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

Optimizing Elasticsearch involves using filters, caching, and measuring performance.

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

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

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.

Deep pagination in Elasticsearch can slow down searches significantly, but there are efficient methods to handle it.

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.

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.