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

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

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

Kafka's subscribe() lets the group manage partitions, while assign() gives control to the application.

Kafka's assign() method allows manual control of partition assignments for consumers.

Elasticsearch ILM automates index management to reduce storage costs and meet data needs.

Optimistic and pessimistic locking are strategies to manage database access during concurrent transactions.

Kafka uses different strategies to assign partitions to consumers in a group efficiently.

Kafka offset reset determines where a consumer starts reading messages without a valid offset.

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

matchIndex tracks what each follower has, while commitIndex shows what is committed.

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

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

Eager rebalancing revokes all assignments, while cooperative allows incremental changes.

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

Kafka rebalance can lead to lost or duplicated work if not handled carefully.

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

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

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