← Anthropic Interview Insights
The problem itself is pretty classic but not having any scaffolding meant I spent a chunk of time just rebuilding basic filesystem traversal stuff.
Start by clarifying requirements and constraints, then propose a two-phase algorithm: first traverse the filesystem to collect file paths and sizes, then group files by size and compare content hashes only for files with matching sizes. Discuss trade-offs between hashing entire files versus using partial hashes, and consider edge cases like symlinks, permissions, and large files.
Pro tip: Mention that you would use a streaming hash (e.g., SHA-256) and compare files byte-by-byte only when hashes match, to avoid false positives and minimize I/O. Also, discuss how to handle hard links and avoid infinite loops with symlinks.
Ask about the filesystem size, file types, performance requirements, and whether symlinks/hard links should be considered. Confirm if the solution should be recursive and if it needs to handle permission errors.
Choose between recursive DFS or iterative BFS to walk the directory tree. Discuss using a stack/queue and handling symbolic links to avoid cycles.
Collect all file paths and their sizes, then group files by size. Only files with identical sizes are candidates for duplicates, reducing the number of content comparisons.
For each group of same-sized files, compute a cryptographic hash (e.g., SHA-256) of each file's content. Group files by hash; any group with more than one file contains duplicates. Optionally, verify byte-by-byte to handle hash collisions.
Address symlinks, hard links, permission errors, and large files. Discuss using partial hashing (e.g., first and last few KB) for quick elimination, and consider memory usage for large numbers of files.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Acknowledge that duplicate detection is inherently probabilistic and context-dependent, then systematically walk through failure modes across data ingestion, hashing, comparison, and output stages. Emphasize that understanding these limitations is crucial for building robust systems and for setting correct user expectations.
Pro tip: Frame limitations as design trade-offs rather than flaws—this shows you understand that every solution involves balancing accuracy, performance, and complexity. Mention that you would instrument the system to detect and log these failure cases for continuous improvement.
List the core assumptions your solution makes, such as files being immutable, hash functions being collision-resistant, or metadata being reliable. Explain how violating these assumptions leads to incorrect results.
Discuss specific edge cases like empty files, very large files, files with identical content but different metadata, or files that change during scanning. Describe how each could cause false positives or negatives.
Explain how scaling to massive datasets or high-throughput environments can introduce errors, such as hash collisions becoming more likely or sampling techniques missing duplicates.
Mention external factors like file system inconsistencies, network partitions, or concurrent modifications that can lead to incomplete or stale comparisons.
Suggest ways to detect and mitigate these breakdowns, such as using multiple hash algorithms, verifying with byte-by-byte comparison, or implementing logging and alerting for anomalies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked through sharding the file index and doing parallel hash comparisons across nodes.
Start by clarifying the current solution's bottlenecks and constraints, then propose a scaling strategy that addresses those bottlenecks using distributed systems principles. Discuss trade-offs between consistency, availability, and partition tolerance, and how you would measure success. Emphasize iterative improvement and monitoring.
Pro tip: Show awareness of Anthropic's focus on safety and reliability by mentioning how you'd handle failures gracefully and ensure data consistency in a distributed setting. Also, quantify improvements with metrics like latency, throughput, and cost.
Analyze the current solution to find performance bottlenecks, such as CPU, memory, I/O, or network limits. Use profiling and monitoring data to pinpoint the most critical constraints.
Suggest horizontal scaling (e.g., adding more machines) or vertical scaling (e.g., upgrading hardware) based on the bottleneck. Consider partitioning data, sharding, or using load balancers to distribute traffic.
Discuss how to handle consistency, availability, and partition tolerance (CAP theorem). Mention techniques like replication, consensus algorithms (e.g., Raft), and eventual consistency where appropriate.
Propose algorithmic improvements, caching, batching, or asynchronous processing to reduce latency and increase throughput. Consider using message queues or stream processing for decoupling.
Define metrics (e.g., p99 latency, QPS, error rates) to evaluate the scaled solution. Plan for monitoring, alerting, and continuous improvement based on real-world feedback.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.