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Monolithic and microservices architectures each have unique advantages and challenges for software development.

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

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

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

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

A reverse proxy routes client requests to different backend services efficiently.

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

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

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

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

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

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

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

An API Gateway simplifies client access to backend services in microservices.

Microservices and modular monoliths differ mainly in deployment and communication methods.

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

Distributed locks ensure only one server performs a task, avoiding duplication.

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

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.

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

The Saga Pattern manages distributed transactions using local transactions and compensating actions.

Protobuf schema evolution requires careful changes to avoid breaking existing clients.

Distributed tracing helps track requests across microservices to find delays.

The Bulkhead Pattern isolates resources to protect critical workloads from failures.

Timeouts in distributed systems indicate suspicion, not confirmed failure.