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

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.

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

Graceful degradation allows apps to function partially during service failures.

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

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

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.

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.

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

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

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

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

Backpressure helps manage message flow in Kafka during traffic spikes.

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

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