The delimiter thing is what trips people up.
Start by clarifying requirements and constraints, then design a simple hash map for core operations. For serialization, use length-prefixed encoding to handle arbitrary byte strings without delimiters. Discuss trade-offs and potential optimizations.
Pro tip: Mention that length-prefixed encoding is a standard technique used in protocols like Protocol Buffers and Thrift, showing awareness of real-world systems. Also, consider versioning the serialization format for future compatibility.
Ask about expected data size, concurrency needs, persistence requirements, and performance constraints to tailor the design.
Propose using a hash map (e.g., dictionary in Python) for O(1) average-case put, get, and delete operations.
Use length-prefixed encoding: for each key-value pair, write the length of the key, then the key bytes, then the length of the value, then the value bytes. This handles arbitrary bytes without delimiters.
Serialize by iterating over the map and writing length-prefixed pairs. Deserialize by reading lengths and bytes sequentially to reconstruct the map.
Mention trade-offs: memory usage vs. speed, potential for compression, concurrency control, and persistence options. Also, consider error handling for corrupted data.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the constraints (data size, access patterns, recovery requirements) and then propose a segmented file format with a manifest that tracks segment order and metadata. Explain how to write segments independently, handle atomicity via a commit marker, and restore by reading the manifest and reassembling segments in order.
Pro tip: Mention that you would design the format to be forward-compatible by including a version number and checksums per segment, so future changes don't break existing readers and corruption can be detected early.
Ask about expected data size, read/write patterns, recovery time objectives, and whether partial reads are needed. This determines segment size and indexing strategy.
Propose splitting data into fixed-size or logical chunks (e.g., by key range or time), each stored as a separate file with a header containing metadata like segment ID, checksum, and version.
Introduce a manifest file that lists all segments, their order, and offsets. Use an atomic commit marker (e.g., write manifest last) to ensure consistency and enable recovery from partial writes.
Describe how to read the manifest, validate each segment's checksum, and reassemble the data in the correct order. Handle missing or corrupt segments gracefully.
Compare segment sizes (smaller = more files, larger = less parallelism), consider compression, and mention lazy loading or streaming for large restores.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is basically LSM-tree territory and I said so, which helped frame the conversation.
Start by clarifying the current storage architecture and constraints, then propose an append-only log with periodic snapshots. Explain the log format, replay process, and compaction triggers, emphasizing trade-offs between write amplification, recovery time, and storage overhead.
Pro tip: Mention that compaction should be triggered based on log size relative to snapshot size and recovery time SLAs, and that you'd use a background thread with atomic snapshot swaps to avoid blocking reads/writes.
Ask about read/write patterns, durability guarantees, acceptable recovery time, and storage budget to tailor the design.
Define log entry format (e.g., operation type, key, value, timestamp), write path (append to file, fsync policy), and log rotation.
On startup, load the latest snapshot, then replay log entries in order to reconstruct the current state, handling partial writes and corruption.
Trigger compaction when log size exceeds a threshold (e.g., 2x snapshot size) or recovery time exceeds SLA; compact by writing a new snapshot and truncating the log.
Ensure compaction doesn't block reads/writes (e.g., copy-on-write, background thread), and handle crashes during compaction with atomic file swaps.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.