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

Correlation IDs help track requests in microservices for easier debugging.

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

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

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.

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

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

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.

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

Kafka uses brokers for data handling and KRaft controllers for metadata management.

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

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

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

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.

Graceful degradation allows apps to function partially during service failures.

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.

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

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

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

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

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

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

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

Quorum Reads ensure data consistency by querying multiple replicas in distributed systems.

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.

Logs, metrics, and traces help engineers debug issues in software systems.

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 consumer reprocessing involves safely replaying events to correct system states.

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.

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

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

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

Kafka consumers resume processing from the last committed offset after a crash.

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

Kafka producers use ACKs, retries, and idempotence to ensure reliable message delivery.

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

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.

Kafka Streams offers higher-level abstractions, while Kafka Consumer provides direct control.

Kafka maintains message order within partitions using keys for routing.

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

Kafka consumers can pause and resume message processing to handle backpressure effectively.

Kafka Partition Reassignment moves replicas between brokers while keeping data safe.

ZooKeeper watches notify clients of changes, avoiding constant polling and reducing server load.

Kafka rebalance can lead to lost or duplicated work if not handled carefully.