A fuzzy based query that uses similarity based on Levenshtein (edit distance) algorithm.
Warning: this query is not very scalable with its default prefix length of 0 – in this case, every term will be enumerated and cause an edit score calculation or max_expansions is not set.
Here is a simple example:
{
"fuzzy" : { "user" : "ki" }
}
More complex settings can be set (the values here are the default values):
{
"fuzzy" : {
"user" : {
"value" : "ki",
"boost" : 1.0,
"min_similarity" : 0.5,
"prefix_length" : 0
}
}
}
The max_expansions parameter (unbounded by default) controls the number of terms the fuzzy query will expand to.
Numeric / Date Fuzzy
fuzzy query on a numeric field will result in a range query “around” the value using the min_similarity value. For example:
{
"fuzzy" : {
"price" : {
"value" : 12,
"min_similarity" : 2
}
}
}
Will result in a range query between 10 and 14. Same applies to dates, with support for time format for the min_similarity field:
{
"fuzzy" : {
"created" : {
"value" : "2010-02-05T12:05:07",
"min_similarity" : "1d"
}
}
}
In the mapping, numeric and date types now allow to configure a fuzzy_factor mapping value (defaults to 1), which will be used to multiply the fuzzy value by it when used in a query_string type query. For example, for dates, a fuzzy factor of “1d” will result in multiplying whatever fuzzy value provided in the min_similarity by it. Note, this is explicitly supported since query_string query only allowed for similarity valued between 0.0 and 1.0.
