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Elasticsearch documents aren't searchable immediately due to the indexing process.

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

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

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

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

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

Elasticsearch uses analyzers and synonyms to improve search accuracy by connecting similar words.

Elasticsearch offers three main autocomplete strategies, each with unique benefits and trade-offs.

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

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

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

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

You can update Elasticsearch indices without downtime using alias switching.

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

Elasticsearch uses distributed architecture to search data quickly across multiple servers.

Filters check conditions without affecting relevance, while queries do.

Elasticsearch ranks search results using BM25 based on relevance scoring.

Stemming and normalization help search engines match various word forms for better results.