Meta Backend Engineer System Design
Ace your Meta backend engineer system design interview with our AI-powered real-time coach. Get instant guidance on scalability, distributed systems, and Meta-specific architecture patterns that showcase your engineering expertise.
Meta's Key Technologies
Our AI coach helps you understand Meta's technology stack and architecture patterns
TAO (The Associations and Objects)
Understand Meta's distributed data store that serves the social graph, handling billions of reads and millions of writes per second with low latency.
Unicorn
Learn about Meta's search infrastructure that powers search functionality across their family of apps, handling complex ranking and retrieval at scale.
Presto
Explore Meta's distributed SQL query engine for big data analytics that allows querying data where it lives, including Hive, Cassandra, and relational databases.
Twine
Understand Meta's workflow management system for handling complex, multi-stage data processing pipelines with dependencies and scheduling.
Prophet
Learn about Meta's forecasting tool for time series data that handles seasonality and holiday effects, used for capacity planning and anomaly detection.
ZippyDB
Explore Meta's distributed key-value store built on top of RocksDB, providing high throughput, low latency, and strong consistency guarantees.
See Meta System Design AI in Action
Interviewer [00:00:05]: Design a scalable backend system for a social media feed that can handle millions of users, with features for posting updates, following users, and generating personalized feeds.
You [00:00:15]: I'd like to start by clarifying the requirements and constraints. What's the scale we're targeting in terms of users, posts, and read/write ratios? Are there any specific performance requirements or features we should prioritize?
Interviewer [00:00:28]: Let's say we have 10 million daily active users, with each user following about 200 others on average. Users post 2 updates per day on average, and read their feed about 20 times per day. The feed should load in under 100ms, and new posts should appear in followers' feeds within 5 seconds.
AI Suggestion:
- System Requirements Analysis:
- Calculate scale: 10M users × 2 posts/day = 20M write operations/day
- Read operations: 10M users × 20 reads/day = 200M read operations/day
- Read:Write ratio = 10:1 (read-heavy system)
- Emphasize Meta's scale challenges: billions of edges in social graph
- Data Model & Storage:
- Propose graph structure similar to TAO (Meta's social graph store)
- Suggest sharding strategy by user_id with consistent hashing
- Discuss read vs. write optimization trade-offs
- Mention RocksDB/ZippyDB for storage (used at Meta)
- Feed Generation Approaches:
- Compare push vs. pull models with hybrid approach (Meta uses both)
- Discuss fanout-on-write for high-value content
- Explain ranking algorithms and personalization
- Mention caching strategies with TTLs for different content types
- Scalability & Performance:
- Discuss load balancing with consistent hashing
- Explain cache hierarchies (L1-L3 caching like Meta uses)
- Mention data denormalization for performance
- Discuss handling hot users/celebrities (Meta's special case handling)
- Meta-Specific Optimizations:
- Mention TAO-like caching for social graph operations
- Discuss regional data centers and replication strategy
- Explain how to handle global events and traffic spikes
- Mention dark reads/writes for testing in production
🏗️ Meta-Scale Architecture Design
Get expert guidance on designing systems that can scale to Meta's billions of users, with insights into their distributed systems architecture, data storage patterns, and performance optimization techniques.
⚡ Scalability Pattern Library
Access our comprehensive library of scalability patterns including sharding, replication, caching strategies, and load balancing approaches with real-time suggestions tailored to Meta's engineering challenges.
🔄 Distributed Systems Expertise
Receive instant guidance on distributed systems concepts crucial for Meta interviews, including consensus protocols, eventual consistency, and strategies for building reliable systems at global scale.
📊 Data Storage & Processing
Get real-time suggestions for data modeling, storage selection, and processing pipelines that align with Meta's technologies like TAO, Presto, and ZippyDB for different use cases.
🔒 System Reliability & Fault Tolerance
Access expert advice on designing reliable systems with failure detection, graceful degradation, and recovery mechanisms that maintain availability even during partial outages.
📝 Communication Framework
Get guidance on effectively communicating your design decisions, trade-offs, and technical depth in a way that resonates with Meta's engineering culture and interview expectations.
Top Meta Engineering Resources
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