Snapchat·Software Engineer·Onsite - System Design / Architecture·Senior

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Jun 2026

Summary

System design round at Snapchat for a software engineering role, focused entirely on building a production-grade API for co-purchase analytics. Dense question, lots of surface area to cover.

Questions Asked (1)

Design a RESTful API system for co-purchase analytics, including endpoints to retrieve top related products for a given product, support filtering by time window, region, and channel, and paginate results. Cover request/response schemas, auth, rate limiting, caching and invalidation, SLA and timeouts, idempotency, and versioning. Also describe the storage model, precomputation pipeline, consistency guarantees, monitoring, testing strategy, and safe rollout.

System DesignAPI & IntegrationsTechnical Trade-offs

Author's notes

This was basically a full system design compressed into one question.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements and scale, then design the API contract with clear endpoints, schemas, and auth. Next, detail the storage and precomputation pipeline, followed by operational concerns like caching, rate limiting, and monitoring. Finally, discuss consistency, testing, and rollout strategy, emphasizing trade-offs throughout.

Pro tip: At Snapchat scale, co-purchase analytics must handle high write throughput and low-latency reads; consider a lambda architecture with batch precomputation and real-time updates, and explicitly discuss how you'd handle hot partitions and data skew.

1. Clarify Requirements and Scale

Ask about expected QPS, data volume, latency SLA, and consistency needs. Define functional requirements: top related products with filters (time, region, channel) and pagination.

2. Design API Contract

Define endpoints (e.g., GET /products/{id}/related), request parameters (time_window, region, channel, page, page_size), response schema (list of products with scores), auth (OAuth2/JWT), rate limiting (token bucket per user/IP), and versioning (URL path or header).

3. Design Storage and Precomputation

Choose a storage model: e.g., precomputed co-purchase counts in a wide-column store (Cassandra) or graph DB. Describe batch pipeline (Spark) to compute co-purchase matrices and incremental updates (Kafka + Flink) for real-time.

4. Address Operational Concerns

Cover caching (Redis with TTL and invalidation on new data), SLA/timeouts (e.g., p99 < 100ms), idempotency (idempotency keys for writes), monitoring (metrics, logging, tracing), and testing (unit, integration, load).

5. Discuss Consistency and Rollout

Explain consistency guarantees (eventual consistency for analytics, read-your-writes for user actions), and safe rollout (canary, feature flags, A/B testing).

Key Points to Mention

Use precomputation for heavy lifting: batch jobs compute co-purchase counts periodically, while streaming updates handle recent data.
Cache aggressively with Redis, but design invalidation carefully—e.g., time-based TTL and event-driven invalidation on new purchases.
Implement rate limiting per API key and user to prevent abuse, using a distributed token bucket (e.g., Redis).
Ensure idempotency for any write endpoints (e.g., updating product relationships) using idempotency keys.
Monitor key metrics: latency, error rates, cache hit ratio, and pipeline lag; set up alerts for SLA violations.
Version the API from day one (e.g., /v1/) and maintain backward compatibility; use feature flags for gradual rollout.

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