This way, we minimize disk fetch and transport overhead: GET /movies/_searchĪbove query returns both the documents - 10, as both the documents contain "Goblet of Fire" as one of the suggestions for title. If we are only interested in the suggestion text, we can use _source option and set it to "suggest". Since version 5, _search endpoint itself has been updated to support suggesters too.īy default, Elasticsearch returns entire matching document. Many examples on the internet use _suggest. Before ES version 5.0, there was a separate endpoint - _suggest for suggesters. Suggester fields are queried using suggest clause inside the request body of _search endpoint. It can be specified per input as shown in the first document(1001) above, or can be kept same for all the inputs as shown in the second document(1002). The weight parameter controls the ranking of documents in search results. We can specify multiple matches for a single document using input parameter. "input": [ "Harry Potter and the Goblet of Fire", This can be a slow, resource intensive process. While indexing a document, we specify input and an optional weight parameter - POST /movies/_doc/ 1001 Suggesters in Lucene are built in-memory by loading the completion values from the index, then building the FST. Sometimes the requirements are just prefix completion or infix. There are multiple ways to implement the autocomplete feature which broadly fall into four main categories: Index time Query time Completion suggester Search-as-you-type database 1. If we try to do so ES throws an error saying ' Can't process field, Analysis requests are only supported on tokenized fields'. Various approaches for autocomplete in Elasticsearch / search as you type. Also, we can't test our mappings using _analyze endpoint in this approach. After analysis, tokens are not available separately - they are put together and inserted into FST, based on their order in the input text. Analyzers on completion types behave differently than analyzers on other text fields. Analyzer value defaults to simple analyzer which lower-cases the input and tokenizes on any non-letter character such as number, space, a hyphen, etc. Mapping also supports analyzer, search analyzer, max_input_length parameters for the completion field. Like edge-n-gram and search_as_you_type, this also does most of the work at index time by updating in-memory FSTs with the input that we provide.Ī special type of ES type - completion, is used for implementing it - PUT /movies These data structures are stored in-memory on nodes to enable faster searches. In this part, we will talk about completion suggester - a type of suggester which is optimized for auto-complete functionality and considered to be faster than the approaches we have discussed so far.Ĭompletion suggesters use a data structure known as Finite State Transducer which is similar to the Trie data structure and is optimized for faster look-ups.
#COMPLETION SUGGESTER ELASTICSEARCH SERIES#
This is part III of my series on designing auto-complete feature in Elasticsearch.
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