← ⚙️ System Design for AI in ProductionNEXT IN SYSTEM DESIGN FOR AI IN PRODUCTIONConcurrency and Thread Safety→
Core
CAP and Consistency Models
The CAP theorem says that during a network partition a distributed system has to choose between consistency and availability; you cannot have both while the network is split. PACELC extends it: even when there is no partition, you trade latency against consistency. Consistency models form a spectrum from linearizability (acts like one copy, real-time order) down through causal to eventual consistency. Logical clocks (Lamport, vector) order events without synchronized wall clocks. AI, ML, and GenAI engineer interviews probe it because every replicated store, queue, and feature pipeline sits somewhere on this spectrum, and naming the point precisely sets senior candidates apart.
a free account unlocks the core curriculum tier · no card
RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
System Design for AI in ProductionDesign a system that records events in a single globally consistent order across many machines.→System Design for AI in ProductionHow do CAP and consistency tradeoffs apply to an ML feature store and online serving?→System Design for AI in ProductionDesign a distributed key-value store (partitioning, replication, and consistency).→System Design for AI in ProductionDesign a payment ledger that records money movement with exactly-once semantics and no lost cents.→System Design for AI in ProductionDesign a distributed cache like Redis or Memcached that serves millions of reads per second.→RAG & Agent System DesignDesign a production RAG system over 10M documents serving ~1,000 QPS at sub-second latency.→
COMPANIES THAT ASSUME THIS
