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

Log replication ensures data consistency by requiring majority acknowledgment before committing changes.

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.

Read replicas allow databases to handle more read requests by distributing them across multiple copies.

Elasticsearch uses master election and quorum to prevent split brain scenarios.

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

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

Failover ensures systems remain operational by managing leader changes during failures.

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

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

Fail-stop means a system stops and stays down, while fail-recover means it can come back but needs to be ready.

Read-after-write consistency ensures users see their latest updates immediately.

Kafka Partition Reassignment moves replicas between brokers while keeping data safe.

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.

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

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

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

Kafka's static membership allows consumers to maintain stable identities, reducing unnecessary rebalances.

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

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

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

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

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

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

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

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