionally verify with byte-by-byte comparison for collision safety. For follow-ups, discuss streaming/chunked hashing for large files, collision-resistant hashing with verification, and distributed approaches like MapReduce or consistent hashing.
Pro tip: Mention that you would use a cryptographic hash like SHA-256 and still verify equality for groups with multiple files to handle collisions, showing you balance efficiency with correctness. Also, proactively discuss memory and I/O trade-offs, as Anthropic values practical engineering judgment.
Ask about input size, file size distribution, whether paths are unique, and if exact byte equality is required. Confirm if the solution should be in-memory or can use external storage.
Propose hashing file contents to group by hash, then verify equality within groups to avoid collisions. Discuss time and space complexity: O(N * L) for hashing, where N is number of files and L is average file size.
For very large files, suggest streaming the file in chunks to compute the hash incrementally, avoiding loading the entire file into memory. Mention using a rolling hash or cryptographic hash with chunking.
Explain that while SHA-256 collisions are astronomically unlikely, you can verify by comparing file contents byte-by-byte for files that hash to the same value. Alternatively, use two different hashes or a Merkle tree for large files.
Describe a MapReduce approach: map each file to (hash, path), shuffle by hash, then reduce to group paths. For very large scale, use consistent hashing to partition files across nodes and handle skew.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.