I started with a trie-like structure for the directory tree and a hash map for file contents.
Start by clarifying requirements (file size limits, concurrency, consistency, scale) and then design a hierarchical namespace using a tree-like structure. Propose a data model (e.g., inodes or a directory table) and discuss trade-offs between simplicity and scalability, covering operations like add, read, delete, and list. Finally, address non-functional aspects like durability, availability, and performance optimizations.
Pro tip: Demonstrate awareness of real-world constraints by discussing how to handle large files (e.g., chunking) and concurrent access (e.g., locking or versioning), and mention how HarveyAI's domain (legal AI) might require strong audit trails and access controls.
Ask questions to understand scale, file sizes, concurrency, consistency, and security needs. This ensures the design meets actual expectations.
Outline the core components: a metadata store for directory hierarchy and a blob store for file contents. Sketch the API and data flow for each operation.
Detail how to represent directories and files (e.g., inodes, adjacency lists) and implement add, read, delete, and list efficiently. Discuss indexing for fast lookups.
Address partitioning, replication, and consistency models. Explain how to handle failures, backups, and concurrent modifications.
Compare design choices (e.g., SQL vs NoSQL, monolithic vs distributed) and suggest optimizations like caching, chunking, and lazy deletion.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the constraints: file sizes, number of files, network bandwidth, and whether the vaults are static or changing. Then propose a hierarchical comparison strategy: first compare metadata (file lists, sizes, timestamps), then use cryptographic hashes (e.g., Merkle trees) to identify differing files, and finally transfer only the differing blocks or files. Emphasize minimizing data transfer by leveraging incremental hashing and possibly probabilistic data structures like Bloom filters for initial screening.
Pro tip: Mention that you would use a Merkle tree to efficiently compare large numbers of files, and that you can further optimize by comparing hashes of file chunks to pinpoint differences within a file, transferring only the mismatched chunks. Also, discuss trade-offs between hash collision probability and hash size, and consider using a secure hash like SHA-256.
Ask about the number and size of files, network bandwidth, whether the vaults are static or dynamic, and the acceptable probability of false positives/negatives. This determines the appropriate strategy.
Exchange file lists with sizes and modification timestamps to quickly identify files that are likely different. This is a low-cost initial filter.
Build a Merkle tree over the files (or file chunks) on each replica. Compare the root hashes; if they differ, recursively compare child hashes to efficiently locate differing files or chunks.
Once differing files or chunks are identified, transfer only those from one replica to the other (or to a third party) to synchronize. Optionally, use delta encoding to send only the changed bytes.
Consider trade-offs: Merkle tree depth vs. number of round trips, hash size vs. collision risk, and whether to use Bloom filters for an initial probabilistic check. Also mention handling of dynamic changes during comparison.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.