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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.

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.

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.

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.

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

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

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.

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.

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

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

Backpressure helps manage message flow in Kafka during traffic spikes.

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

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

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