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

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

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

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

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

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.

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

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

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

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

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

Elasticsearch offers two pagination methods: from & size for shallow pages and search_after for deep pages.

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

Elasticsearch distributes search queries across shards to find and retrieve results quickly.

An inverted index helps search engines quickly find documents containing specific terms.

Filters check conditions without affecting relevance, while queries do.