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

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

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

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

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

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

Elasticsearch uses shards for data distribution and replicas for redundancy.

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

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

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

Redis is a fast in-memory data store used for caching and low-latency applications.

Kafka uses retry topics and delayed retries to manage message failures efficiently.

Choosing the right distributed lock depends on your system's needs and existing tools.

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

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

Distributed caching spreads data across multiple servers for better performance and reliability.

Classifying failures and using controlled retries in Kafka prevents outages.

Cascading failures occur when one service's failure impacts others, causing widespread issues.

You can update Elasticsearch indices without downtime using alias switching.

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

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

Cache breakdowns can overwhelm databases, but request coalescing solves this issue.

Timeouts in distributed systems indicate suspicion, not confirmed failure.

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

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

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

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