Pretty standard recursive traversal problem.
Start by clarifying requirements and constraints, then outline a recursive or iterative traversal using a stack/queue, handling symbolic links and permissions. Discuss trade-offs between depth-first and breadth-first, synchronous vs asynchronous I/O, and how to process files (e.g., hashing, metadata) efficiently. Finally, mention scalability considerations like parallel processing and error handling.
Pro tip: Demonstrate awareness of real-world file system quirks: hard links, symbolic loops, and permission errors. Also, discuss how to avoid blocking I/O and leverage concurrency for performance, which is crucial for a company like Dropbox that deals with massive scale.
Ask about the expected scale (number of files, depth), whether to follow symlinks, handle permissions, and what 'useful' means (size, hash, metadata). Confirm if the crawler should be synchronous or asynchronous, and if it needs to be resilient to errors.
Decide between recursive DFS (simple, but risk of stack overflow) and iterative BFS/DFS using an explicit stack/queue. Consider memory usage and order of processing. Mention that BFS can be better for parallel processing.
Address symbolic links (avoid cycles), hard links (avoid double-processing), permission errors (skip or log), and special files (devices, sockets). Use appropriate system calls (e.g., os.scandir for efficiency).
For each file, perform the required operation (e.g., compute size, hash, extract metadata). Discuss trade-offs: hashing large files in chunks, using async I/O or thread pools for concurrency, and batching metadata reads.
Talk about parallelizing traversal and processing (e.g., with worker threads/processes), handling backpressure, and logging errors without failing the entire crawl. Mention potential bottlenecks like disk I/O and how to mitigate them.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where I started sweating a little.
Start by clarifying requirements and scale, then present a high-level architecture with a database for state, an API for requests, and a worker pool. Dive into the core challenges: exactly-once processing via idempotency and deduplication, cycle prevention with visited sets, retries with backoff and dead-letter queues, and concurrency control with rate limiting and worker quotas.
Pro tip: Emphasize that exactly-once processing is achieved through idempotent operations and deduplication, not by trying to guarantee exactly-once delivery. Also, discuss how to handle failures gracefully with retries and dead-letter queues to avoid infinite loops.
Ask about expected crawl volume, latency requirements, and consistency needs. This informs database choices and concurrency limits.
Choose a database (e.g., relational for strong consistency or NoSQL for scale) to track crawl jobs, directories, and visited URLs. Include tables for jobs, workers, and deduplication.
Design a REST API to accept crawl requests and enqueue jobs. Workers pull jobs, process directories, and enqueue subdirectories as new jobs.
Explain exactly-once processing via idempotency and deduplication, cycle prevention with visited sets, retries with exponential backoff and dead-letter queues, and concurrency limits with rate limiting and worker quotas.
Highlight trade-offs between consistency and availability, and describe how to handle worker failures, database outages, and poison messages.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.