Started with a hash map keyed by content, which works but I didn't immediately think about the cost of hashing large files.
Clarify the input format (paths with content or metadata) and define duplicate criteria (exact content match vs. metadata). Then propose a multi-stage approach: group by size, then hash content, and finally compare full content only for hash collisions. Discuss trade-offs between time, space, and accuracy, and consider edge cases like empty files and large files.
Pro tip: Mention that you would use a cryptographic hash (e.g., SHA-256) for content comparison to minimize collision risk, but also note that for very large files, a streaming hash avoids loading entire files into memory. This shows awareness of real-world constraints.
Ask whether duplicates are defined by exact content or metadata, and whether files are on disk or in memory. Confirm if the solution should handle large files and if approximate duplicates are acceptable.
If using content, first group files by size; files with different sizes cannot be duplicates. If using metadata, group by relevant metadata fields (e.g., name, timestamp). This reduces the number of pairwise comparisons.
For each group from step 2, compute a hash (e.g., SHA-256) of the file content. Group files by hash value. This efficiently identifies potential duplicates with high probability.
If hash collisions are a concern, compare the actual content of files within each hash group to confirm duplicates. This step can be skipped if using a strong cryptographic hash and collision risk is acceptable.
Collect all groups with more than one file and return them as the result. Ensure the output format matches the expected structure (e.g., list of lists of paths).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the problem constraints and the definition of 'large files' (e.g., size, memory limits, I/O speed). Then propose a streaming or chunked approach that avoids loading the entire file into memory, and discuss time-space trade-offs such as using external sorting or compression. Finally, mention specific optimizations like buffered I/O, parallel processing, and algorithmic improvements to reduce complexity.
Pro tip: Emphasize that you would first measure and profile to identify bottlenecks before optimizing, and that you'd consider the end-to-end pipeline including disk I/O and network transfer, not just CPU time.
Ask about file size, available memory, time limits, and whether the file fits in memory. Understand the exact operation (e.g., search, sort, transform) and any constraints like single-pass vs multi-pass.
Propose processing the file in chunks or using a streaming API to keep memory usage constant (O(1) or O(chunk size)). For example, read line-by-line or use memory-mapped files if appropriate.
Discuss algorithmic improvements: use hash-based lookups instead of linear scans, external sorting for large data, or parallel processing with multiple threads/processes to utilize CPU cores.
Reduce memory footprint by using compression, on-disk data structures (e.g., B-trees), or probabilistic data structures (e.g., Bloom filters) when exact answers aren't needed.
Mention buffered I/O, asynchronous I/O, or direct I/O to reduce syscall overhead. Also consider data locality, disk seek times, and network transfer if the file is remote.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Blanked a bit on skew handling specifically.
Start by clarifying requirements and scale, then propose a high-level architecture that partitions data by a hash of the dedup key, uses consistent hashing for distribution, and incorporates replication for fault tolerance. Discuss trade-offs between consistency and availability, and how to handle skew via techniques like salting or dynamic rebalancing.
Pro tip: Emphasize that deduplication at scale often requires a probabilistic data structure like a Bloom filter to reduce expensive lookups, but be ready to discuss its false positive implications and how to mitigate them.
Ask questions to understand data volume, throughput, latency requirements, and consistency needs. This sets the stage for design decisions.
Propose partitioning by a hash of the dedup key (e.g., content hash) and use consistent hashing to distribute partitions across nodes. Discuss how to handle skew with techniques like salting or splitting hot partitions.
Describe replication strategies (e.g., leader-follower, quorum) for durability and availability. Discuss consistency models (strong vs. eventual) and how to achieve them with consensus protocols like Raft or Paxos.
Explain how to efficiently check for duplicates using indexes, Bloom filters, or caches. Discuss trade-offs between accuracy and performance, and how to handle false positives.
Cover monitoring, rebalancing, and failure recovery. Mention how to scale horizontally and handle node failures without data loss.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.