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Stateless servers are easier to scale because they don't store user session data locally.

A CDN speeds up web applications by caching content closer to users.

FAANG engineers use high-level design principles to create scalable systems.

Distributed caching spreads data across multiple servers for better performance and reliability.

Service Discovery helps microservices locate each other dynamically without fixed IP addresses.

Choosing the right boundaries for microservices is crucial for effective architecture.

Caching helps applications respond faster by serving data from memory instead of hitting the database.

Load balancers distribute traffic across multiple servers to enhance performance and reliability.

Database per Service ensures each microservice owns its data, reducing dependencies.

Sharding splits databases into smaller parts to improve performance and scalability.

Availability is about access; reliability is about correct performance.

RPC allows one service to call a function in another service over the network.

Kafka uses state stores to remember previous events in stateful stream processing.

Microservices are useful when you need independent scaling, deployment, and clear boundaries in software design.

Vertical scaling means upgrading one server, while horizontal scaling means adding more servers.

gRPC supports four communication patterns: Unary, Server Streaming, Client Streaming, and Bidirectional Streaming.

Microservices can communicate using five key patterns based on their needs.

Cache breakdowns can overwhelm databases, but request coalescing solves this issue.

Consistent hashing helps distribute data across servers with minimal movement.

Message queues allow services to communicate asynchronously, improving scalability and reliability.

Choosing between a shared database and a database per service affects system design significantly.

L4 and L7 load balancers differ in how they route network traffic based on layers of the OSI model.

Distributed backpressure helps manage the flow of work between services to prevent overload.

The CAP Theorem explains the trade-offs in distributed systems during network failures.

API Gateway, Proxy, and Load Balancer serve different roles in managing requests and traffic in systems.

Search engines collect, index, retrieve, and rank documents to provide relevant results.

Dependency failures can disrupt applications, but resilience patterns can help manage them.

Connection pooling reuses database connections but can cause issues if misconfigured.

DNS helps microservices find each other, but it has limitations that may require a service registry.

REST, RPC, and gRPC are different methods for communication in software systems.

gRPC allows efficient communication between services using a structured request-response model.

Redis is a fast in-memory data store used for caching and low-latency applications.

The USE Method helps find and analyze infrastructure bottlenecks in software systems.

Elasticsearch ILM automates index management to reduce storage costs and meet data needs.

DNS resolution translates a URL into an IP address for web access.

Kafka Streams uses local state for quick data access and changelogs for recovery.