Elasticsearch explained
How Elasticsearch powers fast full-text search: documents and indices, the inverted index, analyzers and tokenisation, mappings, match and bool queries, relevance scoring with BM25, aggregations, shards and replicas, keeping it in sync with your database, and when to use it.
CodexBash, , 6 min read
Searching a product catalogue for "wireless keyboard" with SQL's LIKE '%wireless keyboard%' is slow on large tables, misses "Keyboard (Wireless)", cannot handle typos, and has no idea which result is most relevant. Elasticsearch is a distributed search and analytics engine built for exactly this: fast full-text search with relevance ranking, typo tolerance, filters and facets, over millions or billions of documents. It also powers log analytics in the ELK stack. (OpenSearch is an open-source fork with a very similar API.) This article explains how it works and how to use it well.
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