I am new to Elasticsearch, and I am not sure how to proceed in my situation.
I have the following mapping:
{
"mappings": {
"book": {
"properties": {
"title": {
"properties": {
"en": {
"type": "string",
"analyzer": "english"
},
"ar": {
"type": "string",
"analyzer": "arabic"
}
}
},
"keyword": {
"properties": {
"en": {
"type": "string",
"analyzer": "english"
},
"ar": {
"type": "string",
"analyzer": "arabic"
}
}
}
}
}
}
}
A sample document may have two languages for the same field of the same book. Here are two example documents:
{
"title" : {
"en": "hello",
"ar": "مرحبا"
},
"keyword" : {
"en": "world",
"ar": "عالم"
}
}
{
"title" : {
"en": "Elasticsearch"
},
"keyword" : {
"en": "full-text index"
}
}
When I know what language is used in query, I am able to build query as follows (when English is used):
"query": {
"multi_match" : {
"query" : "keywords",
"fields" : [ "title.en", "keyword.en" ]
}
}
Based on my current document mapping, how can I build a query if
the query language is unknown or
is mixed with English and Arabic?
Thanks for any input!
Regards.
p.s. I am also open to any improvement to the above mapping.
the query language is unknown
You can use same multi match query but on all the fields.for eg,
Assuming you are using keyword analyzer
"query": {
"multi_match" : {
"query" : "keywords",
"fields" : [ "title.en", "keyword.en", "title.ar", "keyword.ar" ]
}
}
is mixed with English and Arabic
You need to change the analyzer to standard and then you can perform the same query.
Thanks
Related
I have a bunch of categories with translations in my category field. I have defined language analyzers for the fields in my index so I can search for them. But it doesnt find the singular version of my words. wasmachine in titles.title-nl is singular of wasmachines but not found. What am I missing?
Demo document
"_source" : {
"google_id" : 2706,
"titles" : [
{
"title-en" : "laundry appliances",
"title-de" : "waschen & trocknen",
"title-fr" : "appareils de blanchisserie",
"title-nl" : "wasmachines"
}
]
}
Way I mapped them
PUT categories/_mapping/category
{
"dynamic": false,
"properties": {
"titles.title-nl": {
"type": "text",
"analyzer": "dutch"
},
"titles.title-en": {
"type": "text",
"analyzer": "english"
},
"titles.title-de": {
"type": "text",
"analyzer": "german"
},
"titles.title-fr": {
"type": "text",
"analyzer": "french"
}
}
}
The way I search for them
GET categories/_search
{
"size": 4,
"query": {
"multi_match": {
"query": "wasmachines",
"fields": ["titles.title-de","titles.title-en", "titles.title-fr", "titles.title-nl"]
}
}
}
The problem is that the default dutch analyzer doesn't know how to stem the word wasmachines, you will need to recreate your index with a custom analyzer using a stemmer_override.
Looking in the elastic documentation you can do the following to recreate the dutch analyzer and tell that wasmachines should be stemmed to wasmachine, just put wasmachine => wasmachines inside the rules for the stemmer_override
PUT categories/
{
"settings": {
"analysis": {
"filter": {
"dutch_stop": {
"type": "stop",
"stopwords": "_dutch_"
},
"dutch_keywords": {
"type": "keyword_marker",
"keywords": ["voorbeeld"]
},
"dutch_stemmer": {
"type": "stemmer",
"language": "dutch"
},
"dutch_override": {
"type": "stemmer_override",
"rules": [
"fiets=>fiets",
"bromfiets=>bromfiets",
"wasmachine=>wasmachines",
"ei=>eier",
"kind=>kinder"
]
}
},
"analyzer": {
"rebuilt_dutch": {
"tokenizer": "standard",
"filter": [
"lowercase",
"dutch_stop",
"dutch_keywords",
"dutch_override",
"dutch_stemmer"
]
}
}
}
}
}
You will also need to use that new analyzer in your mapping:
PUT categories/_mapping/category
{
"dynamic": false,
"properties": {
"titles.title-nl": {
"type": "text",
"analyzer": "rebuilt_dutch"
},
"titles.title-en": {
"type": "text",
"analyzer": "english"
},
"titles.title-de": {
"type": "text",
"analyzer": "german"
},
"titles.title-fr": {
"type": "text",
"analyzer": "french"
}
}
}
After that you will be able to search for wasmachine and get the documents that have wasmachines.
I have created the index in elasticsearch with the following mapping:
{
"test": {
"mappings": {
"documents": {
"properties": {
"fields": {
"type": "nested",
"properties": {
"uid": {
"type": "keyword"
},
"value": {
"type": "text",
"copy_to": [
"fulltext"
]
}
}
},
"fulltext": {
"type": "text"
},
"tags": {
"type": "text"
},
"title": {
"type": "text",
"fields": {
"raw": {
"type": "keyword"
}
}
},
"url": {
"type": "text",
"fields": {
"raw": {
"type": "keyword"
}
}
}
}
}
}
}
}
While searching I want to set the preference of fields for example if search text found in title or url then that document comes first then other documents.
Can we set a field preference for search result sequence(in my case preference like title,url,tags,fields)?
Please help me into this?
This is called "boosting" . Prior to elasticsearch 5.0.0 - boosting could be applied in indexing phase or query phase( added as part of field mapping ). This feature is deprecated now and all mappings after 5.0 are applied in query time .
Current recommendation is to to use query time boosting.
Please read this documents to get details on how to use boosting:
1 - https://www.elastic.co/guide/en/elasticsearch/guide/current/_boosting_query_clauses.html
2 - https://www.elastic.co/guide/en/elasticsearch/guide/current/_boosting_query_clauses.html
I am trying to search again content using prefix and if I search for diode I get results that differ from Diode. How do I get ES to return result where both diode and Diode return the same results? This is the mappings and settings I am using in ES.
"settings":{
"analysis": {
"analyzer": {
"lowercasespaceanalyzer": {
"type": "custom",
"tokenizer": "whitespace",
"filter": [
"lowercase"
]
}
}
}
},
"mappings": {
"articles": {
"properties": {
"title": {
"type": "text"
},
"url": {
"type": "keyword",
"index": "true"
},
"imageurl": {
"type": "keyword",
"index": "true"
},
"content": {
"type": "text",
"analyzer" : "lowercasespaceanalyzer",
"search_analyzer":"whitespace"
},
"description": {
"type": "text"
},
"relatedcontentwords": {
"type": "text"
},
"cmskeywords": {
"type": "text"
},
"partnumbers": {
"type": "keyword",
"index": "true"
},
"pubdate": {
"type": "date"
}
}
}
}
here is an example of the query I use
POST _search
{
"query": {
"bool" : {
"must" : {
"prefix" : { "content" : "capacitance" }
}
}
}
}
it happens because you use two different analyzers at search time and at indexing time.
So when you input query "Diod" at search time because you use "whitespace" analyzer your query is interpreted as "Diod".
However, because you use "lowercasespaceanalyzer" at index time "Diod" will be indexed as "diod". Just use the same analyzer both at search and index time, or analyzer that lowercases your strings because default "whitespace" analyzer doesn't https://www.elastic.co/guide/en/elasticsearch/reference/current/analysis-whitespace-analyzer.html
There will be no term of Diode in your index. So if you want to get same results, you should let your query context analyzed by same analyzer.
You can use Query string query like
"query_string" : {
"default_field" : "content",
"query" : "Diode",
"analyzer" : "lowercasespaceanalyzer"
}
UPDATE
You can analyze your context before query.
AnalyzeResponse resp = client.admin().indices()
.prepareAnalyze(index, text)
.setAnalyzer("lowercasespaceanalyzer")
.get();
String analyzedContext = resp.getTokens().get(0);
...
Then use analyzedContext as new query context.
I'm trying to use multi field properties for multi language support. I created following mapping for this:
{
"mappings": {
"product": {
"properties": {
"prod-id": {
"type": "string"
},
"prod-name": {
"type": "string",
"fields": {
"en": {
"type": "string",
"analyzer": "english"
},
"fr": {
"type": "string",
"analyzer": "french"
}
}
}
}
}
}
}
I created test record:
{
"prod-id": "1234567",
"prod-name": [
"Test product",
"Produit d'essai"
]
}
and tried to query using some language:
{
"query": {
"bool": {
"must": [
{"match": {
"prod-name.en": "Produit"
}}
]
}
}
}
As a result I got my document. But I expected that I will have empty result when I use French but choose English. It seems ElasticSearch ignores which field I specified in query. There is no difference in search result when I use "prod-name.en" or "prod-name.fr" or just "prod-name". Is this behaviour expected? Should I do some special things to have searching just in one language?
Another problem with updating multi field property. I can't update just one field.
{
"doc" : {
"prod-name.en": "Test"
}
}
I got following error:
{
"error": {
"root_cause": [
{
"type": "mapper_parsing_exception",
"reason": "Field name [prod-name.en] cannot contain '.'"
}
],
"type": "mapper_parsing_exception",
"reason": "Field name [prod-name.en] cannot contain '.'"
},
"status": 400
}
Is there any way to update just one field in multi field property?
In your mapping, the prod-name.en field will simply be analyzed using the english analyzer and the same for the french field. However, ES will not choose for you which value to put in which field.
Instead, you need to modify your mapping like this
{
"mappings": {
"product": {
"properties": {
"prod-id": {
"type": "string"
},
"prod-name": {
"type": "object",
"properties": {
"en": {
"type": "string",
"analyzer": "english"
},
"fr": {
"type": "string",
"analyzer": "french"
}
}
}
}
}
}
}
and input document to be like this and you'll get the results you expect.
{
"prod-id": "1234567",
"prod-name": {
"en": "Test product",
"fr": "Produit d'essai"
}
}
As for the updating part, your partial document should be like this instead.
{
"doc" : {
"prod-name": {
"en": "Test"
}
}
}
I want to run the any query/filter based on the field exists. In our case if user answers a particular field then only we will store that value, other wise will not store that field it self. How can I run the query?
Below is my mapping:
"mappings": {
"responses_10_57": {
"properties": {
"rid: {
"type": "long"
},
"end_time": {
"type": "date",
"format": "dateOptionalTime"
},
"start_time": {
"type": "date",
"format": "dateOptionalTime"
},
"qid_1": {
"type": "string",
"fields": {
"raw": {
"type": "string",
"analyzer": "str_params"
}
}
},
"qid_2": {
"type": "string",
"fields": {
"raw": {
"type": "string",
"analyzer": "str_params"
}
}
},
"qid_3": {
"properties": {
"msg_text": {
"type": "string"
},
"msg_tags": {
"type": "string",
"fields": {
"raw": {
"type": "string",
"analyzer": "str_params"
}
}
}
}
}
}
}
}
qid_1 is the name field, qid_2 is the category field, qid_3 is the text message field.
But the qid_3 is not a mandatory field. So we will not insert the record if user doesn't entered any text message.
1) I want each category wide count those who responded the third question.
2) I have to search the names who answered the third question.
How can I write these two queries?
Both queries should have an exists filter to limit the response to only those documents where the qid_3 exists (is not null). For your first query you could try a terms aggregation. For your second query, you can filter the source to include only the names in the response or store the field and use fields.
1)
{
"size": 0,
"filter" : {
"exists" : { "field" : "quid_3" }
},
"aggs" : {
"group_by_category" : {
"terms" : { "field" : "qid_2" }
}
}
}
2)
{
"filter" : {
"exists" : { "field" : "quid_3" }
},
"_source": [ "qid_1"]
}