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

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

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

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

B-Trees help databases find data quickly without scanning every row.

Transactions ensure database operations are reliable using ACID properties.

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

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

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.

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

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

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

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

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

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.