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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.

Filters check conditions without affecting relevance, while queries do.

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

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

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

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

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

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

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

Elasticsearch ranks search results using BM25 based on relevance scoring.

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.

Elasticsearch aggregations help summarize data using metrics for calculations and buckets for grouping.

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

You can update Elasticsearch indices without downtime using alias switching.

Elasticsearch aggregations help analyze data by grouping, calculating, and estimating values.