← JP Morgan Interview Insights
This was the anchor question for the whole interview.
Start by clarifying functional and non-functional requirements, then propose a high-level design with a load balancer, application servers, and a distributed key-value store. Dive into the core algorithm for generating short codes, discuss trade-offs (e.g., hash vs. counter-based), and address scalability, reliability, and data consistency.
Pro tip: Emphasize how you would handle read-heavy traffic and ensure low latency, as this is critical for a URL shortener at scale. Also, mention monitoring and analytics to show business awareness, which is valued in fintech.
Ask about expected traffic (e.g., 100M URLs/day), read/write ratio, latency requirements, and custom short codes. Confirm if analytics or expiration are needed.
Sketch components: client, load balancer, application servers, database, cache, and analytics. Explain the flow: client sends long URL, server generates short code, stores mapping, returns short URL.
Discuss algorithms: base62 encoding of a globally unique counter (e.g., using Snowflake or ZooKeeper) or hash-based (MD5 + collision handling). Compare trade-offs: counter is predictable but simple; hash is unpredictable but needs collision resolution.
Choose a distributed NoSQL database (e.g., Cassandra, DynamoDB) for scalability and high availability. Use a cache (Redis) for hot URLs to reduce latency. Discuss sharding by short code or hash.
Address horizontal scaling of app servers, database replication, and caching. Discuss handling failures, rate limiting, and analytics (e.g., click counts) via async processing.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Pretty standard opener but the non-functional part is where they actually cared.
Start by clarifying the scope and scale (e.g., read/write ratio, expected QPS, latency SLAs) to show you think before designing. Then systematically list functional requirements (core features) and non-functional requirements (scalability, availability, latency, consistency, security), and briefly discuss trade-offs for each. Conclude by prioritizing requirements based on business needs and technical constraints.
Pro tip: In a financial institution like JP Morgan, emphasize non-functional requirements such as security, auditability, and compliance (e.g., GDPR, SOX) alongside scalability, as these are often as critical as functional features.
Ask questions to understand expected scale (e.g., 100M URLs, 10K QPS), read/write ratio, latency requirements, and any compliance needs. State your assumptions clearly.
List core features: URL shortening, redirection, custom aliases, expiration, analytics, and user authentication/authorization if needed. Prioritize must-haves vs. nice-to-haves.
Cover scalability (horizontal scaling, partitioning), availability (99.99% uptime), latency (p99 < 100ms), consistency (eventual vs. strong), durability, security (rate limiting, encryption), and compliance.
Explain how requirements interact (e.g., strong consistency vs. low latency) and propose a prioritized list based on business impact and technical feasibility.
Recap the key requirements and ask if the interviewer wants to dive deeper into any area, showing collaborative problem-solving.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the service's domain, consumers, and key use cases, then propose a resource-oriented RESTful API with clear versioning and consistent error handling. Emphasize security, scalability, and alignment with JP Morgan's regulatory and enterprise standards.
Pro tip: Show awareness of financial industry constraints like audit trails, idempotency, and data sensitivity; mention how you'd handle PII and comply with regulations such as GDPR or SOX.
Ask about the service's purpose, target consumers (internal/external), expected load, and any regulatory or security requirements. This ensures the API design meets actual needs.
Identify core entities and map them to RESTful resources with standard HTTP methods (GET, POST, PUT, DELETE). Consider using nouns for resources and keeping operations idempotent where necessary.
Establish conventions for naming, versioning (e.g., URI versioning), pagination, filtering, and error responses. Plan for backward compatibility and deprecation strategies.
Incorporate authentication (OAuth 2.0, JWT), authorization (RBAC), rate limiting, and encryption. Ensure audit logging and data privacy controls are in place.
Discuss caching, asynchronous processing, and load balancing. Mention how the API will handle high throughput and potential spikes in traffic.
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 'short' means (e.g., 6-8 characters), expected scale, and whether keys must be unique or just low-collision. Then discuss generation methods like base62 encoding of random numbers or hashing, and collision handling strategies such as checking a database, using a bloom filter, or appending a counter.
Pro tip: Mention that in a distributed system, you can avoid collisions entirely by using a unique node ID or timestamp in the key generation, but always have a fallback collision resolution mechanism. Also, consider the trade-off between key length and collision probability using the birthday paradox.
Ask about the expected scale (number of keys), acceptable key length, and whether keys need to be globally unique or just unique within a namespace. This determines the approach.
Discuss options: random generation (e.g., using a secure random generator and base62 encoding), hashing a unique input (like a URL or ID), or using a counter with base62 encoding. Mention trade-offs like predictability and length.
Explain how to estimate collision probability using the birthday paradox. For example, with 62^6 possible keys, collisions become likely after ~62^3 keys. This informs whether collisions need active handling.
Describe strategies: check if key exists in storage (e.g., database) and regenerate if collision; use a bloom filter for efficient pre-check; or append a counter or random suffix. In distributed systems, use a centralized service or unique node IDs to avoid collisions.
Discuss performance implications: database lookups add latency, bloom filters use memory but are fast, and pre-allocating key ranges can reduce collisions. Mention that for very high scale, a dedicated key generation service (like Snowflake) might be better.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I went with a key-value store for the redirect path since the access pattern is basically a point lookup by short code.
Start by clarifying the requirements and constraints of the system, then propose a storage solution that aligns with those needs, and finally describe the data model with entities, relationships, and access patterns. Emphasize trade-offs and justify your choices based on factors like scalability, consistency, and query patterns.
Pro tip: At a financial institution like JP Morgan, always mention regulatory compliance, data security, and auditability as key considerations in your storage and data modeling decisions.
Ask questions to understand the use case, data volume, read/write patterns, latency requirements, consistency needs, and budget constraints.
Recommend a storage technology (e.g., relational, NoSQL, data lake, time-series) and explain why it fits the requirements, mentioning alternatives and trade-offs.
Outline the main entities, their attributes, relationships, and how they will be stored (e.g., tables, documents, key-value pairs). Include indexing and partitioning strategies.
Explain how the data will be queried and updated, and how the model supports those operations efficiently.
Summarize the pros and cons of your approach, and how it can scale or evolve over time.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about an in-memory cache sitting in front of the KV store for hot URLs.
Start by clarifying the requirements: what 'popular' means (e.g., top N redirects by request volume), expected read/write ratio, latency targets, and consistency needs. Then propose a caching layer (e.g., Redis or in-memory cache) with a well-defined key structure, TTL, and invalidation strategy, and discuss trade-offs like cache stampede, memory limits, and consistency vs. performance.
Pro tip: Mention that you would start with a simple cache-aside pattern and measure hit rate and latency improvements before adding complexity like write-through or multi-level caching. Also, highlight the importance of monitoring cache effectiveness and having a fallback to the origin to avoid outages.
Ask about traffic patterns, definition of 'popular', read/write ratio, latency SLOs, and consistency requirements. This ensures your solution aligns with business and technical needs.
Select a cache-aside approach with a distributed cache like Redis for scalability, or an in-memory cache for lower latency. Justify based on requirements.
Define a key structure (e.g., 'redirect:{short_code}') and store the target URL and metadata. Consider TTL based on how often redirects change.
Decide on TTL, explicit invalidation on updates, or write-through. Discuss trade-offs between consistency and performance, and how to avoid stale redirects.
Plan for cache stampede (e.g., using locks or probabilistic early expiration), hot keys, and cache outages. Include monitoring and fallback to origin.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I said async event queue to avoid adding latency to the redirect path.
Start by clarifying the requirements: what events to track (clicks, redirects), expected scale, latency needs, and existing infrastructure. Then propose a high-level architecture that captures events reliably, processes them (e.g., stream or batch), and stores aggregated counts for fast querying. Finally, discuss trade-offs and how you'd ensure data accuracy and scalability.
Pro tip: Emphasize idempotency and exactly-once processing to avoid double-counting, especially in financial systems where accuracy is critical. Also, mention the importance of monitoring and alerting on data quality metrics.
Ask about the scale (events per second), latency requirements (real-time vs batch), data retention, and how the analytics will be consumed (dashboards, reports).
Propose a client-side or server-side event capture mechanism, such as a lightweight API endpoint or SDK, that logs click and redirect events with necessary metadata (timestamp, user ID, URL, etc.).
Select a stream processing framework (e.g., Kafka + Flink) for real-time or a batch system (e.g., Spark) for periodic aggregation, depending on latency needs.
Use a scalable datastore (e.g., Cassandra, Redis, or a time-series DB) to maintain counts, ensuring atomic increments or idempotent writes to avoid double-counting.
Add error handling, retries, and dead-letter queues; monitor for data loss or duplication and set up alerts on key metrics.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Rate limiting on the write endpoint, URL scanning against a blocklist.
Start by clarifying the types of abuse (malicious URLs, API misuse) and the context (e.g., public API, user-generated content). Then outline a layered defense strategy covering prevention, detection, and response, emphasizing trade-offs between security, usability, and performance. Conclude with how you would measure and iterate on the solution.
Pro tip: In a financial institution like JP Morgan, emphasize compliance and risk management—mention specific regulations (e.g., PSD2, GDPR) and the need for audit trails. Also, highlight the importance of rate limiting and anomaly detection to protect both customers and infrastructure.
Ask questions to understand the system: Is it a public API? What data is sensitive? Who are the users? Identify potential abuse vectors like URL injection, DDoS, credential stuffing, and data scraping.
Implement input validation, URL whitelisting/blacklisting, and sanitization. Use API keys, OAuth, and scopes for authentication/authorization. Apply rate limiting and quotas per user/IP.
Set up logging, monitoring, and alerting for anomalous patterns (e.g., sudden spikes, repeated failed logins). Use machine learning for behavioral analysis and threat detection.
Define automated responses like temporary bans, CAPTCHAs, or throttling. Have a manual review process for escalations. Ensure incident response playbooks are in place.
Discuss trade-offs: strict validation may block legitimate users; rate limiting may impact performance. Propose A/B testing, feedback loops, and regular security audits to refine rules.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.