I'm trying to query ElasticSearch to match every document that in a list of list contains all the values requested, but I can't seem to find the perfect query.
Mapping:
"id" : {
"type" : "keyword"
},
"mainlist" : {
"properties" : {
"format" : {
"type" : "keyword"
},
"tags" : {
"type" : "keyword"
}
}
},
...
Documents:
doc1 {
"id" : "abc",
"mainlist" : [
{
"type" : "big",
"tags" : [
"tag1",
"tag2"
]
},
{
"type" : "small",
"tags" : [
"tag1"
]
}
]
},
doc2 {
"id" : "abc",
"mainlist" : [
{
"type" : "big",
"tags" : [
"tag1"
]
},
{
"type" : "small",
"tags" : [
"tag2"
]
}
]
},
doc3 {
"id" : "abc",
"mainlist" : [
{
"type" : "big",
"tags" : [
"tag1"
]
}
]
}
The query I've tried that got me closest to the result is:
GET /index/_doc/_search
{
"query": {
"bool": {
"must": [
{
"term": {
"mainlist.tags": "tag1"
}
},
{
"term": {
"mainlist.tags": "tag2"
}
}
]
}
}
}
although I get as result doc1 and doc2, while I'd only want doc1 as contains tag1 and tag2 in a single list element and not spread across both sublists.
How would I be able to achieve that?
Thanks for any help.
As mentioned by #caster, you need to use the nested data type and query as in normal way Elasticsearch treats them as object and relation between the elements are lost, as explained in offical doc.
You need to change both mapping and query to achieve the desired output as shown below.
Index mapping
{
"mappings": {
"properties": {
"id": {
"type": "keyword"
},
"mainlist" :{
"type" : "nested"
}
}
}
}
Sample Index doc according to your example, no change there
Query
{
"query": {
"nested": {
"path": "mainlist",
"query": {
"bool": {
"must": [
{
"term": {
"mainlist.tags": "tag1"
}
},
{
"match": {
"mainlist.tags": "tag2"
}
}
]
}
}
}
}
}
And result
hits": [
{
"_index": "71519931_new",
"_id": "1",
"_score": 0.9139043,
"_source": {
"id": "abc",
"mainlist": [
{
"type": "big",
"tags": [
"tag1",
"tag2"
]
},
{
"type": "small",
"tags": [
"tag1"
]
}
]
}
}
]
use nested field type，this is work for it
https://www.elastic.co/guide/en/elasticsearch/reference/8.1/nested.html
Related
I have two fields in my elastic search which is lowest_local_price and lowest_global_price.
I want to map dynamic value to third field price on run time based on local or global country.
If local country matched then i want to map lowest_local_price value to price field.
If global country matched then i want to map lowest_global_price value to price field.
If local or global country matched then i want to apply range query on the price field and boost that doc by 2.0.
Note : This is not compulsary filter or query, if matched then just want to boost the doc.
I have tried below solution but does not work for me.
Query 1:
$params["body"] = [
"runtime_mappings" => [
"price" => [
"type" => "double",
"script" => [
"source" => "if (params['_source']['country_en_name'] == '$country_name' ) { emit(params['_source']['lowest_local_price']); } else { emit( params['_source']['global_rates']['$country->id']['lowest_global_price']); }"
]
]
],
"query" => [
"bool" => [
"filter" => [
"range" => [ "price" => [ "gte" => $min_price]]
],
"boost" => 2.0
]
]
];
Query 2:
$params["body"] = [
"runtime_mappings" => [
"price" => [
"type" => "double",
"script" => [
"source" => "if (params['_source']['country_en_name'] == '$country_name' ) { emit(params['_source']['lowest_local_price']); } else { emit( params['_source']['global_rates']['$country->id']['lowest_global_price']); }"
]
]
],
"query" => [
"bool" => [
"filter" => [
"range" => [ "price" => [ "gte" => $min_price, "boost" => 2.0]]
],
]
]
];
None of them working for me, because it can boost the doc. I know filter does not work with boost, then what is the solution for dynamic field mapping with range query and boost?
Please help me to solve this query.
Thank you in advance!
You can (most likely) achieve what you want without runtime_mappings by using a combination of bool queries, here's how.
Let's define test mapping
We need to clarify what mapping we are working with, because different field types require different query types.
Let's assume that your mapping looks like this:
PUT my-index-000001
{
"mappings": {
"dynamic": "runtime",
"properties": {
"country_en_name": {
"type": "text"
},
"lowest_local_price": {
"type": "float"
},
"global_rates": {
"properties": {
"UK": {
"properties":{
"lowest_global_price": {
"type": "float"
}
}
},
"FR": {
"properties":{
"lowest_global_price": {
"type": "float"
}
}
},
"US": {
"properties":{
"lowest_global_price": {
"type": "float"
}
}
}
}
}
}
}
}
Note that country_en_name is of type text, in general such fields should be indexed as keyword but for the sake of demonstration of the use of runtime_mappings I kept it text and will show later how to overcome this limitation.
bool is the same as if for Elasticsearch
The query without runtime mappings might look like this:
POST my-index-000001/_search
{
"query": {
"bool": {
"should": [
{
"match_all": {}
},
{
"bool": {
"should": [
{
"bool": {
"must": [
{
"match": {
"country_en_name": "UK"
}
},
{
"range": {
"lowest_local_price": {
"gte": 1000
}
}
}
]
}
},
{
"range": {
"global_rates.UK.lowest_global_price": {
"gte": 1000
}
}
}
],
"boost": 2
}
}
]
}
}
}
This can be interpreted as the following:
Any document
OR (
(document with country_en_name=UK AND lowest_local_price > X)
OR
(document with global_rates.UK.lowest_global_price > X)
)[boost this part of OR]
The match_all is needed to return also documents that do not match the other queries.
How will the response of the query look like?
Let's put some documents in the ES:
POST my-index-000001/_doc/1
{
"country_en_name": "UK",
"lowest_local_price": 1500,
"global_rates": {
"FR": {
"lowest_global_price": 1000
},
"US": {
"lowest_global_price": 1200
}
}
}
POST my-index-000001/_doc/2
{
"country_en_name": "FR",
"lowest_local_price": 900,
"global_rates": {
"UK": {
"lowest_global_price": 950
},
"US": {
"lowest_global_price": 1500
}
}
}
POST my-index-000001/_doc/3
{
"country_en_name": "US",
"lowest_local_price": 950,
"global_rates": {
"UK": {
"lowest_global_price": 1100
},
"FR": {
"lowest_global_price": 1000
}
}
}
Now the result of the search query above will be something like:
{
...
"hits" : {
"total" : {
"value" : 3,
"relation" : "eq"
},
"max_score" : 4.9616585,
"hits" : [
{
"_index" : "my-index-000001",
"_type" : "_doc",
"_id" : "1",
"_score" : 4.9616585,
"_source" : {
"country_en_name" : "UK",
"lowest_local_price" : 1500,
...
}
},
{
"_index" : "my-index-000001",
"_type" : "_doc",
"_id" : "3",
"_score" : 3.0,
"_source" : {
"country_en_name" : "US",
"lowest_local_price" : 950,
"global_rates" : {
"UK" : {
"lowest_global_price" : 1100
},
...
}
}
},
{
"_index" : "my-index-000001",
"_type" : "_doc",
"_id" : "2",
"_score" : 1.0,
"_source" : {
"country_en_name" : "FR",
"lowest_local_price" : 900,
"global_rates" : {
"UK" : {
"lowest_global_price" : 950
},
...
}
}
}
]
}
}
Note that document with _id:2 is on the bottom because it didn't match any of the boosted queries.
Will runtime_mappings be of any use?
Runtime mappings are useful in case there's an existing mapping with data types that do not permit to execute a certain type of query. In previous versions (before 7.11) one would have to do a reindex in such cases, but now it is possible to use runtime mappings (but the query is more expensive).
In our case, we have got country_en_name indexed as text which is suited for full-text search and not for exact lookups. We should rather use keyword instead. This is how the query may look like with the help of runtime_mappings:
POST my-index-000001/_search
{
"runtime_mappings": {
"country_en_name_keyword": {
"type": "keyword",
"script": {
"source": "emit(params['_source']['country_en_name'])"
}
}
},
"query": {
"bool": {
"should": [
{
"match_all": {}
},
{
"bool": {
"should": [
{
"bool": {
"must": [
{
"term": {
"country_en_name_keyword": "UK"
}
},
{
"range": {
"lowest_local_price": {
"gte": 1000
}
}
}
]
}
},
{
"range": {
"global_rates.UK.lowest_global_price": {
"gte": 1000
}
}
}
],
"boost": 2
}
}
]
}
}
}
Notice how we created a new runtime field country_en_name_keyword with type keyword and used a term lookup instead of match query.
I have a question regarding the query of an array of Elasticsearch. in my case, the structure of the custom attributes is an array of objects, each contains inner_name and value, the type of value are mixed (could be string, number, array, Date..etc), and one of the types is multi-checkbox, where it should take an array as an input. Mapping the custom_attributes as below:
"attributes" : {
"properties" : {
"_id" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
},
"inner_name" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
},
"value" : {
"type" : "text",
"fields" : {
"keyword" : {
"type" : "keyword",
"ignore_above" : 256
}
}
}
}
},
Where I used mongoosastic to indexing my MongoDB to ES, so the structure of the custom attributes was like:
[
{
customer_name: "customerX",
"custom_attributes" : [
{
"group" : "xyz",
"attributes" : [
{
"inner_name" : "attr1",
"value" : 123,
},
{
"inner_name" : "attr2",
"value" : [
"Val1",
"Val2",
"Val3",
"Val4"
]
}
]
}
]
},
{
customer_name: "customerY",
"custom_attributes" : [
{
"group" : "xyz",
"attributes" : [
{
"inner_name" : "attr2",
"value" : [
"Val1",
"Val2"
]
}
]
}
]
}
]
I want to perform a query where all values must be in the array. However, the problem with the below query is that it returns the document whenever it contains any of the values in the array. Here's the query:
{
"query": {
"bool": {
"must": [
{
"match": {
"custom_attributes.attributes.inner_name": "attr2"
}
},
{
"terms": {
"custom_attributes.attributes.value": [
"val1",
"val2",
"val3",
"val4"
]
}
}
]
}
}
}
For example, it returns both documents, where it should return just the first one only! what is wrong with my query? Is there another way to write the query?
The elasticsearch terms query tries to match any if any of your values is present in a document, think of the operator being OR instead of AND which is the one you want. There are two solutions for this
Use multiple term queries inside your bool must query, which will provide the needed AND functionality
{
"query": {
"bool": {
"must": [
{
"match": {
"custom_attributes.attributes.inner_name": "attr2"
}
},
{
"term": {
"custom_attributes.attributes.value": "val1"
}
},
{
"term": {
"custom_attributes.attributes.value": "val2"
}
},
{
"term": {
"custom_attributes.attributes.value": "val3"
}
},
{
"term": {
"custom_attributes.attributes.value": "val4"
}
}
]
}
}
}
Use the match query with the operator AND and whitespace analyzer. This WILL NOT work if your terms contain a whitespace
{
"query": {
"bool": {
"must": [
{
"match": {
"custom_attributes.attributes.inner_name": "attr2"
}
},
{
"match": {
"custom_attributes.attributes.value": {
"query": "val1 val2 val3 val4",
"operator": "and",
"analyzer": "whitespace"
}
}
}
]
}
}
}
I have an elastic search index with the following mapping:
PUT /student_detail
{
"mappings" : {
"properties" : {
"id" : { "type" : "long" },
"name" : { "type" : "text" },
"email" : { "type" : "text" },
"age" : { "type" : "text" },
"status" : { "type" : "text" },
"tests":{ "type" : "nested" }
}
}
}
Data stored is in form below:
{
"id": 123,
"name": "Schwarb",
"email": "abc#gmail.com",
"status": "current",
"age": 14,
"tests": [
{
"test_id": 587,
"test_score": 10
},
{
"test_id": 588,
"test_score": 6
}
]
}
I want to be able to query the students where name like '%warb%' AND email like '%gmail.com%' AND test with id 587 have score > 5 etc. The high level of what is needed can be put something like below, dont know what would be the actual query, apologize for this messy query below
GET developer_search/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"name": "abc"
}
},
{
"nested": {
"path": "tests",
"query": {
"bool": {
"must": [
{
"term": {
"tests.test_id": IN [587]
}
},
{
"term": {
"tests.test_score": >= some value
}
}
]
}
}
}
}
]
}
}
}
The query must be flexible so that we can enter dynamic test Ids and their respective score filters along with the fields out of nested fields like age, name, status
Something like that?
GET student_detail/_search
{
"query": {
"bool": {
"must": [
{
"wildcard": {
"name": {
"value": "*warb*"
}
}
},
{
"wildcard": {
"email": {
"value": "*gmail.com*"
}
}
},
{
"nested": {
"path": "tests",
"query": {
"bool": {
"must": [
{
"term": {
"tests.test_id": 587
}
},
{
"range": {
"tests.test_score": {
"gte": 5
}
}
}
]
}
},
"inner_hits": {}
}
}
]
}
}
}
Inner hits is what you are looking for.
You must make use of Ngram Tokenizer as wildcard search must not be used for performance reasons and I wouldn't recommend using it.
Change your mapping to the below where you can create your own Analyzer which I've done in the below mapping.
How elasticsearch (albiet lucene) indexes a statement is, first it breaks the statement or paragraph into words or tokens, then indexes these words in the inverted index for that particular field. This process is called Analysis and that this would only be applicable on text datatype.
So now you only get the documents if these tokens are available in inverted index.
By default, standard analyzer would be applied. What I've done is I've created my own analyzer and used Ngram Tokenizer which would be creating many more tokens than just simply words.
Default Analyzer on Life is beautiful would be life, is, beautiful.
However using Ngrams, the tokens for Life would be lif, ife & life
Mapping:
PUT student_detail
{
"settings": {
"analysis": {
"analyzer": {
"my_analyzer": {
"tokenizer": "my_tokenizer"
}
},
"tokenizer": {
"my_tokenizer": {
"type": "ngram",
"min_gram": 3,
"max_gram": 4,
"token_chars": [
"letter",
"digit"
]
}
}
}
},
"mappings" : {
"properties" : {
"id" : {
"type" : "long"
},
"name" : {
"type" : "text",
"analyzer": "my_analyzer",
"fields": {
"keyword": {
"type": "keyword"
}
}
},
"email" : {
"type" : "text",
"analyzer": "my_analyzer",
"fields": {
"keyword": {
"type": "keyword"
}
}
},
"age" : {
"type" : "text" <--- I am not sure why this is text. Change it to long or int. Would leave this to you
},
"status" : {
"type" : "text",
"analyzer": "my_analyzer",
"fields": {
"keyword": {
"type": "keyword"
}
}
},
"tests":{
"type" : "nested"
}
}
}
}
Note that in the above mapping I've created a sibling field in the form of keyword for name, email and status as below:
"name":{
"type":"text",
"analyzer":"my_analyzer",
"fields":{
"keyword":{
"type":"keyword"
}
}
}
Now your query could be as simple as below.
Query:
POST student_detail/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"name": "war" <---- Note this. This would even return documents having "Schwarb"
}
},
{
"match": {
"email": "gmail" <---- Note this
}
},
{
"nested": {
"path": "tests",
"query": {
"bool": {
"must": [
{
"term": {
"tests.test_id": 587
}
},
{
"range": {
"tests.test_score": {
"gte": 5
}
}
}
]
}
}
}
}
]
}
}
}
Note that for exact matches I would make use of Term Queries on keyword fields while for normal searches or LIKE in SQL I would make use of simple Match Queries on text Fields provided they make use of Ngram Tokenizer.
Also note that for >= and <= you would need to make use of Range Query.
Response:
{
"took" : 233,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
},
"max_score" : 3.7260926,
"hits" : [
{
"_index" : "student_detail",
"_type" : "_doc",
"_id" : "1",
"_score" : 3.7260926,
"_source" : {
"id" : 123,
"name" : "Schwarb",
"email" : "abc#gmail.com",
"status" : "current",
"age" : 14,
"tests" : [
{
"test_id" : 587,
"test_score" : 10
},
{
"test_id" : 588,
"test_score" : 6
}
]
}
}
]
}
}
Note that I observe the document you've mentioned in your question, in my response when I run the query.
Please do read the links I've shared. It is vital that you understand the concepts. Hope this helps!
I have a field with mapping:
"my_field_name": {
"type": "nested",
"properties": {
"labels": {
"type": "text"
},
"source": {
"type": "text"
}
}
}
and when I put data, format is like:
"my_field_name": {
"url_of_an_image": [
"tag1",
"tag2",
"tag3",
"tag4",
"tag5"
],
"url_of_an_image2": [
"tag4",
"tag5",
"tag6",
"tag7",
"tag8"
]
}
I want to write an elasticsearch query to gather all the documents that contains given tag.
How can I search a tag in all documents and select the ones that contains given tag?
Thank you
(EDITED AFTER COMMENT)
As a first consideration, the mapping doesn't match the structure of your document, e.g., my_field_name has neither labels nor source fields. Which is the one that better represents your data?
As a second consideration, in your comment you said that
while querying I don't know "url_of_an_image" since it is different for each document
However, from your example, it looks like url_of_an_image is a property of the items of my_field_name rather than of the document. Therefore, I'd suggest doing something like:
"my_field_name": [
{
"image_url": "url_of_an_image",
"tags": [
"tag1",
"tag2",
"tag3",
"tag4",
"tag5"
]
},
{
"image_url": "url_of_an_image2",
"tags": [
"tag4",
"tag5",
"tag6",
"tag7",
"tag8"
]
}
]
And this would be the mapping:
"my_field_name": {
"type": "nested",
"properties": {
"image_url": {
"type": "text"
},
"tags": {
"type": "text"
}
}
}
This way, you can run queries like:
{
"query": {
"nested" : {
"path" : "my_field_name",
"query" : {
"bool" : {
"must" : [
{ "match" : {"my_field_name.image_url" : "url_of_an_image2"} },
{ "match" : {"my_field_name.tags" : "tag3"} }
]
}
}
}
}
}
You can achieve this by using the Nested Query: https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-nested-query.html
i am trying to filter my result using nested filter but i am getting incorrect result
here is my mapping info
{
"stock" : {
"mappings" : {
"clip" : {
"properties" : {
"description" : {
"type" : "string"
},
"keywords" : {
"type" : "nested",
"properties" : {
"category" : {
"type" : "string"
},
"tags" : {
"type" : "string",
"index_name" : "tag"
}
}
},
"tags" : {
"type" : "string",
"index_name" : "tag"
},
"title" : {
"type" : "string"
}
}
}
}
}
}
clip document data
{
"_index" : "stock",
"_type" : "clip",
"_id" : "AUnsTOBBpafrKleQN284",
"_score" : 1.0,
"_source":{
"title": "journey to forest",
"description": "this clip contain information about the animals",
"tags": ["birls", "wild", "animals", "roar", "forest"],
"keywords": [
{
"tags": ["spring","summer","autumn"],
"category": "Weather"
},
{
"tags": ["Cloudy","Stormy"],
"category": "Season"
},
{
"tags": ["Exterior","Interior"],
"category": "Setting"
}
]
}
i am trying to filter tags inside nested field 'keywords'
here is my query
{
"query": {
"filtered": {
"query": {
"match_all": {}
},
"filter": {
"nested": {
"path": "keywords",
"filter": {
"bool": {
"must": [
{
"terms": { "tags": ["autumn", "summer"] }
}
]
}
}
}
}
}
}
}
i am getting no result why ?
what's wrong with my query or schema please help
The above query is syntactically incorrect . You need to provide the full path to tags from root keywords in the term query i.e.keywords.tags
{
"query": {
"filtered": {
"query": {
"match_all": {}
},
"filter": {
"nested": {
"path": "keywords",
"filter": {
"bool": {
"must": [
{
"terms": { "keywords.tags": ["autumn", "summer"] }
}
]
}
}
}
}
}
}
}