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

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

Poison messages in Kafka can cause infinite retries, risking system stability.

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

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

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

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

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

Kafka consumers can use multiple threads to speed up message processing, but it complicates ordering and offset management.

Kafka allows safe message reprocessing by resetting consumer positions without altering data.

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

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.

Kafka uses partitions to allow multiple consumers to process messages in parallel.

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

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

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

Kafka's `seek()` lets you replay only specific messages, saving time.

Backpressure helps manage message flow in Kafka during traffic spikes.

Classifying failures and using controlled retries in Kafka prevents outages.

Kafka commit strategies determine how offsets are managed during message processing.

Kafka maintains message order within partitions using keys for routing.

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

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

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

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

Kafka consumers have three important states: Position, Committed Offset, and Application State.

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

Kafka uses partitions to allow parallel processing while ensuring message order with keys.

Kafka's commitSync waits for confirmation, while commitAsync continues immediately.

Kafka uses heartbeats and timeouts to check if consumers are alive.

Graceful shutdown in Kafka ensures no data loss during consumer termination.

Kafka consumers can fetch data in batches to improve throughput and reduce latency.

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

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

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

Kafka's parallel processing depends on partitions, not just consumers or threads.

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

Kafka consumer offsets help track message processing to avoid loss and duplication.

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

Kafka consumer lag shows how far behind consumers are from new messages.