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Start by clarifying requirements: expected scale, URL length limits, collision handling, and whether custom aliases are needed. Then design a class that uses a hash map for bidirectional mapping and a base62 encoding scheme to generate short codes, ensuring O(1) average time for both operations. Discuss trade-offs like using a counter vs. random generation, and how to handle collisions and persistence.
Pro tip: Mention that in a real system, you'd use a distributed ID generator (like Snowflake) and a database with a unique index to avoid collisions, and consider caching hot URLs for low latency.
Ask about scale (QPS, number of URLs), short URL length, allowed characters, custom aliases, and persistence needs. This shows you think about real-world constraints.
Propose using two hash maps: one from long URL to short code, and one from short code to long URL. This ensures O(1) lookup for both shorten and expand.
Use a base62 encoding of a unique integer ID (e.g., from a counter or distributed ID generator). Alternatively, use a hash (e.g., MD5) and take first few characters, handling collisions.
If using hashing, check for collisions and resolve by appending a counter or rehashing. Also handle invalid short URLs, duplicate long URLs, and custom aliases.
Mention that in production, you'd use a database (e.g., MySQL with unique index) and a distributed cache (e.g., Redis). Discuss sharding and replication for scale.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Covered counter plus base62, hash-based with collision handling, and pre-generated ID pools.
Start by clarifying the requirements for the short code, such as expected scale, uniqueness, and length. Then systematically compare common generation approaches like hash-based, random, and sequential encoding, highlighting trade-offs in collision rate, predictability, and scalability. Conclude with a recommendation tailored to the scenario.
Pro tip: Mention that the choice often depends on whether the system needs to be distributed and how much you care about short code length versus collision handling. Also, note that pre-generating codes can help with performance but adds complexity.
Ask about expected traffic, uniqueness guarantees, code length constraints, and whether codes should be unpredictable. This ensures your answer is relevant to the actual problem.
Enumerate common methods: hash-based (e.g., MD5 truncated), random string generation, base62 encoding of auto-increment IDs, and pre-generated key pools.
For each approach, discuss pros and cons: collision probability, predictability, scalability, storage overhead, and complexity. Compare them in a table if helpful.
Address how to handle uniqueness in a distributed system, such as using a centralized counter, Snowflake IDs, or consistent hashing with collision resolution.
Based on the requirements, recommend one or a hybrid approach, explaining why it best balances the trade-offs for the given context.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I talked through a simple key-value schema and then moved to read replicas for scaling reads.
Start by clarifying requirements (read/write ratio, latency, scale, custom aliases, expiration). Then propose a schema with a unique short key as the primary key, and discuss scaling reads via caching and replicas, and writes via sharding and asynchronous processing.
Pro tip: Mention that you would use a distributed ID generator (like Snowflake) or a key generation service to avoid collisions and hot partitions, and that you would store the mapping in a key-value store like Cassandra or DynamoDB for horizontal scalability.
Ask about expected read/write ratio, latency requirements, scale (QPS, storage), and features like custom aliases or expiration.
Propose a table with short key as primary key, long URL, creation time, expiration, and user ID. Consider using a key-value store for simplicity and scalability.
Use caching (e.g., Redis) for hot URLs, read replicas, and CDN for redirects. Discuss cache eviction policies and consistency.
Shard by short key using consistent hashing, use asynchronous writes, and pre-generate keys to avoid contention. Consider write-ahead logging and batch writes.
Discuss trade-offs between SQL vs NoSQL, consistency vs availability, and how to handle key generation (e.g., base62 encoding of auto-increment IDs vs random strings).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying that both policies are valid and the choice depends on product requirements, such as analytics, idempotency, and storage. Then present a balanced trade-off analysis and describe how to enforce your chosen policy using a unique index on the long URL or a deterministic hash.
Pro tip: Mention that idempotent shortening simplifies client retries and prevents duplicate links, but if you need per-user or per-campaign tracking, you can still generate unique short URLs while storing a canonical mapping for analytics. This shows you understand both technical and business implications.
Ask about the product goals: Is deduplication needed? Are analytics per click or per link? What are the storage and latency constraints?
Discuss pros and cons of deterministic (same long URL -> same short URL) vs. non-deterministic (new short URL each time) approaches, covering storage, idempotency, analytics, and collision handling.
State your recommendation based on the requirements, e.g., deterministic for simplicity and deduplication, or non-deterministic for granular tracking.
Describe how to enforce the policy: for deterministic, use a unique index on a hash of the long URL or a mapping table; for non-deterministic, generate a random short code and ensure uniqueness via a unique index.
Mention handling of hash collisions, race conditions, and scalability (e.g., using distributed locks or atomic operations).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was basically a lightning round of follow-ups crammed into one question.
Start by clarifying requirements and scale (e.g., read-heavy, latency targets, retention policies). Then design each component—caching, click analytics, expiration, and abuse prevention—with clear trade-offs and data flow. Finally, tie them together with a focus on reliability, scalability, and safety.
Pro tip: Emphasize that click analytics should be decoupled from the redirect path using asynchronous event streaming (e.g., Kafka) to avoid adding latency, and mention that abuse detection must balance false positives with user experience.
Ask about expected QPS, read/write ratio, latency SLAs, data retention, and abuse sensitivity. This shapes caching strategy, analytics pipeline, and expiration policies.
Use a multi-layer cache: CDN for hot redirects, in-memory cache (e.g., Redis) for mapping short-to-long URLs, and possibly a local cache. Discuss cache eviction (LRU), TTL, and consistency with database updates.
Decouple analytics from the redirect path: log click events asynchronously to a message queue (e.g., Kafka), then process them in a stream/batch pipeline to update counts in a scalable store (e.g., Cassandra, Redis). Consider approximate counting for high volume.
Store expiration timestamp with each URL. Use lazy deletion (check on read) and background cleanup (e.g., cron job) to remove expired links. For caching, ensure TTL aligns with expiration to avoid serving stale redirects.
Implement rate limiting per user/IP, URL scanning against blacklists (e.g., Google Safe Browsing), and anomaly detection for spam. Provide reporting and takedown mechanisms, and consider manual review for flagged links.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.