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

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

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

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

Fallbacks allow systems to handle failures safely and maintain user experience.

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

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

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

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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

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

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

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

Distributed tracing helps track requests across microservices to find delays.

Correlation IDs help track requests in microservices for easier debugging.

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

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

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

A Circuit Breaker helps prevent system failures by stopping calls to unhealthy services.

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

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

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

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

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

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

A timeout indicates a deadline was exceeded, not necessarily a failure.

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

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

Graceful shutdown in Kafka ensures no data loss during consumer termination.

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

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

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

Structured logging helps in quickly identifying issues by organizing log data consistently.

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

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

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