We want to leverage ElasticSearch to find us similar objects.
Lets say I have an Object with 4 fields:
product_name, seller_name, seller_phone, platform_id.
Similar products can have different product names and seller names across different platforms (fuzzy match).
While, phone is strict and a single variation might cause yield a wrong record (strict match).
What were trying to create is a query that will:
Take into account all fields we have for current record and OR
between them.
Mandate platform_id is the one I want to specific look at. (AND)
Fuzzy the product_name and seller_name
Strictly match the phone number or ignore it in the OR between the fields.
If I would write it in pseudo code, I would write something like:
((product_name like 'some_product_name') OR (seller_name like
'some_seller_name') OR (seller_phone = 'some_phone')) AND (platform_id
= 123)
To do exact match on seller_phone i am indexing this field without ngram analyzers along with fuzzy_query for product_name and seller_name
Mapping
PUT index111
{
"settings": {
"analysis": {
"analyzer": {
"edge_n_gram_analyzer": {
"tokenizer": "whitespace",
"filter" : ["lowercase", "ednge_gram_filter"]
}
},
"filter": {
"ednge_gram_filter" : {
"type" : "NGram",
"min_gram" : 2,
"max_gram": 10
}
}
}
},
"mappings": {
"document_type" : {
"properties": {
"product_name" : {
"type": "text",
"analyzer": "edge_n_gram_analyzer"
},
"seller_name" : {
"type": "text",
"analyzer": "edge_n_gram_analyzer"
},
"seller_phone" : {
"type": "text"
},
"platform_id" : {
"type": "text"
}
}
}
}
}
Index documents
POST index111/document_type
{
"product_name":"macbok",
"seller_name":"apple",
"seller_phone":"9988",
"platform_id":"123"
}
For following pseudo sql query
((product_name like 'some_product_name') OR (seller_name like 'some_seller_name') OR (seller_phone = 'some_phone')) AND (platform_id = 123)
Elastic Query
POST index111/_search
{
"query": {
"bool": {
"must": [
{
"term": {
"platform_id": {
"value": "123"
}
}
},
{
"bool": {
"should": [{
"fuzzy": {
"product_name": {
"value": "macbouk",
"boost": 1.0,
"fuzziness": 2,
"prefix_length": 0,
"max_expansions": 100
}
}
},
{
"fuzzy": {
"seller_name": {
"value": "apdle",
"boost": 1.0,
"fuzziness": 2,
"prefix_length": 0,
"max_expansions": 100
}
}
},
{
"term": {
"seller_phone": {
"value": "9988"
}
}
}
]
}
}]
}
}
}
Hope this helps
Related
Situation :
Elastic version used: 2.3.1
I have an elastic index configured like so
PUT /my_index
{
"settings": {
"analysis": {
"filter": {
"my_synonym_filter": {
"type": "synonym",
"synonyms": [
"british,english",
"queen,monarch"
]
}
},
"analyzer": {
"my_synonyms": {
"tokenizer": "standard",
"filter": [
"lowercase",
"my_synonym_filter"
]
}
}
}
}
}
Which is great, when I query the document and use a query term "english" or "queen" I get all documents matching british and monarch. When I use a synonym term in filter aggregation it doesnt work. For example
In my index I have 5 documents, 3 of them have monarch, 2 of them have queen
POST /my_index/_search
{
"size": 0,
"query" : {
"match" : {
"status.synonym":{
"query": "queen",
"operator": "and"
}
}
},
"aggs" : {
"status_terms" : {
"terms" : { "field" : "status.synonym" }
},
"monarch_filter" : {
"filter" : { "term": { "status.synonym": "monarch" } }
}
},
"explain" : 0
}
The result produces:
Total hits:
5 doc count (as expected, great!)
Status terms: 5 doc count for queen (as expected, great!)
Monarch filter: 0 doc count
I have tried different synonym filter configuration:
queen,monarch
queen,monarch => queen
queen,monarch => queen,monarch
But the above hasn't changed the results. I was wanting to conclude that maybe you can use filters at query time only but then if terms aggregation is working why shouldn't filter, hence I think its my synonym filter configuration that is wrong. A more extensive synonym filter example can be found here.
QUESTION:
How to use/configure synonyms in filter aggregation?
Example to replicate the case above:
1. Create and configure index:
PUT /my_index
{
"settings": {
"analysis": {
"filter": {
"my_synonym_filter": {
"type": "synonym",
"synonyms": [
"wlh,wellhead=>wellwell"
]
}
},
"analyzer": {
"my_synonyms": {
"tokenizer": "standard",
"filter": [
"lowercase",
"my_synonym_filter"
]
}
}
}
}
}
PUT my_index/_mapping/job
{
"properties": {
"title":{
"type": "string",
"analyzer": "my_synonyms"
}
}
}
2.Put two documents:
PUT my_index/job/1
{
"title":"wellhead smth else"
}
PUT my_index/job/2
{
"title":"wlh other stuff"
}
3.Execute a search on wlh which should return 2 documents; have a terms aggregation which should have 2 documents for wellwell and a filter which shouldn't have 0 count:
POST my_index/_search
{
"size": 0,
"query" : {
"match" : {
"title":{
"query": "wlh",
"operator": "and"
}
}
},
"aggs" : {
"wlhAggs" : {
"terms" : { "field" : "title" }
},
"wlhFilter" : {
"filter" : { "term": { "title": "wlh" } }
}
},
"explain" : 0
}
The results of this query is:
{
"took": 8,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 0,
"hits": []
},
"aggregations": {
"wlhAggs": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "wellwell",
"doc_count": 2
},
{
"key": "else",
"doc_count": 1
},
{
"key": "other",
"doc_count": 1
},
{
"key": "smth",
"doc_count": 1
},
{
"key": "stuff",
"doc_count": 1
}
]
},
"wlhFilter": {
"doc_count": 0
}
}
}
And thats my problem, the wlhFilter should have at least 1 doc count in it.
I'm short in time, so if needed I can elaborate a bit more at a later time today/tomorrow. But the following should work:
DELETE /my_index
PUT /my_index
{
"settings": {
"analysis": {
"filter": {
"my_synonym_filter": {
"type": "synonym",
"synonyms": [
"british,english",
"queen,monarch"
]
}
},
"analyzer": {
"my_synonyms": {
"tokenizer": "standard",
"filter": [
"lowercase",
"my_synonym_filter"
]
}
}
}
},
"mappings": {
"test": {
"properties": {
"title": {
"type": "text",
"analyzer": "my_synonyms",
"fielddata": true
}
}
}
}
}
POST my_index/test/1
{
"title" : "the british monarch"
}
GET my_index/_search
{
"query": {
"match": {
"title": "queen"
}
}
}
GET my_index/_search
{
"query": {
"match": {
"title": "queen"
}
},
"aggs": {
"queen_filter": {
"filter": {
"term": {
"title": "queen"
}
}
},
"monarch_filter": {
"filter": {
"term": {
"title": "monarch"
}
}
}
}
}
Could you share the mapping you have defined for your status.synonym field?
EDIT: V2
The reason why your filter's output is 0, is because a filter in Elasticsearch never goes through an analysis phase. It's meant for exact matches.
The token 'wlh' in your aggregation will not be translated to 'wellwell', meaning that it doesn't occur in the inverted index. This is because, during index time, your 'wlh' is translated into 'wellwell'.
In order to achieve what you want, you will have to index the data into a separate field and adjust your filter accordingly.
You could try something like:
DELETE my_index
PUT /my_index
{
"settings": {
"number_of_shards": 1,
"number_of_replicas": 0,
"analysis": {
"filter": {
"my_synonym_filter": {
"type": "synonym",
"synonyms": [
"wlh,wellhead=>wellwell"
]
}
},
"analyzer": {
"my_synonyms": {
"tokenizer": "standard",
"filter": [
"lowercase",
"my_synonym_filter"
]
}
}
}
},
"mappings": {
"job": {
"properties": {
"title": {
"type": "string",
"fields": {
"synonym": {
"type": "string",
"analyzer": "my_synonyms"
}
}
}
}
}
}
}
PUT my_index/job/1
{
"title":"wellhead smth else"
}
PUT my_index/job/2
{
"title":"wlh other stuff"
}
POST my_index/_search
{
"size": 0,
"query": {
"match": {
"title.synonym": {
"query": "wlh",
"operator": "and"
}
}
},
"aggs": {
"wlhAggs": {
"terms": {
"field": "title.synonym"
}
},
"wlhFilter": {
"filter": {
"term": {
"title": "wlh"
}
}
}
}
}
Output:
{
"aggregations": {
"wlhAggs": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "wellwell",
"doc_count": 2
},
{
"key": "else",
"doc_count": 1
},
{
"key": "other",
"doc_count": 1
},
{
"key": "smth",
"doc_count": 1
},
{
"key": "stuff",
"doc_count": 1
}
]
},
"wlhFilter": {
"doc_count": 1
}
}
}
Hope this helps!!
So with the help of #Byron Voorbach below and his comments this is my solution:
I have created a separate field which I use synonym analyser on, as
opposed to having a property field (mainfield.property).
And most importantly the problem was my synonyms were contracted! I
had, for example, british,english => uk. Changing that to
british,english,uk solved my issue and the filter aggregation is
returning the right number of documents.
Hope this helps someone, or at least point to the right direction.
Edit:
Oh lord praise the documentation! I completely fixed my issue with Filters (S!) aggregation (link here). In filters configuration I specified Match type of query and it worked! Ended up with something like this:
"aggs" : {
"messages" : {
"filters" : {
"filters" : {
"status" : { "match" : { "cats.saurus" : "monarch" }},
"country" : { "match" : { "cats.saurus" : "british" }}
}
}
}
}
I'm new with elasticsearch and I'm trying to develop a search for an ecommerce to suggested 5~10 matching products to the user.
As it should work while the user is typing, we found in the official documentation the use of edge_ngram and it KIND OF worked. But as we searched to test, the results were not the expected. As shows the example below (in our test)
Searching example
As it is shown in the image, the result for the term "Furadeira" (Power Drill) returns accessories before the power drill itself. How can I enhance the results? Even the order where the match is found in the string would help me, I guess.
So, this is the code I have until now:
//PUT example
{
"settings": {
"number_of_shards": 1,
"analysis": {
"filter": {
"autocomplete_filter": {
"type": "edge_ngram",
"min_gram": 1,
"max_gram": 20
},
"portuguese_stop": {
"type": "stop",
"stopwords": "_portuguese_"
},
"portuguese_stemmer": {
"type": "stemmer",
"language": "light_portuguese"
}
},
"analyzer": {
"portuguese": {
"type": "custom",
"tokenizer": "standard",
"filter": [
"lowercase",
"portuguese_stop",
"portuguese_stemmer"
]
},
"autocomplete": {
"type": "custom",
"tokenizer": "standard",
"filter": [
"lowercase",
"autocomplete_filter"
]
}
}
}
}
}
/* mapping */
//PUT /example/products/_mapping
{
"products": {
"properties": {
"name": {
"type": "text",
"analyzer": "autocomplete",
"search_analyzer": "standard"
}
}
}
}
/* Search */
//GET /example/products/_search
{
"query" : {
"query_string": {
"query" : "furadeira",
"type" : "most_fields", // Tried without this aswell
"fields" : [
"name^8",
"model^10",
"manufacturer^4",
"description"
]
}
}
}
/* Product example */
// PUT example/products/38313
{
"name": "FITA VEDA FRESTA (ESPUMA 4503) 12X5 M [ H0000164055 ]",
"description": "Caracteristicas do produto:Veā¦Diminui ruidos indesejaveis.",
"price":21.90,
"product_id": 38313,
"image": "http://placehold.it/200x200",
"quantity": 92,
"width": 20.200,
"height": 1.500,
"length": 21.500,
"weight": 0.082,
"model": "167083",
"manufacturer": "3M DO BRASIL"
}
Thanks in advance.
you could enhance your query to be a so-called boolean query, which contains your existing query in a must clause, but have an additional query in a should clause, that matches exactly (not using the ngrammed field). If the query matches the should clause it will be scored higher.
See the bool query documentation.
let's assume you have a field that differentiates the Main product from Accessories. I call it level_field.
now you can have two approaches to go:
1) boost up The Main product _score by adding 'should' operation:
put your main query in the must operation and in should operation use level_field to boost the _score of documents which are the Main products.
{
"query": {
"bool": {
"must": {
"match": {
"name": {
"query": "furadeira"
}
}
},
"should": [
{ "match": {
"level_field": {
"query": "level1",
"boost": 3
}
}},
{ "match": {
"level_field": {
"query": "level2",
"boost": 2
}
}}
]
}
}
}
2) in second approach you can decrease _score for documents that they are not the Main products by using boosting query:
{
"query": {
"boosting": {
"positive": {
"query_string": {
"query" : "furadeira",
"type" : "most_fields",
"fields" : [
"name^8",
"model^10",
"manufacturer^4",
"description"
]
}
}
},
"negative": {
"term": {
"level_field": {
"value": "level2"
}
}
},
"negative_boost": 0.2
}
}
}
I hope it helps
Suppose I have a set of documents like :-
{
"Name":"Random String 1"
"Type":"Keyword"
"City":"Lousiana"
"Quantity":"10"
}
Now I want to implement a full text search using an N-gram analyazer on the field Name and City.
After that , I want to filter only the results returned with
"_score" :<Query Score Returned by ES>
greater than 1.2 (Maybe By Range Query Aggregation Method)
And after that apply term aggregation method on the property: "Type" and then return the top results in each bucket by using "top_hits" aggregation method.
How can I do so ?
I've been able to implement everything apart from the Range Query on score returned by a search query.
if you want to score the documents organically then i you can use min_score in query to filter the matched documents for the score.
for ngram analyer i added whitespace tokenizer and a lowercase filter
Mappings
PUT index1
{
"settings": {
"analysis": {
"analyzer": {
"edge_n_gram_analyzer": {
"tokenizer": "whitespace",
"filter" : ["lowercase", "ednge_gram_filter"]
}
},
"filter": {
"ednge_gram_filter" : {
"type" : "NGram",
"min_gram" : 2,
"max_gram": 10
}
}
}
},
"mappings": {
"document_type" : {
"properties": {
"Name" : {
"type": "text",
"analyzer": "edge_n_gram_analyzer"
},
"City" : {
"type": "text",
"analyzer": "edge_n_gram_analyzer"
},
"Type" : {
"type": "keyword"
}
}
}
}
}
Index Document
POST index1/document_type
{
"Name":"Random String 1",
"Type":"Keyword",
"City":"Lousiana",
"Quantity":"10"
}
Query
POST index1/_search
{
"min_score": 1.2,
"size": 0,
"query": {
"bool": {
"should": [
{
"term": {
"Name": {
"value": "string"
}
}
},
{
"term": {
"City": {
"value": "string"
}
}
}
]
}
},
"aggs": {
"type_terms": {
"terms": {
"field": "Type",
"size": 10
},
"aggs": {
"type_term_top_hits": {
"top_hits": {
"size": 10
}
}
}
}
}
}
Hope this helps
Given an index with documents that have a brand property, we need to create a term aggregation that is case insensitive.
Index definition
Please note that the use of fielddata
PUT demo_products
{
"settings": {
"analysis": {
"analyzer": {
"my_custom_analyzer": {
"type": "custom",
"tokenizer": "keyword",
"filter": [
"lowercase"
]
}
}
}
},
"mappings": {
"product": {
"properties": {
"brand": {
"type": "text",
"analyzer": "my_custom_analyzer",
"fielddata": true,
}
}
}
}
}
Data
POST demo_products/product
{
"brand": "New York Jets"
}
POST demo_products/product
{
"brand": "new york jets"
}
POST demo_products/product
{
"brand": "Washington Redskins"
}
Query
GET demo_products/product/_search
{
"size": 0,
"aggs": {
"brand_facet": {
"terms": {
"field": "brand"
}
}
}
}
Result
"aggregations": {
"brand_facet": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "new york jets",
"doc_count": 2
},
{
"key": "washington redskins",
"doc_count": 1
}
]
}
}
If we use keyword instead of text we end up the 2 buckets for New York Jets because of the differences in casing.
We're concerned about the performance implications by using fielddata. However if fielddata is disabled we get the dreaded "Fielddata is disabled on text fields by default."
Any other tips to resolve this - or should we not be so concerned about fielddate?
Starting with ES 5.2 (out today), you can use normalizers with keyword fields in order to (e.g.) lowercase the value.
The role of normalizers is a bit like analyzers for text fields, though what you can do with them is more restrained, but that would probably help with the issue you're facing.
You'd create the index like this:
PUT demo_products
{
"settings": {
"analysis": {
"normalizer": {
"my_normalizer": {
"type": "custom",
"filter": [ "lowercase" ]
}
}
}
},
"mappings": {
"product": {
"properties": {
"brand": {
"type": "keyword",
"normalizer": "my_normalizer"
}
}
}
}
}
And your query would return this:
"aggregations" : {
"brand_facet" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "new york jets",
"doc_count" : 2
},
{
"key" : "washington redskins",
"doc_count" : 1
}
]
}
}
Best of both worlds!
You can lowercase the aggregation at query time if you use a script. It won't perform as well as a normalized keyword field, but is still quite fast in my experience. For example, your query would be:
GET demo_products/product/_search
{
"size": 0,
"aggs": {
"brand_facet": {
"terms": {
"script": "doc['brand'].value.toLowerCase()"
}
}
}
}
I have an elastic search index with following documents and I want to have an autocomplete functionality over the specified fields:
mapping: https://gist.github.com/anonymous/0609b1d110d91dceb9a90faa76d1d5d4
Usecase:
My query is of the form prefix type eg "sta", "star", "star w" .."start war" etc with an additional filter as tags = "science fiction". Also there queries could match other fields like description, actors(in cast field, not this is nested). I also want to know which field it matched to.
I investigated 2 ways for doing that but non of the methods seem to address the usecase above:
1) Suggester autocomplete:
https://www.elastic.co/guide/en/elasticsearch/reference/1.7/search-suggesters-completion.html
With this it seems I have to add another field called "suggest" replicating the data which is not desirable.
2) using a prefix filter/query:
https://www.elastic.co/guide/en/elasticsearch/reference/1.7/query-dsl-prefix-filter.html
this gives the whole document back not the exact matching terms.
Is there a clean way of achieving this, please advise.
Don't create mapping separately, insert data directly into index. It will create default mapping for that. Use below query for autocomplete.
GET /netflix/movie/_search
{
"query": {
"query_string": {
"query": "sta*"
}
}
}
I think completion suggester would be the cleanest way but if that is undesirable you could use aggregations on name field.
This is a sample index(I am assuming you are using ES 1.7 from your question
PUT netflix
{
"settings": {
"analysis": {
"analyzer": {
"prefix_analyzer": {
"tokenizer": "keyword",
"filter": [
"lowercase",
"trim",
"edge_filter"
]
},
"keyword_analyzer": {
"tokenizer": "keyword",
"filter": [
"lowercase",
"trim"
]
}
},
"filter": {
"edge_filter": {
"type": "edge_ngram",
"min_gram": 1,
"max_gram": 20
}
}
}
},
"mappings": {
"movie":{
"properties": {
"name":{
"type": "string",
"fields": {
"prefix":{
"type":"string",
"index_analyzer" : "prefix_analyzer",
"search_analyzer" : "keyword_analyzer"
},
"raw":{
"type": "string",
"analyzer": "keyword_analyzer"
}
}
},
"tags":{
"type": "string", "index": "not_analyzed"
}
}
}
}
}
Using multi-fields, name field is analyzed in different ways. name.prefix is using keyword tokenizer with edge ngram filter
so that string star wars can be broken into s, st, sta etc. but while searching, keyword_analyzer is used so that search query does not get broken into multiple small tokens. name.raw will be used for aggregation.
The following query will give top 10 suggestions.
GET netflix/movie/_search
{
"query": {
"filtered": {
"filter": {
"term": {
"tags": "sci-fi"
}
},
"query": {
"match": {
"name.prefix": "sta"
}
}
}
},
"size": 0,
"aggs": {
"unique_movie_name": {
"terms": {
"field": "name.raw",
"size": 10
}
}
}
}
Results will be something like
"aggregations": {
"unique_movie_name": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "star trek",
"doc_count": 1
},
{
"key": "star wars",
"doc_count": 1
}
]
}
}
UPDATE :
You could use highlighting for this purpose I think. Highlight section will get you the whole word and which field it matched. You can also use inner hits and highlighting inside it to get nested docs also.
{
"query": {
"query_string": {
"query": "sta*"
}
},
"_source": false,
"highlight": {
"fields": {
"*": {}
}
}
}