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

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

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

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

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

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

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

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