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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.

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.

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.

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.

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.

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

Alerting helps notify the right people about production issues quickly.

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.

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

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.

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.

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