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

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

Kafka uses state stores to remember previous events in stateful stream processing.

In distributed systems, timestamps don't always reflect the true order of events.

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

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

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

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

Exponential backoff helps manage retries by increasing wait times to reduce system overload.

Backpressure helps manage message flow in Kafka during traffic spikes.

Lamport Timestamps help order events in distributed systems while preserving causality.

Causality in distributed systems helps understand how events influence each other, beyond just timestamps.

Kafka consumer reprocessing involves safely replaying events to correct system states.

Graceful degradation allows apps to function partially during service failures.

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

Kafka Streams uses tumbling and hopping windows for time-based data aggregation.

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

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

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

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

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

The retry pattern helps systems recover from temporary failures but can cause overload if misused.

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

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

Fail-stop means a system stops and stays down, while fail-recover means it can come back but needs to be ready.

Fallbacks allow systems to handle failures safely and maintain user experience.

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

Jitter helps spread out client retries to avoid traffic spikes.

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

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

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

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

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

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

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

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

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

Kafka Streams uses local state for quick data access and changelogs for recovery.

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

Apache Kafka is a system for managing large volumes of events efficiently.

Timeouts in distributed systems indicate suspicion, not confirmed failure.