The autosuggest items must be indexed using a document field whose “mapping” has been declared to be a “completion” type. Also, they automatically filter out duplicate results. I will show in later sections how autosuggest can be implemented using standard queries, but you should always consider using a Completion Suggester first as they are faster than standard queries (at least in theory). The Elasticsearch API supports quite complex general searches, but also has specific support for autosuggest in the form of its “ Completion Suggesters“. Like all software that uses Elasticsearch, an autosuggest system needs to interact with Elasticsearch via the Elasticsearch API. For an introduction to these, it is hard to beat the “ Getting Started” chapter of the online “ Elasticsearch Definitive Guide“. While hopefully not essential, it will help if the reader already has some familiarity with Elasticsearch, in particular the concepts of “index”, “index mapping”, and some of the basics of the query DSL. In what follows, I describe several different ways to use Elasticsearch to support autosuggest, starting with simple “completion suggesters” through to more sophisticated queries. All this makes it possible to use Elasticsearch as part of an autosuggest system. It is built on top of Apache Lucene and so it supports a nice range of natural language text analysis options and support for geo-spatial features. For example, would you like “Repetitive Strain Injury” to be a suggestion for “ strai“Įlasticsearch is a document store designed to support fast searches.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |