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In this elastic search tutorial, we discuss about phrase matching in Elasticsearch. This is part of Query DSL (Domain Specific Language). Fuzziness is based on Levenshtein Edit Distance.
00:00 - What is fuzziness with example
02:39 - Examples of fuzzy queries
04:30 - Allowed values for fuzziness
Playlist Link: https://www.youtube.com/watch?v=lnEzmQHa6Co&list=PLa6iDxjj_9qVaf5CsXWP-GAgZoVwKowjx&ab_channel=Codetuber
#coding #theory #computerscience #elasticsearch #clusters #distributedSystems #tutorial #logstash #kibana #beats #aws #dataScience #queryDSL #fuzziness #levenshtein #editDistance
Returns documents that contain terms similar to the search term, as measured by a Levenshtein edit distance.
An edit distance is the number of one-character changes needed to turn one term into another. These changes can include:
Changing a character (box → fox)
Removing a character (black → lack)
Inserting a character (sic → sick)
Transposing two adjacent characters (act → cat)
To find similar terms, the fuzzy query creates a set of all possible variations, or expansions, of the search term within a specified edit distance. The query then returns exact matches for each expansion
(This tutorial is part of a series of tutorials on Elasticsearch, logstash and Kibana. It uses docker for purpose of installation, and may even use aws in the future.)