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Elasticsearch

In shortAn open-source search and analytics engine for indexing large amounts of text, with fuzzy, phonetic and vector search built in.

Updated 24 Oct 2026· 2 min read

Elasticsearch is a search and analytics engine built on the Apache Lucene library. You load documents into an index, and it lets you search and aggregate them very quickly, even across millions of records. Many site searches, product catalogues and log-analysis stacks run on it. In MLforSEO’s fuzzy matching material it appears as one of the tools for approximate matching at scale.

How it works

When you index a document, Elasticsearch runs the text through an analyser that splits it into tokens (see tokenisation), lowercases them and optionally removes stop words or reduces words to their stems. It stores the result in an inverted index: a lookup from each term to the documents that contain it. Queries are analysed the same way and matched against that index, then scored for relevance (BM25 by default).

Several features matter for matching messy text:

  • Fuzzy queries find terms within a Levenshtein edit distance of up to two, with fuzziness set to AUTO so short words allow fewer edits.
  • Phonetic analysis (a plugin) indexes sound-alike codes such as Metaphone, so “Smith” and “Smyth” match. See phonetic matching.
  • N-gram and edge n-gram tokenisers support partial and autocomplete-style matching.
  • Vector search stores embeddings and finds nearest neighbours, which enables semantic search alongside keyword search.

How it’s used in SEO and marketing

  • Product name standardisation: matching “iPhone 15 Pro 128 GB” and “Apple iPhone15 Pro (128GB)” across feeds.
  • Matching internal link anchor text to page titles at site scale.
  • Powering on-site search and analysing what visitors search for.
  • Server log analysis for crawl budget work, often as part of the Elastic Stack with Kibana dashboards.

Example

You have 40,000 supplier product names and a master catalogue. Index the catalogue with a standard analyser plus edge n-grams, then run each supplier name as a fuzzy match query. Exact and near-exact names match automatically; anything with a low relevance score goes to a review list. This is the same idea as the Python fuzzy matching notebooks, moved into a search engine that can handle much larger volumes.

Limitations and things to watch

  • It is infrastructure: you need to host it (or pay for a managed service) and maintain mappings and analysers.
  • Fuzzy matching is capped at two edits per term and does not understand meaning; use vectors or a language model for synonyms.
  • Relevance scores are relative to a query, so they are not a fixed similarity percentage you can threshold across queries.
  • For a one-off spreadsheet task, RapidFuzz or a Google Sheets template is usually faster to set up.

Learn it on MLforSEO

Where Elasticsearch shows up on MLforSEO

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