I started with requirements and that part went okay.
Start by clarifying requirements and scale, then design the core data model and storage strategy for ephemeral media, followed by the upload/viewing flows and expiration mechanism. Emphasize trade-offs around consistency, latency, and cost, and discuss how to handle scale and reliability.
Pro tip: Proactively discuss how you would handle the 'hot user' problem (e.g., a celebrity with millions of friends) and the trade-offs between push vs. pull for story delivery, showing you understand real-world scaling challenges.
Ask questions to understand functional and non-functional requirements: number of users, daily active users, average friends per user, media size, view patterns, and consistency needs. Estimate scale (e.g., 100M DAU, 1B stories/day).
Outline the main components: mobile clients, API gateway, media upload service, metadata service, story service, friend graph service, and expiration service. Sketch the flow from upload to viewing.
Design how to store media (object storage like S3) and metadata (NoSQL like Cassandra for scalability). Discuss TTL for automatic expiration and indexing for fast retrieval of friends' stories.
Detail the upload process: client uploads media to a pre-signed URL, then metadata is written. For viewing, describe how to fetch friends' story metadata and then media, considering caching and CDN for low latency.
Explain how stories expire after 24 hours: use TTL in the database and a background job to delete media from object storage. Discuss trade-offs between lazy deletion and scheduled cleanup.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
CDN was the obvious answer and I said it pretty quickly.
Start by clarifying requirements: scale, media types, latency, and cost constraints. Then design a pipeline that separates upload (ingest) from delivery (serving), using object storage, CDN, and asynchronous processing. Discuss trade-offs between consistency, availability, and cost, and how to handle failures and spikes.
Pro tip: Emphasize the importance of decoupling upload and processing with a message queue to handle bursts and enable retries, and mention using signed URLs for secure direct uploads to object storage to reduce server load.
Ask about scale (daily uploads, concurrent users), media types (images, videos), latency requirements, and budget constraints. This ensures the design meets actual needs.
Outline the main components: client, upload service, object storage, processing pipeline, CDN, and metadata database. Explain how they interact.
Detail how media is uploaded: use signed URLs for direct-to-storage uploads, chunked/resumable uploads for large files, and a message queue to trigger processing.
Describe asynchronous processing (transcoding, thumbnails, moderation) and storage tiers (hot vs. cold). Discuss metadata storage and indexing for fast retrieval.
Explain CDN usage for global low-latency delivery, caching strategies, and how to handle spikes with auto-scaling and rate limiting. Mention monitoring and failure handling.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about using a key-value store with TTL set at write time.
Start by clarifying requirements: scale, read/write patterns, and consistency needs. Then propose a storage solution with TTL, such as a NoSQL database with TTL indexes or a cache with expiration, and explain the expiration process, including cleanup and potential data archival. Finally, discuss trade-offs and how to handle edge cases like delayed expiration and data recovery.
Pro tip: Mention that TTL enforcement is not instantaneous and can be delayed, so design your system to tolerate expired data being read briefly. Also, consider using a two-tier storage approach: hot storage with TTL for active stories and cold storage for archival if needed.
Ask about scale (e.g., millions of stories per day), read/write ratio, latency requirements, and whether expired stories need to be archived or permanently deleted.
Select a database that supports TTL natively, such as DynamoDB with TTL, Cassandra with TTL, or Redis with expiration. Explain why it fits the requirements.
Define the schema for story metadata, including fields like story_id, user_id, creation_time, media_url, and TTL. Consider indexing for efficient queries.
Describe how TTL works: the database automatically deletes items after the TTL expires. Mention that deletion may be delayed and how to handle reads of expired items (e.g., filter by creation_time).
Talk about consistency, cost, and performance implications. Address edge cases like clock skew, delayed deletion, and whether to archive expired stories.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the requirements and scale of the friends' Stories feed, then compare push and pull fanout models in terms of latency, cost, and complexity. Recommend a hybrid approach that leverages push for active users and pull for others, and justify based on Snapchat's specific constraints like ephemeral content and large friend graphs.
Pro tip: Emphasize that the choice depends on read/write patterns and user activity distribution; mentioning a hybrid model shows you understand real-world trade-offs beyond textbook answers.
Ask about scale (DAU, friends per user), latency requirements, and content ephemerality to ground your answer in Snapchat's context.
Briefly explain push (write-time fanout to followers' feeds) and pull (read-time aggregation from friends' stories) models and their basic trade-offs.
Compare push vs. pull on write amplification, read latency, storage cost, and complexity, considering Snapchat's large friend graphs and ephemeral content.
Recommend a hybrid approach: push for active users to ensure low latency, pull for inactive users to reduce write load, and explain how to handle ephemerality.
Summarize why the hybrid model best balances Snapchat's needs for low latency, scalability, and cost-efficiency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Suggested storing view state in a fast read store keyed by user and story ID.
Start by clarifying requirements: scale (e.g., millions of users, billions of views), read/write patterns, latency needs, and consistency requirements. Then propose a data model and storage solution, such as a distributed key-value store with per-user sets of viewed story IDs, and discuss trade-offs like memory usage, TTL, and sharding. Finally, address scalability concerns like hot keys, replication, and caching.
Pro tip: Mention that you would use a probabilistic data structure like a Bloom filter or HyperLogLog to reduce memory footprint, but note the trade-off of false positives and the need for a fallback to exact storage for correctness. This shows you understand both efficiency and correctness at scale.
Ask about scale (number of users, stories, views per day), read/write ratio, latency requirements, and consistency needs (e.g., is it okay to occasionally show a viewed story again?).
Propose a data model: for each user, store a set of viewed story IDs. Consider using a key-value store like Redis or Cassandra, with user ID as the key and a set or sorted set of story IDs as the value.
Discuss sharding by user ID to distribute load, replication for availability, and TTL to expire old views. Address memory constraints by using compact representations (e.g., bitmaps, Bloom filters) and tiered storage (hot vs. cold).
Explain how a view is recorded (write) and how the system checks if a story was viewed (read). Consider caching frequently accessed data and using asynchronous writes for high throughput.
Compare exact vs. approximate tracking, discuss consistency vs. availability, and mention alternatives like using a graph database or a time-series database if access patterns are more complex.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Short answer: store an audience type on the story metadata and filter at read time, with the friends list checked against a separate social graph service.
Start by clarifying the requirements: privacy rules are set per Story by the creator, and visibility must be enforced at read time for viewers. Then describe a system that stores audience rules as metadata, evaluates them at query time using an efficient index, and ensures consistency across services. Emphasize trade-offs between precomputation and real-time evaluation, and how to handle edge cases like friend list changes.
Pro tip: Mention that privacy enforcement should happen at the data access layer (e.g., in the API gateway or a dedicated authorization service) to avoid duplication and ensure consistency, and that you'd use a deny-by-default approach with explicit allow rules.
Ask questions to understand the expected scale, latency requirements, and whether privacy rules can change after a Story is posted. Confirm that the core need is to enforce visibility based on the viewer's relationship to the creator.
Define how to represent audience rules (e.g., close friends list, all friends, custom lists) and how to store the creator-viewer relationship graph. Consider using a graph database or a friend list service with efficient lookups.
Describe how to evaluate visibility when a viewer requests a Story: fetch the Story's audience rule, check the viewer's relationship against that rule, and allow or deny access. Discuss caching and indexing to meet latency goals.
Explain how to handle changes to friend lists or privacy settings after a Story is posted. Consider whether to precompute visibility lists or evaluate dynamically, and how to invalidate caches.
Discuss partitioning, sharding, and rate limiting. Cover edge cases like blocked users, deleted friends, and privacy rule changes mid-viewing. Mention monitoring and auditing for privacy compliance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.