💻 IT & Coding concepts,
clear in 60 seconds.

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

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

Database indexing speeds up data retrieval by allowing quick lookups.

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

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

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

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

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

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

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

Elasticsearch ranks search results using BM25 based on relevance scoring.

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

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

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