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Search engines collect, index, retrieve, and rank documents to provide relevant results.

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

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

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

Elasticsearch fuzzy search allows finding results even with typos in queries.

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

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

Filters check conditions without affecting relevance, while queries do.

A document in Elasticsearch isn't searchable right after it's saved due to the refresh process.

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

The number of Elasticsearch nodes depends on storage, workload, and performance needs.

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

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