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

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

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

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

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

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

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

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

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.

Backpressure helps manage message flow in Kafka during traffic spikes.

Classifying failures and using controlled retries in Kafka prevents outages.

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

The Bulkhead Pattern isolates resources to protect critical workloads from failures.

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

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

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

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

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