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

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

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

Filters check conditions without affecting relevance, while queries do.

Database indexing speeds up data retrieval by allowing quick lookups.

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.

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

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

Tokenization and analyzers help search engines process and match text effectively.

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

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

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

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

Composite indexes speed up database queries that filter or sort by multiple columns.

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

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.

Partition pruning helps databases skip unnecessary data scans, speeding up queries.

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

Elasticsearch ranks search results using BM25 based on relevance scoring.

You can update Elasticsearch indices without downtime using alias switching.

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

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

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