Start by clarifying requirements and scale, then design a high-level architecture that separates media storage, metadata, and delivery. Focus on the 24-hour expiry using TTL and lazy deletion, and design an offline-first client with a sync protocol that handles conflicts and idempotency.
Pro tip: Emphasize the trade-offs between eager vs. lazy deletion and how you'd handle clock skew and time zones for expiry. Also, discuss how you'd ensure sync reliability with retries and conflict resolution, showing you've thought about edge cases.
Ask about user base, daily active users, story creation rate, and read patterns. Clarify what 'save' means (private vs. public) and offline capabilities (e.g., create stories offline, view cached stories).
Outline components: mobile clients, API gateway, story service, media storage (e.g., S3), metadata store (e.g., Cassandra), CDN for delivery, and a sync service for offline actions.
Design schemas for stories, saved stories, and user feeds. Use TTL in the metadata store for automatic expiry, and consider lazy deletion for media. For saved stories, store references with no TTL.
Design client-side storage (e.g., SQLite) for offline actions. Implement a sync protocol with a queue of operations, idempotent APIs, and conflict resolution (e.g., last-write-wins or merge).
Discuss trade-offs: TTL vs. lazy deletion, consistency vs. availability, sync frequency vs. battery. Address scaling with sharding, caching, and CDN.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about blob storage with a TTL-based cleanup job and CDN edge caching with short cache lifetimes for active stories.
Start by clarifying the scale and requirements (e.g., number of stories, media size, global user base) and then propose a high-level architecture that separates storage (object store) from delivery (CDN). Discuss how to handle expiry and retention policies through metadata-driven lifecycle management, and highlight trade-offs between cost, latency, and durability.
Pro tip: Emphasize that media expiry should be driven by metadata and asynchronous processes (e.g., TTL-based deletion) rather than hard-coded in the storage layer, to allow flexibility and avoid accidental data loss. Also, mention that CDN caching should respect expiry headers to avoid serving stale content.
Ask about the expected number of stories, average media size, global distribution, and specific expiry/retention requirements (e.g., 24-hour stories, legal holds). This ensures the design meets actual needs.
Propose using an object storage system (e.g., S3, GCS) for durable, scalable storage of media. Discuss partitioning strategies (e.g., by user ID or time) and metadata storage (e.g., in a database) to track expiry and retention policies.
Leverage a CDN to cache media at edge locations for low-latency global delivery. Explain how to set cache-control headers based on expiry (e.g., max-age) and how to invalidate or purge content when stories are deleted or expire.
Describe a system where metadata includes TTL and retention rules. Use asynchronous workers or scheduled jobs to delete expired media from storage and purge CDN caches. For retention (e.g., legal holds), flag objects to prevent deletion.
Address trade-offs: cost vs. performance (e.g., storage class selection, CDN pricing), consistency vs. availability (e.g., eventual consistency in deletion), and optimizations like tiered storage or lazy deletion.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the core entities and their relationships: stories (ephemeral content), views (user interactions), and favorites (user preferences). Then propose a schema that balances read/write performance, scalability, and Snapchat's specific needs like ephemerality and high-volume writes.
Pro tip: Mention how you'd handle the ephemeral nature of stories (e.g., TTL indexes) and the high write volume of views (e.g., time-series or columnar storage) to show you understand Snapchat's unique challenges.
Ask about expected read/write patterns, data volume, and latency requirements. For Snapchat, assume high write throughput for views and low-latency reads for stories.
Define entities: User, Story, View, Favorite. Determine relationships: a user creates many stories, a story has many views, a user can favorite many stories.
Propose tables/collections with fields and indexes. For stories, include TTL for ephemerality. For views, consider a time-series or wide-column store. For favorites, use a simple key-value or relational table.
Explain how to efficiently fetch a user's stories, count views per story, and list a user's favorites. Discuss denormalization or caching for hot paths.
Compare SQL vs NoSQL choices, sharding strategies, and how to handle growth. Mention consistency vs availability trade-offs for views.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Went with a visibility enum on the story: public, friends-only, custom list.
Start by clarifying the types of stories (e.g., private, public, custom) and the access control requirements for each. Then propose a flexible model that combines role-based and attribute-based access control, and discuss trade-offs around performance, privacy, and user experience. Conclude with how you would handle edge cases like sharing, expiration, and revocation.
Pro tip: Emphasize the principle of least privilege and the need for auditability; mention that privacy is a product feature, not just a technical constraint, and that you'd design for user trust and transparency.
Ask questions to understand the different story types (private, friends-only, public, custom) and the access rules for each. Identify who can view, contribute, and share.
Propose a hybrid model using RBAC for roles (owner, contributor, viewer) and ABAC for dynamic attributes (friendship, location, time). Explain how policies are evaluated.
Describe how access checks are enforced at API and data layers, including caching for performance. Discuss how to handle sharing and revocation.
Incorporate privacy principles like data minimization, consent, and audit logging. Mention compliance with regulations (e.g., GDPR, CCPA).
Compare centralized vs. decentralized enforcement, latency vs. security, and how the model scales with millions of users. Suggest monitoring and iteration.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This is where I felt most out of my depth.
Start by clarifying the requirements and scale (e.g., millions of followers, read-heavy workload). Then discuss the fan-out problem, comparing push (write fan-out) vs pull (read fan-out) models, and propose a hybrid approach with optimizations like caching, precomputation, and asynchronous processing. Conclude by addressing trade-offs and potential bottlenecks.
Pro tip: Mention that for Snapchat, ephemeral content and real-time delivery are critical, so a push-based approach with edge caching and CDN integration is often preferred, but be ready to discuss how to handle celebrity accounts with millions of followers via a hybrid model.
Ask about the number of followers, read/write ratio, latency requirements, and consistency needs to frame the problem.
Define fan-out as the challenge of delivering a single story to millions of followers efficiently, and contrast push vs pull models.
Suggest using push for normal users and pull for celebrities, or a combination, to balance write and read loads.
Cover caching (e.g., Redis), CDNs, asynchronous queues, and precomputation to reduce latency and load.
Analyze trade-offs like latency vs cost, and identify potential bottlenecks (e.g., hot partitions) and mitigation strategies.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.