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Database replication helps keep data available and allows systems to handle more read requests.

Synchronous replication waits for confirmation from replicas, while asynchronous allows faster writes without waiting.

Database replication enhances data availability and read performance but has challenges like replication lag.

Consistency models determine what data reads in distributed systems can see.

Kafka uses replication and in-sync replicas to ensure data is always available.

Stateless servers are easier to scale because they don't store user session data locally.

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

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

Distributed locks ensure only one server performs a task, avoiding duplication.

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

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

The dual-write trap in Elasticsearch can cause data inconsistency during reindexing.

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

Elasticsearch CCR allows real-time data replication for faster disaster recovery.