JP Morgan·Software Engineer·Onsite - System Design / Architecture·Senior

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Jun 2026

Summary

System design round at JP Morgan for a software engineer role. The whole session was basically one big question about URL shortening, and the interviewer kept it high-level rather than drilling into code or schema specifics, which I wasn't expecting.

Questions Asked (1)

Design a URL shortening service like bit.ly or TinyURL, covering short-code generation, storage, caching, and click tracking.

System DesignTechnical Trade-offsData Modeling

Author's notes

I started with short-code generation and spent probably too long debating hashing versus a counter with base62 encoding versus a distributed ID approach.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (read/write ratio, scale, latency, analytics needs) and then walk through the high-level design: API, short-code generation, storage, caching, and click tracking. Emphasize trade-offs, especially around code generation (hash vs. counter), database choice (SQL vs. NoSQL), and caching strategy, while keeping the design scalable and fault-tolerant.

Pro tip: At JP Morgan, reliability and data integrity are paramount, so highlight how you would handle failures (e.g., database replication, cache fallback) and ensure click tracking is accurate and not lost, perhaps using a message queue for asynchronous processing.

1. Clarify Requirements and Scale

Ask about expected traffic (e.g., 100M URLs, 10K writes/sec, 100K reads/sec), read/write ratio, latency requirements, and whether analytics are needed. This sets the stage for design decisions.

2. High-Level Design and API

Define the API endpoints (e.g., POST /shorten, GET /{code}) and sketch the components: load balancer, web servers, database, cache, and analytics pipeline. Explain the flow from URL submission to redirection.

3. Short-Code Generation Strategy

Compare approaches: hash-based (e.g., MD5 then base62) vs. counter-based (e.g., distributed counter with base62). Discuss trade-offs: collision handling, predictability, and scalability. Recommend a counter-based approach with a distributed ID generator (e.g., Snowflake) for uniqueness and scalability.

4. Storage and Caching

Choose a database (e.g., NoSQL like Cassandra for scalability or SQL for ACID) and design the schema (short_code -> long_url, metadata). Implement caching (e.g., Redis) with LRU eviction to handle read-heavy traffic, and discuss cache invalidation and TTL.

5. Click Tracking and Analytics

Design a scalable click tracking system: log clicks asynchronously (e.g., to Kafka) and process them for real-time or batch analytics. Discuss how to avoid impacting redirection latency and ensure data accuracy.

Key Points to Mention

Trade-offs between hash-based and counter-based short-code generation, including collision handling and predictability.
Database choice: SQL vs. NoSQL, considering scalability, consistency, and query patterns.
Caching strategy: Redis with LRU, cache-aside pattern, and handling cache misses.
Click tracking: asynchronous logging via message queue (e.g., Kafka) to decouple from redirection and ensure scalability.
Handling high read traffic: read replicas, CDN for static assets, and horizontal scaling of web servers.
Fault tolerance and reliability: database replication, cache fallback, and idempotent operations.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.