WARNING: The 2.x versions of Elasticsearch have passed their EOL dates. If you are running a 2.x version, we strongly advise you to upgrade.
This documentation is no longer maintained and may be removed. For the latest information, see the current Elasticsearch documentation.
Fuzzy Queryedit
The fuzzy query is the fuzzy equivalent of
the term query. You will seldom use it directly yourself, but understanding
how it works will help you to use fuzziness in the higher-level match query.
To understand how it works, we will first index some documents:
POST /my_index/my_type/_bulk
{ "index": { "_id": 1 }}
{ "text": "Surprise me!"}
{ "index": { "_id": 2 }}
{ "text": "That was surprising."}
{ "index": { "_id": 3 }}
{ "text": "I wasn't surprised."}
Now we can run a fuzzy query for the term surprize:
GET /my_index/my_type/_search
{
"query": {
"fuzzy": {
"text": "surprize"
}
}
}
The fuzzy query is a term-level query, so it doesn’t do any analysis. It
takes a single term and finds all terms in the term dictionary that are
within the specified fuzziness. The default fuzziness is AUTO.
In our example, surprize is within an edit distance of 2 from both
surprise and surprised, so documents 1 and 3 match. We could reduce the
matches to just surprise with the following query:
GET /my_index/my_type/_search
{
"query": {
"fuzzy": {
"text": {
"value": "surprize",
"fuzziness": 1
}
}
}
}
Improving Performanceedit
The fuzzy query works by taking the original term and building a
Levenshtein automaton—like a big graph representing all the strings
that are within the specified edit distance of the original string.
The fuzzy query then uses the automaton to step efficiently through all of the terms in the term dictionary to see if they match. Once it has collected all of the matching terms that exist in the term dictionary, it can compute the list of matching documents.
Of course, depending on the type of data stored in the index, a fuzzy query with an edit distance of 2 can match a very large number of terms and perform very badly. Two parameters can be used to limit the performance impact:
-
prefix_length -
The number of initial characters that will not be “fuzzified.” Most
spelling errors occur toward the end of the word, not toward the beginning.
By using a
prefix_lengthof3, for example, you can signficantly reduce the number of matching terms. -
max_expansions -
If a fuzzy query expands to three or four fuzzy options, the new options may be
meaningful. If it produces 1,000 options, they are essentially
meaningless. Use
max_expansionsto limit the total number of options that will be produced. The fuzzy query will collect matching terms until it runs out of terms or reaches themax_expansionslimit.