The collision handling part is where I spent way too long.
Start by clarifying requirements and constraints, then outline the core components: URL validation, hash-based code generation with collision handling, in-memory storage with deduplication, and JSON file persistence. Walk through the design and implementation details, emphasizing trade-offs and edge cases.
Pro tip: Discuss how you would handle collisions and deduplication efficiently, and mention the trade-offs between different hashing strategies and persistence approaches. Show awareness of scalability and potential concurrency issues.
Ask questions to understand expected scale, persistence needs, and any specific validation rules. Confirm whether the library should be thread-safe and how collisions should be resolved.
Outline the main modules: URL validator, hash generator, storage layer (in-memory map), and persistence handler. Decide on a hashing algorithm (e.g., MD5, SHA-256, or custom base62 encoding) and collision resolution strategy (e.g., linear probing, appending random characters).
Explain how to check for existing short codes for the same URL to avoid duplicates. For collisions, describe how to generate a new code (e.g., by rehashing with a salt or incrementing a counter) and update storage accordingly.
Describe how to serialize the in-memory store to a JSON file and load it on startup. Discuss when to persist (e.g., on every write or periodically) and how to handle file I/O errors.
Highlight trade-offs between different hashing algorithms (speed vs. collision resistance), persistence strategies (write-through vs. write-behind), and memory usage. Mention edge cases like invalid URLs, empty inputs, and concurrent access.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
They asked this as a follow-up discussion.
Start by analyzing the memory characteristics of the current URL storage system, including per-URL overhead and data structure choices. Then, discuss how memory scales with the number of URLs and identify bottlenecks. Finally, propose a sharding or distributed architecture that maintains performance and scalability.
Pro tip: Quantify memory usage with concrete numbers (e.g., bytes per URL) and mention real-world constraints like network latency and consistency trade-offs. This shows you think beyond just adding machines.
Estimate memory per URL by considering the data structure (e.g., hash table, trie) and metadata. Calculate total memory for expected URL counts and identify growth patterns.
Determine when a single machine's memory becomes a bottleneck, considering factors like RAM limits, garbage collection overhead, and operational costs.
Suggest sharding the URL data across multiple machines using consistent hashing or range-based partitioning. Discuss trade-offs between different sharding strategies.
Explain how to handle data replication, consistency models (e.g., eventual consistency), and failure recovery in a distributed setup.
Mention additional optimizations like compression, caching, or using disk-based storage for cold data to further reduce memory pressure.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.