I started with requirements which felt right, read-heavy workload, latency sensitivity, high availability.
Start by clarifying the scope and scale with the interviewer, then systematically cover functional and non-functional requirements before diving into a high-level architecture. Focus on key components like feed generation, media storage, and scalability, and be prepared to discuss trade-offs.
Pro tip: Emphasize the read-heavy nature of the system and how you would optimize for it, such as using CDNs and caching. Also, proactively discuss trade-offs between consistency and availability, showing you understand real-world constraints.
Ask questions to understand the scope: expected scale (users, photos, videos), core features (upload, feed, follow, like, comment), and any specific constraints. This ensures you're solving the right problem.
List the key functional requirements (e.g., user can upload media, follow others, view feed) and non-functional requirements (e.g., low latency, high availability, scalability, durability). Prioritize them based on the interviewer's input.
Sketch the main components: client, API gateway, services (user, media, feed, etc.), databases (SQL/NoSQL), object storage for media, CDN, and caching layers. Explain how data flows through the system.
Pick 1-2 critical areas to detail, such as feed generation (push vs. pull model) or media storage and delivery (upload pipeline, transcoding, CDN). Discuss trade-offs and justify your choices.
Discuss how the system scales (sharding, replication, caching), handles bottlenecks, and trade-offs (e.g., consistency vs. latency, cost vs. performance). Mention monitoring and failure handling.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Object store plus CDN, pretty standard answer.
Start by clarifying requirements (e.g., media types, read/write ratio, latency, durability) and then propose a high-level architecture that separates storage, processing, and delivery. Emphasize scalability, fault tolerance, and cost-efficiency, and discuss trade-offs between consistency, latency, and cost.
Pro tip: Demonstrate awareness of Meta's specific scale and existing infrastructure (e.g., Haystack, f4, Tao) by mentioning how your design could integrate with or learn from them. Also, proactively discuss monitoring and failure handling to show operational maturity.
Ask questions to understand the scope: media types (photos, videos), upload/download patterns, latency requirements, durability, and budget constraints. This ensures your design targets the right problems.
Outline the main components: ingestion service, storage layer (blob store), metadata service, processing pipeline (transcoding, thumbnailing), and CDN for delivery. Explain how they interact.
Discuss storage options: object storage (e.g., S3-like) for blobs, distributed file systems, or custom solutions like Haystack. Cover data partitioning, replication, and consistency models.
Explain how to serve media efficiently using CDNs, edge caching, and adaptive bitrate streaming for videos. Address cache invalidation and geographic distribution.
Discuss how to scale each component horizontally, handle failures (replication, erasure coding), and ensure availability. Mention monitoring, alerting, and capacity planning.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the part I was least prepared for in terms of actual trade-off depth.
Start by defining the three approaches and their trade-offs in terms of latency, cost, and scalability. Then, discuss how each handles key challenges like fan-out on write vs. read, and finally recommend a hybrid approach that balances these trade-offs, justifying your choice with concrete examples.
Pro tip: Emphasize that the choice depends on user behavior and system constraints, and that a hybrid approach often works best by combining push for active users and pull for inactive or celebrity users. Mention that Meta's news feed uses a hybrid model to optimize for both performance and cost.
Briefly explain push (fan-out on write), pull (fan-out on read), and hybrid models, including how they generate a user's news feed.
Compare latency, read/write amplification, storage cost, and scalability for each approach, highlighting scenarios where each excels or fails.
Discuss factors like user activity patterns, celebrity users, and infrastructure costs that influence the choice.
Choose a hybrid approach and explain how it mitigates the downsides of pure push or pull, with examples of how to implement it.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Talked about feed caches, user profile caches, and media metadata.
Start by clarifying the system's requirements and access patterns, then propose a layered caching strategy (client, CDN, application, database) with appropriate eviction policies. Prioritize caching data based on read/write ratio, access frequency, and tolerance for staleness, and discuss trade-offs like consistency vs. latency.
Pro tip: Always tie caching decisions back to business metrics (e.g., QPS, latency SLOs) and explicitly address cache invalidation and stampede protection—these are common failure points in production.
Ask about read/write ratio, data size, latency targets, consistency needs, and traffic patterns to ground your caching strategy.
Propose caching at multiple layers: client-side, CDN, application-level (e.g., Redis), and database query cache, explaining the role of each.
Select data based on frequency of access, cost of recomputation, and staleness tolerance—e.g., user sessions, hot content, reference data.
Choose eviction strategies (LRU, LFU, TTL) and invalidation mechanisms (write-through, write-behind, pub/sub) based on consistency requirements.
Discuss cache stampede protection, sharding, replication, and monitoring to ensure reliability under load.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the system's scope and primary use cases, then identify the core entities and their relationships using an entity-relationship diagram or textual description. Walk through the model from a high-level overview to key attributes and access patterns, justifying design choices based on requirements and scalability.
Pro tip: Emphasize how your data model supports the system's read/write patterns and scalability needs, and mention any trade-offs you considered (e.g., normalization vs. denormalization) to show depth.
Ask clarifying questions to understand the system's purpose, scale, and key operations. This ensures your data model aligns with actual needs.
List the main objects or concepts in the system (e.g., User, Post, Comment) and briefly describe their purpose.
Describe how entities relate (one-to-many, many-to-many) and specify cardinality. Use examples to illustrate.
For each entity, mention important fields and how they will be queried or updated, linking to performance considerations.
Explain design decisions such as normalization, indexing, or sharding, and how they address scale and consistency requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying requirements and constraints, then propose a high-level architecture that addresses scaling from the ground up. Focus on sharding strategies, data partitioning, replication, and trade-offs between consistency and availability, while emphasizing horizontal scaling and fault tolerance.
Pro tip: Demonstrate awareness of Meta's specific challenges by mentioning real-world examples like sharding by user ID and using consistent hashing, and discuss how to handle hotspots and rebalancing without downtime.
Ask about read/write patterns, latency requirements, consistency needs, and data size to tailor the design. Establish assumptions like global distribution and high availability.
Outline a layered approach: load balancers, stateless services, caching, and a sharded database. Mention using a distributed database or custom sharding layer.
Choose a shard key (e.g., user ID) and partitioning method (hash-based, range-based). Discuss consistent hashing to minimize rebalancing and techniques for handling hotspots.
Explain replication for read scalability and fault tolerance, and how to scale writes via sharding. Cover cross-shard queries and distributed transactions if needed.
Discuss trade-offs: consistency vs. availability, latency vs. durability. Mention monitoring, rebalancing, and failure recovery.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.