Spent the first few minutes just walking through file discovery with a recursive directory traversal, which felt solid.
Start by clarifying requirements (scale, consistency, latency, storage types) and then propose a modular design with components for scanning, change detection, transfer, and verification. Walk through the data flow, discuss trade-offs (e.g., full vs incremental, push vs pull), and address failure handling and scalability.
Pro tip: Emphasize idempotency and checkpointing to enable resumable transfers and exactly-once semantics, which are critical for large-scale backup systems. Also, mention how you would monitor and alert on backup health and integrity.
Ask questions to understand scale (number of files, total size, rate of change), consistency needs (point-in-time snapshots?), latency, storage types (local, cloud, tape), and security/compliance requirements.
Outline main components: a scanner that traverses the source tree, a change detector (using metadata like mtime, size, or content hashes), a transfer service that moves data, and a metadata store for tracking state. Consider using a message queue for scalability.
Explain how to efficiently detect changes: compare file metadata (mtime, size) and optionally content hashes. For large files, use chunking and only transfer changed chunks (e.g., rsync algorithm, content-defined chunking).
Describe mechanisms for fault tolerance: retries with exponential backoff, checkpointing to resume transfers, idempotent operations, and transactional updates to metadata. Ensure that partial failures don't corrupt the backup.
Discuss how to scale horizontally: parallel scanning and transfer, partitioning by directory or file, using distributed queues and workers. Optimize for network and storage throughput, and consider compression and deduplication.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.