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The layered filtering approach felt natural to me: skip anything that doesn't share a size with at least one other file, then do a cheap partial hash on the first few KB, then full hash only the survivors.
Start by outlining a multi-pass algorithm that progressively filters candidates: first group by file size, then compute a fast partial hash (e.g., first and last few KB) to narrow further, and finally compute a full cryptographic hash only for remaining candidates. Then discuss how to handle collisions (e.g., byte-by-byte comparison as a final check), memory constraints (e.g., external sorting, streaming, or disk-based hash tables), and parallelization (e.g., map-reduce or worker pools).
Pro tip: Emphasize that the goal is to minimize I/O, so always filter with the cheapest operation first (size) and only read file contents when necessary; also mention that partial hashing should read from both the beginning and end of files to catch common differences quickly.
Traverse the file system and record each file's path and size. Group files by size; only groups with more than one file are candidates for identical content.
For each candidate group, compute a fast hash (e.g., xxHash) of a small portion of each file (e.g., first 4KB and last 4KB). Group by this partial hash to further reduce candidates.
For remaining candidates, compute a strong cryptographic hash (e.g., SHA-256) of the entire file. Group by full hash.
For each group with the same full hash, perform a byte-by-byte comparison to confirm identical content, handling any hash collisions.
Use external sorting or disk-based hash tables to manage memory. Parallelize by processing directories or files in parallel with worker pools, ensuring thread-safe data structures.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I pivoted to talking about storing a persistent index of path-to-hash mappings and only reprocessing files whose mtime or inode changed.
Start by clarifying the current deduplication system's architecture and the expected scale of incremental updates. Then propose a design that tracks file changes (e.g., via filesystem events or metadata) and updates the deduplication index incrementally, handling edge cases like file modifications and deletions. Emphasize trade-offs between consistency, performance, and complexity.
Pro tip: Mention that you would use a write-ahead log or change journal to make updates durable and recoverable, and discuss how to handle concurrent updates without locking the entire index.
Ask about the current system's scale, update frequency, consistency requirements, and whether deletions are needed. This ensures your solution fits the context.
Propose using filesystem notifications (e.g., inotify) or periodic metadata scans to detect added/modified files. Discuss trade-offs between real-time and batch updates.
Describe how to update the deduplication index: for new files, compute chunks and add to index; for modified files, identify changed chunks and update references; for deletions, remove references and garbage collect.
Explain how to maintain index consistency during updates, e.g., using versioning, locking granularity, or transactional updates. Discuss how to avoid race conditions.
Compare approaches (e.g., event-driven vs. polling) in terms of latency, resource usage, and complexity. Mention potential optimizations like batching and caching.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the current single-machine design's assumptions and constraints, then systematically identify components that need to change for distribution. Propose a concrete distributed architecture with specific technologies and trade-offs, and discuss how to handle failures and consistency.
Pro tip: Explicitly state your assumptions about scale, consistency, and latency requirements before diving into the design—this shows you understand that distributed systems are about trade-offs, not one-size-fits-all solutions. Also, mention that you'd start with a simple approach and evolve it as needed, avoiding premature complexity.
Ask about scale (data size, QPS), consistency needs, latency targets, and failure tolerance to frame the distributed design appropriately.
Analyze the current design to pinpoint bottlenecks: storage capacity, compute power, single point of failure, and network isolation.
Outline how to partition data (sharding), replicate for availability, and coordinate nodes (e.g., using a consensus protocol or a distributed file system).
Discuss trade-offs between consistency models (strong vs. eventual) and mechanisms for handling node failures, network partitions, and data recovery.
Cover monitoring, deployment, scaling, and how to handle upgrades or rebalancing without downtime.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by emphasizing safety and reversibility: never modify data in place without a verified backup or transaction log. Then describe a two-phase approach: first, validate duplicates via cryptographic hashes and metadata, and second, atomically replace or move files using hard links or content-addressed storage with rollback capability. Finally, discuss trade-offs like storage savings, performance impact, and failure recovery.
Pro tip: Mention that you would use a copy-on-write or log-structured approach to record every operation, so you can undo changes if something goes wrong—this shows you think about production safety, not just the happy path.
Confirm duplicates using strong hashes (e.g., SHA-256) and compare metadata like permissions and timestamps. Decide on the replacement strategy (hard links vs. CAS) based on filesystem support and access patterns.
Take a snapshot or backup of the affected files, and set up a transaction log to record each operation. Ensure you can roll back atomically if any step fails.
For hard links, use rename() to atomically replace the duplicate with a link to the canonical file. For CAS, move the file to a content-addressed path and update references via a symlink or database entry.
After replacement, verify that the new link or CAS entry points to the correct content and that no data was lost. Then remove the original duplicate only after successful validation.
Log the changes, monitor for errors, and document the process for future runs. Consider gradual rollout to catch issues early.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.