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

Message delivery semantics define how messages are sent in distributed systems and handle failures.

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

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

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

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

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

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

Network partitions occur when servers are operational but can't communicate, affecting system performance.

Classifying failures and using controlled retries in Kafka prevents outages.

Token Bucket allows burst traffic, while Leaky Bucket smooths out traffic flow.

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

Backpressure helps manage message flow in Kafka during traffic spikes.

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

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

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

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

Availability is about access; reliability is about correct performance.

Latency, throughput, and response time are key metrics in system performance.

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

The CAP Theorem explains the trade-offs in distributed systems during network failures.

Protobuf schema evolution requires careful changes to avoid breaking existing clients.

Backpressure prevents system overload by managing message processing rates in Kafka.

2-Phase Commit helps maintain data consistency in transactions across multiple databases.

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

Idempotency ensures that repeated operations in APIs do not cause duplicate effects.

Vector clocks help track the order and concurrency of events in distributed systems.