Elasticsearch Query change display results according to the scoring
The current Query gives the result of the Field title in the following order.
Quick 123
Foxes Quick
Quick
Foxes Quick Quick
Quick Foxes
Shouldn't
3. Quick be coming as a first result instead?
Also , Foxes Quick Quick has two occurances of Quick, it should have some preference in the Queried result . But it is coming at 4th poistion .
Index Settings .
{
"fundraisers": {
"settings": {
"index": {
"number_of_shards": "5",
"provided_name": "fundraisers",
"creation_date": "1546515635025",
"analysis": {
"analyzer": {
"my_analyzer": {
"filter": [
"lowercase"
],
"tokenizer": "my_tokenizer"
},
"search_analyzer_search": {
"filter": [
"lowercase"
],
"tokenizer": "search_tokenizer_search"
}
},
"tokenizer": {
"my_tokenizer": {
"token_chars": [
"letter",
"digit"
],
"min_gram": "3",
"type": "edge_ngram",
"max_gram": "50"
},
"search_tokenizer_search": {
"token_chars": [
"letter",
"digit",
"whitespace"
],
"min_gram": "3",
"type": "ngram",
"max_gram": "50"
}
}
},
"number_of_replicas": "1",
"uuid": "mVweO4_sT3Ww00MzdLyavw",
"version": {
"created": "6020399"
}
}
}
}
}
Query
GET fundraisers/_search?explain=true
{
"query": {
"match_phrase": {
"title": {
"query": "qui",
"analyzer": "my_analyzer"
}
}
}
}
Mapping
{
"fundraisers": {
"mappings": {
"fundraisers": {
"properties": {
"status": {
"type": "text"
},
"suggest": {
"type": "completion",
"analyzer": "simple",
"preserve_separators": true,
"preserve_position_increments": true,
"max_input_length": 50
},
"title": {
"type": "text",
"analyzer": "my_analyzer"
},
"twitterUrl": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"videoLinks": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"zipCode": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
}
}
}
}
}
}
Am I complicating this too much by using match_phrase,search analyzer and ngrams or is there any simpler way to achieve the expected result ?
Ref:
https://www.elastic.co/guide/en/elasticsearch/reference/6.5/query-dsl-match-query.html
Ok, first let's create a minimal and reproducible setup:
PUT test
{
"settings": {
"index": {
"number_of_shards": "1",
"number_of_replicas": "1",
"analysis": {
"analyzer": {
"my_analyzer": {
"filter": [
"lowercase"
],
"tokenizer": "my_tokenizer"
},
"search_analyzer_search": {
"filter": [
"lowercase"
],
"tokenizer": "search_tokenizer_search"
}
},
"tokenizer": {
"my_tokenizer": {
"token_chars": [
"letter",
"digit"
],
"min_gram": "3",
"type": "edge_ngram",
"max_gram": "50"
},
"search_tokenizer_search": {
"token_chars": [
"letter",
"digit",
"whitespace"
],
"min_gram": "3",
"type": "ngram",
"max_gram": "50"
}
}
}
}
},
"mappings": {
"_doc": {
"properties": {
"title": {
"type": "text",
"analyzer": "my_analyzer"
}
}
}
}
}
PUT test/_doc/1
{
"title": "Quick 123"
}
PUT test/_doc/2
{
"title": "Foxes Quick"
}
PUT test/_doc/3
{
"title": "Quick"
}
PUT test/_doc/4
{
"title": "Foxes Quick Quick"
}
PUT test/_doc/5
{
"title": "Quick Foxes"
}
Then let's try the simplest query:
GET test/_search
{
"query": {
"match": {
"title": {
"query": "qui"
}
}
}
}
And now your order is:
Quick
Foxes Quick Quick
Quick 123
Foxes Quick
Quick Foxes
That's pretty much what you were expecting, right? There might be other usecases, which are not covered by this query, but IMO you'll have to use multi_match and search on different analyzers, because I'm not sure a phrase_search on an edgegram makes much sense.
Related
Currently, I am using Ngram tokenizer to-do partial matching of Employees.
I can match on FullName, Email address and Employee Number
My current setup looks as follow:
"tokenizer": {
"my_tokenizer": {
"type": "ngram",
"min_gram": 3,
"max_gram": 3,
"token_chars": [
"letter",
"digit"
]
}
}
The problem that I am facing is that Employee Number can be 1 character long and because of the min_gram and max_gram, I can never match. I can't make the min_gram 1 either because the results do not look correct.
So I tried to mix the Ngram with a standard tokenizer and instead of doing in Multimatch search I am doing an simple_query_string.
This seems to also work partially.
My question is how can I partially match on all 3 fields bearing in mind that employee number can be 1 or 2 chars long. And exact match if I use semi quotes around a word or number
In the below example how can search for 11 and return documents 4 and 5?
Also, I would like document 2 to return if I had to search for 706 which is a partial match, but if I had to search with "7061" I would only return document 2
Full Code
PUT index
{
"settings": {
"analysis": {
"analyzer": {
"english_exact": {
"tokenizer": "standard",
"filter": [
"lowercase"
]
},
"my_analyzer": {
"filter": [
"lowercase",
"asciifolding"
],
"tokenizer": "my_tokenizer"
}
},
"tokenizer": {
"my_tokenizer": {
"type": "ngram",
"min_gram": 3,
"max_gram": 3,
"token_chars": [
"letter",
"digit"
]
}
},
"normalizer": {
"lowersort": {
"type": "custom",
"filter": [
"lowercase"
]
}
}
}
},
"mappings": {
"properties": {
"number": {
"type": "text",
"analyzer": "english",
"fields": {
"exact": {
"type": "text",
"analyzer": "english_exact"
}
}
},
"fullName": {
"type": "text",
"fields": {
"ngram": {
"type": "text",
"analyzer": "my_analyzer"
}
},
"analyzer": "standard"
}
}
}
}
PUT index/_doc/1
{
"number" : 1,
"fullName": "Brenda eaton"
}
PUT index/_doc/2
{
"number" : 7061,
"fullName": "Bruce wayne"
}
PUT index/_doc/3
{
"number" : 23,
"fullName": "Bruce Banner"
}
PUT index/_doc/4
{
"number" : 111,
"fullName": "Cat woman"
}
PUT index/_doc/5
{
"number" : 1112,
"fullName": "0723568521"
}
GET index/_search
{
"query": {
"simple_query_string": {
"fields": [ "fullName.ngram", "number.exact"],
"query": "11"
}
}
}
You need to change the analyzer of the number.exact field and reduce the min_gram
count to 2. Modify the index mapping as shown below
Adding a working example
Index Mapping:
{
"settings": {
"analysis": {
"analyzer": {
"english_exact": {
"tokenizer": "standard",
"filter": [
"lowercase"
]
},
"my_analyzer": {
"filter": [
"lowercase",
"asciifolding"
],
"tokenizer": "my_tokenizer"
}
},
"tokenizer": {
"my_tokenizer": {
"type": "ngram",
"min_gram": 2,
"max_gram": 3,
"token_chars": [
"letter",
"digit"
]
}
},
"normalizer": {
"lowersort": {
"type": "custom",
"filter": [
"lowercase"
]
}
}
}
},
"mappings": {
"properties": {
"number": {
"type": "keyword", // note this
"fields": {
"exact": {
"type": "text",
"analyzer": "my_analyzer"
}
}
},
"fullName": {
"type": "text",
"fields": {
"ngram": {
"type": "text",
"analyzer": "my_analyzer"
}
},
"analyzer": "standard"
}
}
}
}
Search Query:
{
"query": {
"simple_query_string": {
"fields": [ "fullName.ngram", "number.exact"],
"query": "11"
}
}
}
Search Result:
"hits": [
{
"_index": "66311552",
"_type": "_doc",
"_id": "4",
"_score": 0.9929736,
"_source": {
"number": 111,
"fullName": "Cat woman"
}
},
{
"_index": "66311552",
"_type": "_doc",
"_id": "5",
"_score": 0.8505551,
"_source": {
"number": 1112,
"fullName": "0723568521"
}
}
]
Update 1:
If you just need to search for 1, modify the data type of the number field from text type to keyword type, as shown in the index mapping above.
Search Query:
{
"query": {
"simple_query_string": {
"fields": [ "fullName.ngram", "number.exact","number"],
"query": "1"
}
}
}
Search Result will be
"hits": [
{
"_index": "66311552",
"_type": "_doc",
"_id": "1",
"_score": 1.3862942,
"_source": {
"number": 1,
"fullName": "Brenda eaton"
}
}
]
Update 2:
You can use two separate analyzers with n-gram tokenizer for the fullName field and number field. Modify with the below index mapping:
{
"settings": {
"analysis": {
"analyzer": {
"english_exact": {
"tokenizer": "standard",
"filter": [
"lowercase"
]
},
"name_analyzer": {
"filter": [
"lowercase",
"asciifolding"
],
"tokenizer": "name_tokenizer"
},
"number_analyzer": {
"filter": [
"lowercase",
"asciifolding"
],
"tokenizer": "number_tokenizer"
}
},
"tokenizer": {
"name_tokenizer": {
"type": "ngram",
"min_gram": 3,
"max_gram": 3,
"token_chars": [
"letter",
"digit"
]
},
"number_tokenizer": {
"type": "ngram",
"min_gram": 2,
"max_gram": 3,
"token_chars": [
"letter",
"digit"
]
}
},
"normalizer": {
"lowersort": {
"type": "custom",
"filter": [
"lowercase"
]
}
}
}
},
"mappings": {
"properties": {
"number": {
"type": "keyword",
"fields": {
"exact": {
"type": "text",
"analyzer": "number_analyzer"
}
}
},
"fullName": {
"type": "text",
"fields": {
"ngram": {
"type": "text",
"analyzer": "name_analyzer"
}
},
"analyzer": "standard"
}
}
}
}
I am using a ngram analysier on my elastic search index. This is needed for the search capablity I require. I am searching for a document with a name called "l/test_V0001". When I search using "l/test" i am only getting results for "l" the / is working as a escape character and not as a text. I have searched and found this is a common issue and expected but can find no work around.
When i search the API for "l/test_V0001" I can find the result I am after. However when doing the same search via the java API I still only get results for "l".
here is the API search:
{
"query": {
"multi_match": {
"query": "l/test_V0001",
"fields": ["name", "name.partial", "name.text"]
}
}
}
and the mapping for the index:
{
"settings": {
"index": {
"max_ngram_diff": 20,
"search.idle.after": "10m"
},
"analysis": {
"analyzer": {
"ngram3_analyzer": {
"tokenizer": "ngram3_tokenizer",
"filter": [
"lowercase"
]
}
},
"tokenizer": {
"ngram3_tokenizer": {
"type": "ngram",
"min_gram": 3,
"max_gram": 20
}
}
}
},
"mappings": {
"dynamic": "strict",
"properties": {
"name": {
"type": "keyword",
"fields": {
"partial": {
"type": "text",
"analyzer": "ngram3_analyzer",
"search_analyzer": "keyword"
},
"text": {
"type": "text"
}
}
},
"value": {
"type": "integer"
}
}
}
}
any help on this or a work around would be great!
so after a bit of digging I found the answer using custom token chars. This is has added to the index mapping:
"tokenizer": {
"ngram3_tokenizer": {
"type": "ngram",
"min_gram": 3,
"max_gram": 20,
"token_chars": [
"letter",
"digit",
"symbol",
"custom"
],
"custom_token_chars": "/"
}
so my full index now looks like:
{
"settings": {
"index": {
"max_ngram_diff": 20,
"search.idle.after": "10m"
},
"analysis": {
"analyzer": {
"ngram3_analyzer": {
"tokenizer": "ngram3_tokenizer",
"filter": [
"lowercase"
]
}
},
"tokenizer": {
"ngram3_tokenizer": {
"type": "ngram",
"min_gram": 3,
"max_gram": 20,
"token_chars": [
"letter",
"digit",
"symbol",
"custom"
],
"custom_token_chars": "/"
}
}
}
},
"mappings": {
"dynamic": "strict",
"properties": {
"name": {
"type": "keyword",
"fields": {
"partial": {
"type": "text",
"analyzer": "ngram3_analyzer",
"search_analyzer": "keyword"
},
"text": {
"type": "text"
}
}
},
"value": {
"type": "integer"
}
}
}
}
this works for both rest client and java API
I am searching for a phrase in a email body. Need to get the exact data filtered like, if I search for 'Avenue New', it should return only results which has the phrase 'Avenue New' not 'Avenue Street', 'Park Avenue'etc
My mapping is like:
{
"exchangemailssql": {
"aliases": {},
"mappings": {
"email": {
"dynamic_templates": [
{
"_default": {
"match": "*",
"match_mapping_type": "string",
"mapping": {
"doc_values": true,
"type": "keyword"
}
}
}
],
"properties": {
"attachments": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"body": {
"type": "text",
"analyzer": "keylower",
"fielddata": true
},
"count": {
"type": "short"
},
"emailId": {
"type": "long"
}
}
}
},
"settings": {
"index": {
"refresh_interval": "3s",
"number_of_shards": "1",
"provided_name": "exchangemailssql",
"creation_date": "1500527793230",
"analysis": {
"filter": {
"nGram": {
"min_gram": "4",
"side": "front",
"type": "edge_ngram",
"max_gram": "100"
}
},
"analyzer": {
"keylower": {
"filter": [
"lowercase"
],
"type": "custom",
"tokenizer": "keyword"
},
"email": {
"filter": [
"lowercase",
"unique",
"nGram"
],
"type": "custom",
"tokenizer": "uax_url_email"
},
"full": {
"filter": [
"lowercase",
"snowball",
"nGram"
],
"type": "custom",
"tokenizer": "standard"
}
}
},
"number_of_replicas": "0",
"uuid": "2XTpHmwaQF65PNkCQCmcVQ",
"version": {
"created": "5040099"
}
}
}
}
}
I have given the search query like:
{
"query": {
"match_phrase": {
"body": "Avenue New"
}
},
"highlight": {
"fields" : {
"body" : {}
}
}
}
The problem here is that you're tokenizing the full body content using the keyword tokenizer, i.e. it will be one big lowercase string and you cannot search inside of it.
If you simply change the analyzer of your body field to standard instead of keylower, you'll find what you need using the match_phrase query.
"body": {
"type": "text",
"analyzer": "standard", <---change this
"fielddata": true
},
I use ElasticSearch-2.3.5. I want to add my custom analyzer to mapping while index creating.
PUT /library
{
"settings": {
"analysis": {
"tokenizer": {
"ngram_tokenizer": {
"type": "nGram",
"min_gram": "1",
"max_gram": "15",
"token_chars": [
"letter",
"digit"
]
}
},
"analyzer": {
"index_ngram_analyzer": {
"type": "custom",
"tokenizer": "ngram_tokenizer",
"filter": [
"lowercase"
]
}
},
"search_term_analyzer": {
"type": "custom",
"tokenizer": "keyword",
"filter": "lowercase"
}
}
},
"mappings": {
"book": {
"properties": {
"Id": {
"type": "long",
"search_analyzer": "search_term_analyzer",
"index_analyzer": "index_ngram_analyzer",
"term_vector":"with_positions_offsets"
},
"Title": {
"type": "string",
"search_analyzer": "search_term_analyzer",
"index_analyzer": "index_ngram_analyzer",
"term_vector":"with_positions_offsets"
}
}
}
}
}
I take a template example from official guide.
{
"settings" : {
"number_of_shards" : 1
},
"mappings" : {
"type1" : {
"properties" : {
"field1" : { "type" : "string", "index" : "not_analyzed" }
}
}
}
}
But I get an error trying to execute the first part of code. There is my error:
{
"error": {
"root_cause": [
{
"type": "mapper_parsing_exception",
"reason": "analyzer [search_term_analyzer] not found for field [Title]"
}
],
"type": "mapper_parsing_exception",
"reason": "Failed to parse mapping [book]: analyzer [search_term_analyzer] not found for field [Title]",
"caused_by": {
"type": "mapper_parsing_exception",
"reason": "analyzer [search_term_analyzer] not found for field [Title]"
}
},
"status": 400
}
I can do it if I put my mappings inside of settings, but I think that it is wrong way. So I try to find my book by using a part of title. I have the "King Arthur" book for example. My query looks like this:
POST /library/book/_search
{
"query": {
"match": {
"Title": "kin"
}
}
}
Nothing will be found. What I do wrong? Could you help me? It seems my analyzer and tokenizer don't work. How can I get the terms "k", "i", "ki", "king" etc.? Because I think that I have only two terms right now. There are 'king' and 'arthur'.
You have misplaced the search_term_analyzer analyzer, it should be inside the analyzer section
PUT /library
{
"settings": {
"analysis": {
"tokenizer": {
"ngram_tokenizer": {
"type": "nGram",
"min_gram": "1",
"max_gram": "15",
"token_chars": [
"letter",
"digit"
]
}
},
"analyzer": {
"index_ngram_analyzer": {
"type": "custom",
"tokenizer": "ngram_tokenizer",
"filter": [
"lowercase"
]
},
"search_term_analyzer": {
"type": "custom",
"tokenizer": "keyword",
"filter": "lowercase"
}
}
}
},
"mappings": {
"book": {
"properties": {
"Id": {
"type": "long", <---- you probably need to make this a string or remove the analyzers
"search_analyzer": "search_term_analyzer",
"analyzer": "index_ngram_analyzer",
"term_vector":"with_positions_offsets"
},
"Title": {
"type": "string",
"search_analyzer": "search_term_analyzer",
"analyzer": "index_ngram_analyzer",
"term_vector":"with_positions_offsets"
}
}
}
}
}
Also make sure to use analyzer instead of index_analyzer, the latter as been deprecated in ES 2.x
I've been trying to create my own index for users, where the query is indexed on the "name" value.
This is my current index settings:
{
"users": {
"settings": {
"index": {
"analysis": {
"filter": {
"shingle_filter": {
"max_shingle_size": "2",
"min_shingle_size": "2",
"output_unigrams": "true",
"type": "shingle"
},
"edgeNGram_filter": {
"type": "edgeNGram",
"min_gram": "1",
"max_gram": "20"
}
},
"analyzer": {
"autocomplete_query_analyzer": {
"filter": [
"standard",
"asciifolding",
"lowercase"
],
"tokenizer": "standard"
},
"autocomplete_index_analyzer": {
"filter": [
"standard",
"asciifolding",
"lowercase",
"shingle_filter",
"edgeNGram_filter"
],
"tokenizer": "standard"
}
}
},
"number_of_shards": "1",
"number_of_replicas": "1"
}
}
}
}
and my mapping:
{
"users": {
"mappings": {
"data": {
"properties": {
"name": {
"type": "string",
"analyzer": "autocomplete_index_analyzer",
"search_analyzer": "autocomplete_query_analyzer"
}
}
}
}
}
}
Right now my problem is that search queries do not return results that contain the term. For example if I have a user "David", the search queries "Da", "Dav", "Davi", etc will return the value but search for "vid" or "avid" will not return any values.
Is this because of some value I'm missing in the settings?
You need to use nGram instead of edgeNGram. So simply change this
"edgeNGram_filter": {
"type": "edgeNGram",
"min_gram": "1",
"max_gram": "20"
}
into this
"edgeNGram_filter": {
"type": "nGram", <--- change here
"min_gram": "1",
"max_gram": "20"
}
Note that you need to wipe your index, recreate it and the populate it again.