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

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

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

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

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

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

Primary shards partition data, while replica shards duplicate it for redundancy.

Replicas keep your system available, while snapshots allow for data recovery.

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

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

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

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

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

Database per Service ensures each microservice owns its data, reducing dependencies.

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

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

Scaling databases involves deciding whether to enhance read or write capabilities based on traffic patterns.

Elasticsearch uses shards for data distribution and replicas for redundancy.

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

Choosing between a shared database and a database per service affects system design significantly.

Quorum Reads ensure data consistency by querying multiple replicas in distributed systems.

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

Sharding splits databases into smaller parts to improve performance and scalability.

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

Database bottlenecks slow down performance, but identifying them helps optimize and scale effectively.

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

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

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

Database isolation levels determine how transactions interact and affect data consistency.

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

2-Phase Commit helps maintain data consistency in transactions across multiple databases.

Write-Ahead Log ensures data is safely recorded before changes are made.

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

The choice between SQL and NoSQL databases depends on your application's specific needs.