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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.

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

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

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

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

Cache penetration occurs when requests for non-existent data overload the database.

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

Kafka uses retention to keep historical data and compaction to keep the latest state.

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

API pagination helps manage large datasets by breaking them into smaller parts.

Availability is about access; reliability is about correct performance.

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

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

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

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

The Bulkhead Pattern isolates resources to protect critical workloads from failures.

The Cache-Aside Pattern speeds up data retrieval by using a cache to store frequently accessed data.