This category includes 2 types of suggesters: completion and context suggester. _index_prefix additional field: r, re, rea, real, “real “, real p, real pa, real pan, real pand, real panda, “real panda “, real panda b, real panda bl, real panda blo, real panda blog, p, pa, pan, pand, panda, “panda “, panda b, panda bl, panda blo, panda blog, “panda blog “, b, bl, blo, blog, “blog “ _2gram additional field: real panda, panda blog Both of them generate the same set of tokens. _index_prefix where it puts generated tokens. The only difference between Edge NGram token filter and index_prefixes parameter is that the latter creates an additional field. _index_prefix additional field: r, re, rea, real, p, pa, pan, pand, panda, b, bl, blo, blog token’s offset & position doesn’t matter (they’re the same for tokens of the same term).r, re, rea, real, p, pa, pan, pand, panda, b, bl, blo, blog.To understand how the data is treated and what tokens are generated I’ll be using Analyze API.
The text example will be “Real Panda blog”. Here I’ll show concrete examples of how an input data is treated by Elasticsearch and stored in indices for each method. The downside of it is a blown-up index which now should store much more data. When you send a prefix in a query, the index already contains all possible prefixes and/or word combinations, and therefore it can find proper documents containing this prefix much faster. The methods listed in this category prepare the index for autocompletion searches in advance. Applicable for simple queries and small indices. Not fast, not optimized, not recommended. In both cases Elasticsearch needs to check all documents for having a prefix (or a phrase prefix) sent in a query. Link to the documentation Match phrase prefix I won’t be copy-pasting the official documentation, instead I’ll provide links to the detailed description of each method. In this post I’d like to focus on different ways of dealing with autocompletion searches (search-as-you-type) and how they differ from each other.
#Context suggester elasticsearch how to
In the previous post we discussed how the generated data should be stored in DB and how to be passed to Elasticsearch for further search queries.
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