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

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

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

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

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

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

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

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

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

Distributed tracing helps track requests across microservices to find delays.

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

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

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

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

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

Correlation IDs help track requests in microservices for easier debugging.

The RED Method helps monitor microservices using Rate, Errors, and Duration metrics.

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

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

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

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

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

REST API design focuses on creating clean, efficient, and scalable web services.

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

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

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

Graceful degradation allows apps to function partially during service failures.

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

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

Request-Response asks for a result, while Events notify that something has happened.

Queues distribute tasks to one consumer, while Pub/Sub broadcasts events to multiple subscribers.

Synchronous communication waits for a response, while asynchronous continues without waiting.

Cascading failures occur when one service's failure impacts others, causing widespread issues.

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

Partial failure means some services fail while others keep running, impacting system reliability.

Kafka Schema Evolution allows safe changes to message structures without breaking existing consumers.

HLD provides an overview of system architecture, while LLD details implementation specifics.

The Circuit Breaker pattern controls service calls to prevent failures from spreading.

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