Start by clarifying requirements and constraints, then walk through the design of each component (validation, encoding, storage, persistence, memory estimation) before diving into implementation details. Emphasize trade-offs and justify your choices, and finish by outlining a unit test strategy that covers edge cases and persistence.
Pro tip: Proactively discuss how you would handle collisions in the encoding scheme and how you would estimate memory usage based on expected load, showing that you think about scalability and reliability even in a local context.
Ask questions to understand expected scale, persistence needs, and any specific validation rules. Confirm that the solution should be self-contained and run locally.
Choose an encoding scheme (e.g., base62) and explain how to generate unique short codes, handle collisions, and ensure determinism if needed.
Describe the data structure (e.g., hash map) for storing mappings, and how to persist to a file (e.g., JSON, append-only log) and load on startup.
Calculate approximate memory per entry and total memory for expected number of URLs, considering overhead of data structures and strings.
List key test cases: valid/invalid input, encoding/decoding, collision handling, persistence (save/load), and edge cases like empty or very long URLs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements: what 'well-formed' means (syntax, scheme, host, etc.) and the context (e.g., user input, API). Then outline a layered validation approach: first basic syntactic checks, then semantic checks, and finally security considerations, discussing trade-offs between strictness and usability.
Pro tip: Mention that validation should be done on the server side even if client-side validation exists, and consider using a well-tested library like validator.js to avoid reinventing the wheel and to handle edge cases.
Ask questions to understand what 'well-formed' means in this context: should it allow only HTTP/HTTPS? Should it accept internationalized domain names? What about URLs with authentication or ports? This ensures you're solving the right problem.
Check the URL against a standard format, e.g., using a regex or a URL parser. Ensure it has a scheme, host, and optionally path, query, and fragment. Reject obviously malformed strings.
Verify that the scheme is allowed (e.g., http, https), the host is a valid domain or IP, and the port (if present) is in a valid range. Optionally, check that the domain is not blacklisted or malicious.
Think about SSRF, open redirects, and homograph attacks. Normalize the URL (e.g., punycode for IDNs) and consider length limits. Discuss whether to resolve DNS or make a HEAD request (trade-off: latency vs. safety).
Balance strictness vs. user experience. Mention using a library (e.g., validator.js) vs. custom regex. Highlight the importance of server-side validation and error handling.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went with MD5 truncated to 6 characters of base62, which the interviewer seemed fine with.
Start by clarifying requirements (scale, code length, character set, expected lifetime) and then present a layered strategy: base encoding with a collision-resistant hash, followed by collision resolution via a deterministic probing or counter-based approach. Emphasize trade-offs between simplicity, performance, and uniqueness guarantees, and mention how you'd validate and monitor collisions in production.
Pro tip: Mention that you'd use a bloom filter or a precomputed set to quickly check for collisions before hitting the database, and that you'd consider using a distributed counter or a unique ID generator like Snowflake to avoid collisions altogether.
Ask about scale (e.g., millions of URLs), desired code length, allowed characters, and whether codes need to be guessable or not. This determines the encoding and collision strategy.
Decide between hashing (e.g., MD5, SHA-256) truncated and base62-encoded, or a counter-based approach. Discuss trade-offs: hashing is stateless but collision-prone; counter is collision-free but requires coordination.
For hashing, use linear probing or re-hash with a salt until a free slot is found. For counter, use a distributed ID generator (e.g., Snowflake) and encode the ID. Mention checking against a database or in-memory set.
Discuss caching, sharding, and using a bloom filter to reduce database lookups. Consider pre-generating codes or using a key generation service (KGS) for high throughput.
Explain how you'd track collision rates, latency, and storage. Mention logging and alerting for unexpected collision spikes, and periodic re-evaluation of the strategy.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Hadn't thought about this concretely before and it showed.
Start by breaking down the memory footprint into components: the hash table overhead, key storage, value storage, and any additional metadata. Then, discuss strategies for scaling beyond a single process, such as sharding, consistent hashing, or using a distributed cache, while considering trade-offs like latency and complexity.
Pro tip: Mention that you would measure actual memory usage with tools like jmap or heap dumps before optimizing, and consider the impact of object headers and alignment in Java, which can significantly increase the footprint.
Calculate the memory for a single entry: key (string length + object overhead), value (object size), and the map entry object itself. Include hash table array overhead.
Multiply per-entry memory by the number of entries, then add the overhead of the hash table array (capacity * reference size). Consider load factor and resizing.
Use profiling tools (e.g., JVM heap dumps, memory profilers) to measure actual usage and compare with your estimate, adjusting for factors like object alignment and garbage collection overhead.
If the map outgrows a single process, consider sharding the map across multiple processes using consistent hashing, or use a distributed cache like Redis. Discuss trade-offs: increased latency, complexity, and consistency.
Evaluate if an in-memory map is still appropriate; maybe a disk-based store or a database with caching layer is better. Discuss hybrid approaches.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
The collision test via stub was the interesting part here.
Start by outlining the core components of a URL shortener (encoding, storage, redirection) and the unit tests for each, focusing on edge cases and error handling. Then, explain how to force a hash collision by either mocking the hash function or using a deterministic algorithm with known collisions, and describe how to test collision resolution.
Pro tip: Demonstrate awareness of trade-offs: for example, discuss how collision handling affects performance and consistency, and mention that in production you'd use a distributed counter or a unique ID generator to avoid collisions altogether.
Break down the URL shortener into units: URL validation, hash generation, storage (create/read), and redirection logic. This ensures comprehensive coverage.
For each component, list test cases including happy paths, edge cases (e.g., empty URL, very long URL, invalid format), and error conditions (e.g., duplicate short code, non-existent code).
Describe techniques: mocking the hash function to return a fixed value, using a hash algorithm with known collisions (e.g., MD5), or precomputing two inputs that collide. Emphasize that this tests the collision resolution logic.
Write a test that inserts two URLs that collide and verify that both are stored and retrievable correctly, either by appending a counter or using a different mechanism.
Mention that while forcing collisions in tests is useful, in production you'd avoid collisions with a unique ID generator, and discuss the impact on performance and scalability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.