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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.

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.

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

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

Distributed tracing helps track requests across microservices to find delays.

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

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

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

Elasticsearch CCR allows real-time data replication for faster disaster recovery.

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

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

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

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

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

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

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

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

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

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

Kafka uses replication and in-sync replicas to ensure data is always available.

Cache breakdowns can overwhelm databases, but request coalescing solves this issue.

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

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

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

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

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

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

Kafka uses different strategies to assign partitions to consumers in a group efficiently.

Redis is a fast in-memory data store used for caching and low-latency applications.

Backpressure helps manage message flow in Kafka during traffic spikes.

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