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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.

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

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

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

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

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

Classifying failures and using controlled retries in Kafka prevents outages.

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

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

Tokenization and analyzers help search engines process and match text effectively.

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

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