Layers of a High-Scale System

Uber's initial monolithic architecture, built on a LAMP stack, quickly hit scaling limits, forcing a multi-year migration to microservices. This journey involved transitions from PHP to Node.js and Python, and later to statically-typed languages like Go and Java for improved performance and type safety, a common evolutionary path for systems facing hyper-growth. The complexity of managing over 2,200 microservices eventually led them to a Domain-Oriented Microservice Architecture (DOMA) to regain control. Netflix's massive global caching layer, EVCache, is the backbone of its user experience, handling 400 million operations per second and 30 million replication events globally across 22,000 servers. This distributed in-memory system, based on memcached, is crucial for providing low-latency access to everything from your viewing history to personalized recommendations, ensuring the UI feels instantaneous by avoiding constant database queries. To deliver content with minimal latency, Netflix employs a multi-tiered caching strategy within its Open Connect CDN appliances. The most popular 1% of content is held in RAM for sub-millisecond access, "warm" content resides on high-speed NVMe SSDs, and the long-tail library is stored on HDDs, allowing a single appliance to serve over 100 Gbps of traffic. This hardware-level optimization is a key lesson in cost-performance trade-offs at scale. In the financial sector, event-driven architecture is replacing slow, batch-driven loan origination systems. This asynchronous model provides real-time visibility into loan pipelines, a stark contrast to legacy systems where data silos and overnight batch processing mean teams often work with outdated information, leading to an 18-20% rework rate and delayed closings. For senior-level system design interviews, the focus shifts from basic components to the difficult trade-offs between consistency, availability, and latency under failure conditions (CAP theorem). Candidates are expected to discuss real-world scenarios, such as preventing retry storms during service recovery or ensuring data consistency across distributed databases in the event of a network partition. In high-volume mortgage processing, the challenge lies in digitizing and extracting data from millions of documents. Automation platforms are leveraging AI and intelligent document processing (IDP) to achieve over 99% accuracy in data extraction, moving away from manual, error-prone workflows and reducing loan cycle times by as much as 20%. Technical leadership in a regulated environment like mortgage lending requires balancing innovation with stability. The focus is on enabling teams to evolve the technology stack and adopt patterns like microservices for agility, while ensuring strict compliance, security, and data integrity through robust governance and automation.