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

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

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

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

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

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

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.

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

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

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

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

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