I am trying to match dashes (and other symbols) in my elastic query.
It is fuzzysearch on all the fields using default whitespace analyzer.
My query:
function_score: {
query: {
multi_match: {
query: string
analyzer: "whitespace",
fuzziness: 1
}
}
}
However this has unexpected results with dash characters. E.x. Central-Park doesnt work with this. Or
Dashes only work well when I use a phrase match and strip out the double quotes. But there is no fuzziness.
Does anyone know how I can get the fuzzysearch normally with dashes please?
Adding a working example with index mapping, index data, search query, and search result
Index Mapping:
{
"mappings": {
"properties": {
"place": {
"type": "text",
"analyzer":"whitespace"
}
}
}
}
Index Data:
{
"place": "Cwntral-Park"
}
{
"place": "Central-Park"
}
{
"place": "Central-Area"
}
Search Query:
{
"query": {
"bool": {
"should": {
"match": {
"place": {
"query": "Central-Park",
"fuzziness": 1
}
}
}
}
}
}
Search Result:
"hits": [
{
"_index": "65605120",
"_type": "_doc",
"_id": "1",
"_score": 0.9808291,
"_source": {
"place": "Central-Park"
}
},
{
"_index": "65605120",
"_type": "_doc",
"_id": "3",
"_score": 0.8990934,
"_source": {
"place": "Cwntral-Park"
}
}
]
Related
I have a pattern ".TP-V." which returns strings like "SSTP-VPN". But the pattern ".SSH." Does not return anything, although there are lines like "core:Login:SSH:Cisco". I have no idea what pattern is need.
You need to use ".*SSH.*" instead of ".SSH.".
Adding a working example -
Index Data:
{
"name":"core:Login:SSH:Cisco"
}
{
"name":"SSTP-VPN"
}
Search Query:
{
"query": {
"regexp": {
"name.keyword": {
"value": ".*SSH.*"
}
}
}
}
Search Result:
"hits": [
{
"_index": "68015371",
"_type": "_doc",
"_id": "2",
"_score": 1.0,
"_source": {
"name": "core:Login:SSH:Cisco"
}
}
]
Search Query:
{
"query": {
"regexp": {
"name.keyword": {
"value": ".*TP-V.*"
}
}
}
}
ı want to find a field, Which field did find the search query?
this can be any query I am not writing a specific query
for example
ı searching dilo abinin phrase or any word, and found bellow document
{
"name":"dilo abinin",
"surname: "sürücü"
}
ı want to get name keyword
You can use highlighting, to see which field matched your query
Index API
{
"name":"dilo abinin",
"surname": "sürücü"
}
Search Query:
{
"query": {
"query_string": {
"query": "dilo abinin"
}
},
"highlight": {
"fields": {
"*": {}
}
}
}
Search Result:
"hits": [
{
"_index": "65325154",
"_type": "_doc",
"_id": "1",
"_score": 0.5753642,
"_source": {
"name": "dilo abinin",
"surname": "sürücü"
},
"highlight": {
"name": [ // note this
"<em>dilo</em> <em>abinin</em>"
],
"name.keyword": [
"<em>dilo abinin</em>"
]
}
}
]
I have a search index of filenames containing over 100,000 entries that share about 500 unique variations of the main filename field. I have recently made some modifications to certain filename values that are being generated from my data. I was wondering if there is a way to link certain queries to return an exact match. In the following query:
"query": {
"bool": {
"must": [
{
"match": {
"filename": "foo-bar"
}
}
],
}
}
how would it be possible to modify the index and associate the results so that above query will also match results foo-bar-baz, but not foo-bar-foo or any other variation?
Thanks in advance for your help
You can use a term query instead of a match query. Perfect to use on a keyword:
https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-term-query.html
Adding a working example with index data and search query. (Using the default mapping)
Index Data:
{
"fileName": "foo-bar"
}
{
"fileName": "foo-bar-baz"
}
{
"fileName": "foo-bar-foo"
}
Search Query:
{
"query": {
"bool": {
"should": [
{
"match": {
"fileName.keyword": "foo-bar"
}
},
{
"match": {
"fileName.keyword": "foo-bar-baz"
}
}
]
}
}
}
Search Result:
"hits": [
{
"_index": "test",
"_type": "_doc",
"_id": "1",
"_score": 0.9808291,
"_source": {
"fileName": "foo-bar"
}
},
{
"_index": "test",
"_type": "_doc",
"_id": "2",
"_score": 0.9808291,
"_source": {
"fileName": "foo-bar-baz"
}
}
]
I have a fuzzy query with customized AUTO:10,20 fuzziness value.
{
"query": {
"match": {
"name": {
"query": "nike",
"fuzziness": "AUTO:10,20"
}
}
}
}
How to convert it to a query_string query? I tried nike~AUTO:10,20 but it is not working.
It's possible with query_strng as well, let me show using the same example as OP provided, both match_query provided by OP matches and query_string fetches the same document with same score.
And according to this and this ES docs, Elasticsearch supports AUTO:10,20 format, which is shown in my example as well.
Also
Index mapping
{
"mappings": {
"properties": {
"name": {
"type": "text"
}
}
}
}
Index some doc
{
"name" : "nike"
}
Search query using match with fuzziness
{
"query": {
"match": {
"name": {
"query": "nike",
"fuzziness": "AUTO:10,20"
}
}
}
}
And result
"hits": [
{
"_index": "so-query",
"_type": "_doc",
"_id": "1",
"_score": 0.9808292,
"_source": {
"name": "nike"
}
}
]
Query_string with fuzziness
{
"query": {
"query_string": {
"fields": ["name"],
"query": "nike",
"fuzziness": "AUTO:10,20"
}
}
}
And result
"hits": [
{
"_index": "so-query",
"_type": "_doc",
"_id": "1",
"_score": 0.9808292,
"_source": {
"name": "nike"
}
}
]
Lucene syntax only allows you to specify "fuzziness" with the tilde symbol "~", optionally followed by 0, 1 or 2 to indicate the edit distance.
Elasticsearch Query DSL supports a configurable special value for AUTO which then is used to build the proper Lucene query.
You would need to implement that logic on your application side, by evaluating the desired edit distance based on the length of your search term and then use <searchTerm>~<editDistance> in your query_string-query.
I have a query in which i want to add a must_not clause that would discard all records that have blank data for a some field. I tried a lot of ways but none worked. when I issue the same query (mentioned below) with other specific fields then it works fine.
this query should get all records that do not have "registrationType1" field empty/blank
query:
{
"size": 20,
"_source": [
"registrationType1"
],
"query": {
"bool": {
"must_not": [
{
"term": {
"registrationType1": ""
}
}
]
}
}
}
the results below still contains "registrationType1" with empty values
results:
**"_source": {
"registrationType1": ""}}
, * {
"_index": "oh_animal",
"_type": "animals",
"_id": "3842002",
"_score": 1,
"_source": {
"registrationType1": "A&R"}}
, * {
"_index": "oh_animal",
"_type": "animals",
"_id": "3842033",
"_score": 1,
"_source": {
"registrationType1": "AMHA"}}
, * {
"_index": "oh_animal",
"_type": "animals",
"_id": "3842213",
"_score": 1,
"_source": {
"registrationType1": "AMHA"}}
, * {
"_index": "oh_animal",
"_type": "animals",
"_id": "3842963",
"_score": 1,
"_source": {
"registrationType1": ""}}
, * {
"_index": "oh_animal",
"_type": "animals",
"_id": "3869063",
"_score": 1,
"_source": {
"registrationType1": ""}}**
PFB mappings for the field above
"registrationType1": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword"
}
}
}
You need to use the keyword subfield in order to do this:
{
"size": 20,
"_source": [
"registrationType1"
],
"query": {
"bool": {
"must_not": [
{
"term": {
"registrationType1.keyword": "" <-- change this
}
}
]
}
}
}
If you do not specify any text value on the text fields, there is basically nothing to analyze and return the documents accordingly.
In similar way, if you remove must_not and replace it with must, it would show empty results.
What you can do is, looking at your mapping, query must_not on keyword field. Keyword fields won't be analysed and in that way your query would return the results as you expect.
Query
POST myemptyindex/_search
{
"query": {
"bool": {
"must_not": [
{
"term": {
"registrationType1.keyword": ""
}
}
]
}
}
}
Hope this helps!
I am using elasticsearch version 7.2,
I replicated your data and ingested in my elastic index,and tried querying with and without .keyword.
I am getting the desired result when using the ".keyword" in the field name.It is not returning the docs which have registrationType1="".
Note - The query does not works when not using the ".keyword"
I have added my sample code below, have a look if that helps.
from elasticsearch import Elasticsearch
es = Elasticsearch()
es.indices.create(index="test", ignore=400, body={
"mappings": {
"_doc": {
"properties": {
"registrationType1": {
"type": "text",
"field": {
"keyword": {
"type": "keyword"
}
}
}
}
}
}
})
data = {
"registrationType1": ""
}
es.index(index="test",doc_type="_doc",body=data,id=1)
search = es.search(index="test", body={
"size": 20,
"_source": [
"registrationType1"
],
"query": {
"bool": {
"must_not": [
{
"term": {
"registrationType1.keyword": ""
}
}
]
}
}
})
print(search)
Executing the above should not return any results as we are inserting empty for the field
There was some issue with the mappings itself, I deleted the index and re-indexed it with new mappings and its working now.