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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.

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

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

Classifying failures and using controlled retries in Kafka prevents outages.

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

Graceful degradation allows apps to function partially during service failures.

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.

Crash failures stop a service, while network failures disrupt communication.

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.

Kafka uses retries and Dead Letter Queues to manage message processing failures.

Distributed tracing helps track requests across microservices to find delays.

Poison messages in Kafka can cause infinite retries, risking system stability.

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.

Kafka consumer reprocessing involves safely replaying events to correct system states.

Kafka uses retry topics and delayed retries to manage message failures efficiently.

You can replay Kafka events safely by separating live and replay processes.

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.

Alerting helps notify the right people about production issues quickly.

Fail Fast means stopping quickly on errors, while Fail Safe ensures safety in failures.

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.

The retry pattern helps systems recover from temporary failures but can cause overload if misused.

Kafka can help ensure messages are processed once, but requires careful design.

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

Retry storms occur when too many retries overload a failing service.

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

An error budget shows how much downtime is acceptable while meeting reliability goals.

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

Kafka commit strategies determine how offsets are managed during message processing.

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

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

Fail-stop means a system stops and stays down, while fail-recover means it can come back but needs to be ready.

Kafka uses Dead Letter Topics to handle messages that repeatedly fail processing.

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

Kafka's commitSync waits for confirmation, while commitAsync continues immediately.

Backpressure helps manage message flow in Kafka during traffic spikes.

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

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

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

Kafka allows safe message reprocessing by resetting consumer positions without altering data.

Older offsets can overwrite newer ones in Kafka if not handled properly.