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

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

Choose Elasticsearch for complex text searches, but use databases for structured queries.

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

Caching helps applications respond faster by serving data from memory instead of hitting the database.

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

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

Connection pooling reuses database connections but can cause issues if misconfigured.

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

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

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

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