ES highlight not showing in results - elasticsearch

Can somebody tell me why these fields will not show up in my ES results (within the highlight object) in Sense when I know some of the sites I'm indexing into ES have content for these particular HTML elements?
ES mapping of the fields
"metatag.description": {
"type": "multi_field",
"fields": {
"metatag.description": {
"type": "string",
"index": "not_analyzed",
"include_in_all": false
},
"autocomplete": {
"type": "string",
"index_analyzer": "autocomplete_analyzer",
"search_analyzer": "whitespace_analyzer"
}
}
},
"metatag.keywords": {
"type": "multi_field",
"fields": {
"metatag.keywords": {
"type": "string",
"index": "not_analyzed",
"include_in_all": false
},
"autocomplete": {
"type": "string",
"index_analyzer": "autocomplete_analyzer",
"search_analyzer": "whitespace_analyzer"
}
}
},
For some reason only "title.autocomplete" is in the results?
This is my query:
GET /hugetestindex/doc/_search
{
"query": {
"match": {
"_all": {
"query": "ford mu",
"operator": "and"
}
}
},
"highlight": {
"fields": {
"title.autocomplete": {
"pre_tags" : ["<em>"],
"post_tags" : ["</em>"],
"number_of_fragments": 10,
"fragment_size": 300
},
"metatags.keywords.autocomplete": {
"pre_tags" : ["<em>"],
"post_tags" : ["</em>"],
"number_of_fragments": 10,
"fragment_size": 300
},
"metatags.description.autocomplete": {
"pre_tags" : ["<em>"],
"post_tags" : ["</em>"],
"number_of_fragments": 10,
"fragment_size": 300
}
}
}
}
Where am I going wrong?

Related

Elasticsearch query for all values of field with group by

i am having trouble forming query to fetch all values with sql group by kind of thing.
so below is my data structure:
product index:
{
"createdBy" : "61c1fcdd88dbad1920da8caf",
"creationTime" : "2021-12-22T11:58:53.576932Z",
"lastModifiedBy" : "61c1fcdd88dbad1920da8caf",
"lastModificationTime" : "2021-12-22T11:58:53.576932Z",
"id" : "61c312fdc6aa620a609db0b2",
"title" : "string",
"brand" : "string",
"longDesc" : "string",
"categoryId" : "string",
"imageUrls" : [
"string",
"string"
],
"keySpecs" : [
"string",
"string",
],
"facets" : [
{
"name" : "color",
"value" : "red"
},
{
"name" : "storage",
"value" : "16 GB"
},
{
"name" : "brand",
"value" : "Intex"
}
],
"categoryName" : "handsets"
}
Now, i want to fetch all the facets with their different values and count as well. Let's say
productA has color blue, productB has color red
productA has brand ABC, productB has brand XYZ
so, i want data which list all facets like:
color: blue(200 count), red (12 count)
brand: ABC(13 count), XYZ (99 count)
Also, different product will have different type of facet, like iphone will have color memory brand size, but a pen will have color and brand only (not memory/size).
Note: i'm using latest version of elastic
=================
UPDATE 1:
Below is the es mapping details
{
"settings": {
"analysis": {
"filter": {
"english_stop": {
"type": "stop",
"stopwords": "_english_"
},
"english_keywords": {
"type": "keyword_marker",
"keywords": [
"example"
]
},
"english_stemmer": {
"type": "stemmer",
"language": "english"
},
"english_possessive_stemmer": {
"type": "stemmer",
"language": "possessive_english"
}
},
"analyzer": {
"lalashree_standard_analyzer": {
"tokenizer": "standard",
"filter": [
"english_possessive_stemmer",
"lowercase",
"english_stop",
"english_keywords",
"english_stemmer"
]
},
"html_standard_analyzer": {
"char_filter": [
"html_strip"
],
"tokenizer": "standard",
"filter": [
"english_possessive_stemmer",
"lowercase",
"english_stop",
"english_keywords",
"english_stemmer"
]
}
}
}
},
"mappings": {
"properties": {
"id": {
"type": "keyword"
},
"createdBy": {
"type": "keyword"
},
"creationTime": {
"type": "date"
},
"lastModifiedBy": {
"type": "keyword"
},
"lastModificationTime": {
"type": "date"
},
"deleted": {
"type": "boolean"
},
"deletedBy": {
"type": "keyword"
},
"deletionTime": {
"type": "date"
},
"title": {
"type": "text",
"analyzer": "lalashree_standard_analyzer",
"fields": {
"suggest": {
"type": "completion"
}
}
},
"shortDesc": {
"type": "text",
"analyzer": "lalashree_standard_analyzer"
},
"longDesc": {
"type": "text",
"analyzer": "lalashree_standard_analyzer"
},
"categoryId": {
"type": "keyword"
},
"searchDetails": {
"type": "object",
"properties": {
"desc": {
"type": "text",
"analyzer": "lalashree_standard_analyzer"
},
"keywords": {
"type": "text",
"analyzer": "lalashree_standard_analyzer",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
}
}
},
"imageUrls": {
"type": "keyword",
"index": false
},
"keySpecs": {
"type": "text",
"analyzer": "lalashree_standard_analyzer"
},
"sections": {
"type": "object",
"properties": {
"name": {
"type": "text",
"index": false
},
"shortDesc": {
"type": "text",
"analyzer": "lalashree_standard_analyzer"
},
"longDesc": {
"type": "text",
"analyzer": "lalashree_standard_analyzer"
},
"htmlContent": {
"type": "text",
"analyzer": "html_standard_analyzer"
}
}
},
"facets": {
"type": "nested",
"properties": {
"name": {
"type": "keyword"
},
"value": {
"type": "keyword"
}
}
},
"specificationItems": {
"type": "object",
"properties": {
"key": {
"type": "text",
"analyzer": "lalashree_standard_analyzer",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"values": {
"type": "text",
"analyzer": "lalashree_standard_analyzer"
}
}
},
"categoryName": {
"type": "keyword"
},
"productFamily": {
"type": "nested",
"properties": {
"id": {
"type": "keyword"
},
"familyVariantOptions": {
"type": "nested",
"properties": {
"name": {
"type": "keyword"
},
"values": {
"type": "keyword"
}
}
},
"productFamilyItems": {
"type": "nested",
"properties": {
"baseProductId": {
"type": "keyword"
},
"itemVariantInfoSet": {
"type": "nested",
"properties": {
"name": {
"type": "keyword"
},
"value": {
"type": "keyword"
}
}
}
}
}
}
},
"rating": {
"type": "float"
},
"totalReviewsCount": {
"type": "long"
},
"stores": {
"type": "nested",
"properties": {
"id": {
"type": "keyword"
},
"logo": {
"type": "keyword",
"index": false
},
"active": {
"type": "boolean"
},
"name": {
"type": "text"
},
"quantity": {
"type": "long"
},
"rating": {
"type": "float"
},
"totalReviewsCount": {
"type": "long"
},
"price.mrp": {
"type": "float"
},
"price.sp": {
"type": "float"
},
"location.geoPoint": {
"type": "geo_point"
},
"oos": {
"type": "boolean"
}
}
}
}
}
}

This query first group by names then groups each name's values. By setting sizes, you can arrange number of facets you want and number of items in each facet. I think it does what you need.
Note that if you have too many documents and if performance matters, this query may perform bad.
{
"size": 0,
"aggs": {
"facets": {
"nested": {
"path": "facets"
},
"aggs": {
"names": {
"terms": {
"field": "facets.name",
"size": 10
},
"aggs": {
"values": {
"terms": {
"field": "facets.value",
"size": 10
}
}
}
}
}
}
}
}

Unable to get similar results with a filter applied specific field

I am working on Elastic Search 6.4.2. I am uploading my index and mapping and my searchable fields are title,content and I want to filter the results by "test" field. The values in the test fields are abce, ghij, klmn. I want to filter the results with field "test" and value "ghij".
PUT /test_index
{
"settings": {
"index": {
"number_of_shards": 4,
"number_of_replicas": 1,
"refresh_interval": "60s",
"analysis" : {
"analyzer" : {
"my_analyzer" : {
"tokenizer" : "standard",
"filter" : ["standard", "lowercase", "my_snow"]
},
"blogs_analyzer": {
"type": "stop",
"stopwords": "_english_"
}
} ,
"filter" : {
"my_snow" : {
"type" : "snowball",
"language" : "Lovins"
}
}
}
}
},
"mappings": {
"doc": {
"_source": {
"enabled": true
},
"properties": {
"content": {
"type": "text",
"index": "true",
"store": true,
"analyzer":"my_analyzer",
"search_analyzer": "my_analyzer"
},
"host": {
"type": "keyword",
"index": "true",
"store": true
},
"title": {
"type": "text",
"index": "true",
"store": true,
"analyzer":"my_analyzer",
"search_analyzer": "my_analyzer"
},
"url": {
"type": "text",
"index": "true",
"store": true
},
"test": {
"type": "keyword",
"index": "true",
"store": true
}
}
}
}
}
I tried with the below body in order to get the results.
POST test_index/_search
{
"query": {
"bool": {
"should": [{
"match": {
"content": {
"query": "sports"
}
}
},
{
"match": {
"title": {
"query": "sports"
}
}
}
],
"filter": {
"bool": {
"must": [{
"term": {
"test": "ghij"
}
}]
}
}
}
}
}
If I send the above request I am getting lesser records and I use send direct request GET /test_index/_search?q=sports I am getting more number of results

Elasticsearch - How to provide custom synonyms when querying?

I'm developping a search engine for my client which has to use synonym expansion. I can properly setup my index with a synonym token filter and a custom file (synonym.txt).
Example: ipod, i-pod, i pod
However, whenever we want a synonym expansion, I get the synonyms from Elasticsearch and display them as tags on the website.
Each tag can be unselected. In this case, how can we specify while querying to Elasticsearch, to use a different set of synonyms, not coming from the synonym.txt file ?
Example: if the user is looking for the term ipod, then I will show these two tags: i-pod, i pod. But if the user chooses to unselect "i-pod", I would like to be able to specify that only "i pod" is a synonym of "ipod" while querying.
My index settings are :
{
"settings": {
"analysis": {
"filter": {
"elision": {
"type": "elision",
"articles": ["l", "m", "t", "qu", "n", "s", "j", "d", "c", "jusqu", "quoiqu", "lorsqu", "puisqu"]
},
"french_stop": {
"type": "stop",
"stopwords": "_french_"
},
"french_stemmer": {
"type": "stemmer",
"language": "light_french"
},
"synonymsFilter": {
"type" : "synonym",
"synonyms_path" : "analysis/synonym.txt"
},
"autocompleteFilter": {
"max_shingle_size": "5",
"min_shingle_size": "2",
"type": "shingle"
}
},
"analyzer": {
"default": {
"tokenizer": "letter",
"filter": ["asciifolding", "lowercase", "french_stemmer", "elision", "french_stop"]
},
"auto-complete-suggester": {
"filter": [
"lowercase",
"autocompleteFilter"
],
"char_filter": [
"html_strip"
],
"type": "custom",
"tokenizer": "standard"
},
"did-you-mean-suggester": {
"tokenizer": "standard",
"filter": ["asciifolding", "lowercase"]
},
"synonym_analyzer" : {
"tokenizer" : "whitespace",
"filter" : ["synonymsFilter"]
},
"synonym_analyzer2": {
"tokenizer": "standard",
"filter": ["asciifolding", "lowercase", "french_stop", "autocompleteFilter"]
}
}
}
},
"mappings": {
"companies": {
"date_detection": "false",
"properties": {
"auto_complete": {
"type": "string",
"analyzer": "auto-complete-suggester",
"term_vector" : "yes"
},
"did_you_mean": {
"type": "string",
"analyzer": "did-you-mean-suggester",
"term_vector" : "yes"
},
"synonyms": {
"type": "string",
"analyzer": "synonym_analyzer",
"term_vector" : "yes"
},
"company_name": {
"type": "string",
"fields": {
"raw": { "type": "string", "index": "not_analyzed" },
"suggester": { "type": "string", "analyzer": "did-you-mean-suggester" }
},
"term_vector" : "yes",
"copy_to": [
"did_you_mean",
"auto_complete",
"synonyms"
]
},
"siren": {
"type": "string",
"fields": {
"raw": { "type": "string", "index": "not_analyzed" },
"suggester": { "type": "string", "analyzer": "did-you-mean-suggester" }
}
},
"CPposteEntreprise": {
"type": "string",
"fields": {
"raw": { "type": "string", "index": "not_analyzed" },
"suggester": { "type": "string", "analyzer": "did-you-mean-suggester" }
}
},
"commercial_company_name": {
"type": "string",
"fields": {
"raw": { "type": "string", "index": "not_analyzed" },
"suggester": { "type": "string", "analyzer": "did-you-mean-suggester" }
},
"term_vector" : "yes",
"copy_to": [
"did_you_mean",
"auto_complete",
"synonyms"
]
},
"year_creation_company": {
"type": "long"
},
"month_creation_company": {
"type": "long"
},
"month_year_creation_company": {
"type": "date",
"format": "yyyyMM",
"fields": {
"raw": { "type": "string", "index": "not_analyzed" }
}
},
"city_company": {
"type": "string",
"fields": {
"suggester": { "type": "string", "analyzer": "did-you-mean-suggester" }
},
"term_vector" : "yes",
"copy_to": [
"did_you_mean",
"auto_complete"
]
},
"departement_company": {
"type": "string",
"fields": {
"raw": { "type": "string", "index": "not_analyzed" },
"suggester": { "type": "string", "analyzer": "did-you-mean-suggester" }
},
"term_vector" : "yes",
"copy_to": [
"did_you_mean",
"auto_complete"
]
},
"region_company": {
"type": "string",
"fields": {
"raw": { "type": "string", "index": "not_analyzed" },
"suggester": { "type": "string", "analyzer": "did-you-mean-suggester" }
},
"term_vector" : "yes",
"copy_to": [
"did_you_mean",
"auto_complete"
]
},
"is_excellence": {
"type": "string",
"fields": {
"raw": { "type": "string", "index": "not_analyzed" },
"suggester": { "type": "string", "analyzer": "did-you-mean-suggester" }
}
},
"interlocuteurs": {
"type": "string",
"fields": {
"raw": { "type": "string", "index": "not_analyzed" },
"suggester": { "type": "string", "analyzer": "did-you-mean-suggester" }
},
"term_vector" : "yes",
"copy_to": [
"did_you_mean",
"auto_complete"
]
},
"flag_entreprise_finance": {
"type": "string",
"fields": {
"raw": { "type": "string", "index": "not_analyzed" }
}
},
"flag_indirect": {
"type": "string",
"fields": {
"raw": { "type": "string", "index": "not_analyzed" }
}
},
"flag_direct": {
"type": "string",
"fields": {
"raw": { "type": "string", "index": "not_analyzed" }
}
},
"flag_investissement": {
"type": "string",
"fields": {
"raw": { "type": "string", "index": "not_analyzed" }
}
},
"montant_total_investissement": {
"type": "double",
"fields": {
"raw": { "type": "double", "index": "not_analyzed" }
}
},
"motant_total_finance": {
"type": "double",
"fields": {
"raw": { "type": "double", "index": "not_analyzed" }
}
},
"nombre_investissement": {
"type": "long",
"fields": {
"raw": { "type": "long", "index": "not_analyzed" }
}
},
"nombre_financement_accorde": {
"type": "long",
"fields": {
"raw": { "type": "long", "index": "not_analyzed" }
}
},
"caInterne": {
"type": "double",
"fields": {
"raw": { "type": "double", "index": "not_analyzed" }
}
},
"caExterne": {
"type": "double",
"fields": {
"raw": { "type": "double", "index": "not_analyzed" }
}
},
"caFiltre": {
"type": "double",
"fields": {
"raw": { "type": "double", "index": "not_analyzed" }
}
},
"effectif": {
"type": "double",
"fields": {
"raw": { "type": "double", "index": "not_analyzed" }
}
},
"textRank": {
"type": "string",
"fields": {
"raw": { "type": "string", "index": "not_analyzed" }
},
"term_vector" : "yes",
"copy_to": [
"synonyms"
]
},
"masterKeywords": {
"type": "nested",
"properties": {
"keyword": {
"type":"string",
"fields": {
"raw": { "type": "string", "index": "not_analyzed" }
},
"term_vector" : "yes",
"copy_to": [
"did_you_mean",
"auto_complete"
]
}
}
},
"dossiers":{
"type": "nested",
"date_detection": "false",
"properties": {
"dossierCommercial": {
"type": "long"
},
"sousDossierCommercial": {
"type": "long"
},
"historiqueProduitBPI": {
"type": "string"
},
"statutSousDossier": {
"type": "string"
},
"dateDecision": {
"type": "date",
"fields": {
"suggester": { "type": "string", "analyzer": "did-you-mean-suggester" },
"raw": { "type": "string", "index": "not_analyzed" }
}
},
"nomChargesAffaires": {
"type": "string",
"fields": {
"suggester": { "type": "string", "analyzer": "did-you-mean-suggester" }
}
},
"contactChargesAffaires": {
"type": "string",
"fields": {
"suggester": { "type": "string", "analyzer": "did-you-mean-suggester" }
}
},
"montantAide": {
"type": "double",
"fields": {
"suggester": { "type": "string", "analyzer": "did-you-mean-suggester" },
"raw": { "type": "string", "index": "not_analyzed" }
}
},
"contentValidation": {
"type": "string",
"fields": {
"suggester": { "type": "string", "analyzer": "did-you-mean-suggester" }
},
"term_vector" : "yes",
"copy_to": [
"did_you_mean",
"auto_complete",
"synonyms"
]
},
"contentDecision": {
"type": "string",
"fields": {
"suggester": { "type": "string", "analyzer": "did-you-mean-suggester" }
},
"term_vector" : "yes",
"copy_to": [
"did_you_mean",
"auto_complete",
"synonyms"
]
},
"contentDirectionEngagements": {
"type": "string",
"fields": {
"suggester": { "type": "string", "analyzer": "did-you-mean-suggester" }
},
"term_vector" : "yes",
"copy_to": [
"did_you_mean",
"auto_complete",
"synonyms"
]
},
"metaDomain": {
"type": "string",
"fields": {
"suggester": { "type": "string", "analyzer": "did-you-mean-suggester" }
}
},
"sousSecteur": {
"type": "string",
"fields": {
"suggester": { "type": "string", "analyzer": "did-you-mean-suggester" }
}
},
"keywords": {
"type": "string",
"fields": {
"suggester": { "type": "string", "analyzer": "did-you-mean-suggester" }
},
"term_vector" : "yes",
"copy_to": [
"did_you_mean",
"auto_complete"
]
},
"descriptionProjet": {
"type": "string",
"fields": {
"suggester": { "type": "string", "analyzer": "did-you-mean-suggester" }
},
"term_vector" : "yes",
"copy_to": [
"did_you_mean",
"auto_complete",
"synonyms"
]
}
}
},
"investissements": {
"type": "nested",
"date_detection": "false",
"properties": {
"flag_indirect": {
"type": "string",
"fields": {
"raw": { "type": "string", "index": "not_analyzed" }
}
},
"nom_societe_gestion_svi":{
"type": "string"
},
"date_entree_investissement":{
"type": "date",
"fields": {
"raw": { "type": "string", "index": "not_analyzed" }
}
},
"montant_investissement_df":{
"type": "double"
},
"description_projet_investissement":{
"type": "string",
"term_vector" : "yes",
"copy_to": [
"did_you_mean",
"auto_complete",
"synonyms"
]
}
}
},
"bilans":{
"type": "nested",
"date_detection": "false",
"properties": {
"bilanAnneeN": {
"properties": {
"effectif": {
"type": "long",
"fields": {
"raw": { "type": "long", "index": "not_analyzed" }
}
},
"capital": {
"type": "double"
},
"resultatNet": {
"type": "double"
},
"clotureDate": {
"type": "date"
},
"annee": {
"type": "long"
},
"ebeMoyen": {
"type": "double"
},
"caInterne": {
"type": "double",
"fields": {
"raw": { "type": "double", "index": "not_analyzed" }
}
},
"caExterne": {
"type": "double",
"fields": {
"raw": { "type": "double", "index": "not_analyzed" }
}
}
}
},
"bilanAnneeN1": {
"properties": {
"effectif": {
"type": "long",
"fields": {
"raw": { "type": "long", "index": "not_analyzed" }
}
},
"capital": {
"type": "double"
},
"resultatNet": {
"type": "double"
},
"clotureDate": {
"type": "date"
},
"annee": {
"type": "long"
},
"ebeMoyen": {
"type": "double"
},
"caInterne": {
"type": "double",
"fields": {
"raw": { "type": "double", "index": "not_analyzed" }
}
},
"caExterne": {
"type": "double",
"fields": {
"raw": { "type": "double", "index": "not_analyzed" }
}
}
}
},
"bilanAnneeN2": {
"properties": {
"effectif": {
"type": "long",
"fields": {
"raw": { "type": "long", "index": "not_analyzed" }
}
},
"capital": {
"type": "double"
},
"resultatNet": {
"type": "double"
},
"clotureDate": {
"type": "date"
},
"annee": {
"type": "long"
},
"ebeMoyen": {
"type": "double"
},
"caInterne": {
"type": "double",
"fields": {
"raw": { "type": "double", "index": "not_analyzed" }
}
},
"caExterne": {
"type": "double",
"fields": {
"raw": { "type": "double", "index": "not_analyzed" }
}
}
}
}
}
},
"news": {
"type": "nested",
"date_detection": "false",
"properties": {
"date": {
"type": "date",
"fields": {
"suggester": { "type": "string", "analyzer": "did-you-mean-suggester" },
"raw": { "type": "string", "index": "not_analyzed" }
}
},
"description": {
"type": "string",
"term_vector" : "yes",
"copy_to": [
"did_you_mean",
"auto_complete",
"synonyms"
]
},
"title": {
"type": "string"
},
"content": {
"type": "string",
"term_vector" : "yes",
"copy_to": [
"did_you_mean",
"auto_complete",
"synonyms"
]
},
"url": {
"type": "string"
},
"tags": {
"type": "string",
"term_vector" : "yes",
"copy_to": [
"did_you_mean",
"auto_complete"
]
},
"links": {
"type": "string"
},
"external_source": {
"type": "string"
}
}
}
}
}
}
}
For now, I am using a master field called "synonyms". Is this a good idea ?
Thanks in advance for your help.

elasticsearch nested range filter script

I have an elasticsearch range aggregation problem.
I have a nested object called "prices" in an nested "object" called "products".
in this sub nested object prices I have different prices for different countries and currencies. now I wanna use a range aggregation, but this ones loop over all price items and returns a big range aggregation.
now I want to use a script to filter curriencies and country price. but my if clause never got a return value.
"script": "if(doc['currency']=='GBP') { doc['price']; } else 0"
here is my code for query
"aggs": {
"products": {
"nested": {
"path": "products"
},
"aggs": {
"prices": {
"nested": {
"path": "products.prices"
},
"aggs": {
"range": {
"range": {
"field": "products.prices.price",
"script": "if(doc['currency']=='GBP') { doc['price']; } else 0",
"params": {
"currency": "GBP",
"country": "GB"
},
"ranges": [
{
"to": 50
},
{
"from": 50,
"to": 100
},
{
"from": 100
}
]
}
}
}
}
}
}
}
and my mapping
{
"settings": {
"index": {
"number_of_shards": 2,
"number_of_replicas": 1
},
"analysis": {
"filter": {
"nGram_filter": {
"type": "nGram",
"min_gram": 2,
"max_gram": 20,
"token_chars": ["letter", "digit", "punctuation", "symbol"]
}
},
"analyzer": {
"nGram_analyzer": {
"type": "custom",
"tokenizer": "whitespace",
"filter": ["lowercase", "asciifolding", "nGram_filter"]
},
"whitespace_analyzer": {
"type": "custom",
"tokenizer": "whitespace",
"filter": ["lowercase", "asciifolding"]
}
}
}
},
"mappings": {
"program": {
"properties": {
"title": {
"type": "string",
"fields": {
"raw": {
"type": "string",
"index": "not_analyzed"
}
}
},
"products": {
"type": "nested",
"store": true,
"index": "analyzed",
"fields": {
"raw": {
"type": "nested",
"index": "not_analyzed"
}
},
"properties": {
"sku": {
"type": "string",
"store": true,
"index": "analyzed",
"fields": {
"raw": {
"type": "string",
"index": "not_analyzed"
}
}
},
"prices": {
"type": "nested",
"store": true,
"index": "analyzed",
"fields": {
"raw": {
"type": "nested",
"index": "not_analyzed"
}
},
"properties": {
"price": {
"type": "float",
"store": true,
"index": "analyzed",
"null_value": 0,
"analyzer": "english",
"fields": {
"raw": {
"type": "float",
"index": "not_analyzed"
}
}
},
"price2": {
"include_in_all": false,
"type": "float",
"store": true,
"index": "analyzed",
"null_value": 0,
"fields": {
"raw": {
"type": "float",
"index": "not_analyzed"
}
}
},
"vat": {
"include_in_all": false,
"type": "float",
"store": true,
"index": "analyzed",
"null_value": 0,
"fields": {
"raw": {
"type": "float",
"index": "not_analyzed"
}
}
},
"country": {
"include_in_all": false,
"type": "string",
"store": true,
"index": "analyzed",
"fields": {
"raw": {
"type": "string",
"index": "not_analyzed"
}
}
},
"currency": {
"include_in_all": false,
"type": "string",
"store": true,
"index": "analyzed",
"fields": {
"raw": {
"type": "string",
"index": "not_analyzed"
}
}
}
}
}
}
}
}
}
}
}

you can try this?
{
"filtered" : {
"query" : { "match_all" : {} },
"filter" : {
"nested" : {
"path" : "products",
"filter" : {
"bool" : {
"must" : [
{
"term" : {"prices.currency" : "GBP"}
},
{
"range" : {"range.count" : {"gt" : 5}}
}
]
}
},
"_cache" : true
}
}
}
}

Filtered is deprecated at this point for ElasticSearch and was replaced with bool. The new version of this would be the following:
{
"query" : {
"nested" : {
"path" : "products",
"query" : {
"bool" : {
"must" : [
{
"term" : {"prices.currency" : "GBP"}
},
{
"range" : {"range.count" : {"gt" : 5}}
}
]}
}
}
}
}
Here's a reference to the ElasticSearch documentation

Elasticsearch big data

I`m new in elasticsearch and I have problem.
I have 1 million rows of data and query result take too long.
Went I have 150k it was taking 0.5s , now is taking 10sec.
Each days, number of data is different (One day can be 150k, other 1 million and etc.)
I need advice how to make it faster.
Mapping
{
"mappings": {
"Jobs": {
"_ttl": {
"enabled": true,
"default": "1d"
},
"properties": {
"id": {
"type": "integer"
},
"advertiser_id": {
"type": "integer"
},
"company_id": {
"type": "integer"
},
"feed_id": {
"type": "integer"
},
"description_unique": {
"type": "string",
"fields": {
"raw": {
"type": "string",
"index": "not_analyzed"
}
}
},
"title": {
"type": "string",
"fields": {
"raw": {
"type": "string",
"index": "not_analyzed"
}
}
},
"city": {
"type": "string",
"fields": {
"raw": {
"type": "string",
"index": "not_analyzed"
}
}
},
"county": {
"type": "string",
"fields": {
"raw": {
"type": "string",
"index": "not_analyzed"
}
}
},
"country": {
"type": "integer"
},
"description": {
"type": "string",
"fields": {
"raw": {
"type": "string",
"index": "not_analyzed",
"store": true
}
}
},
"company": {
"type": "string",
"fields": {
"raw": {
"type": "string",
"index": "not_analyzed"
}
}
},
"url": {
"type": "string",
"fields": {
"raw": {
"type": "string",
"index": "not_analyzed"
}
}
},
"premium": {
"type": "integer"
},
"bid": {
"type": "integer"
},
"created": {
"type": "date",
"format": "dateOptionalTime",
"default": "basic_date"
},
"updated": {
"type": "date",
"format": "dateOptionalTime"
}
}
}
}
}
Query
{
"query": {
"bool": {
"must": [
{
"multi_match": {
"query": "Survey Developer",
"type": "best_fields",
"fields": [
"title",
"description"
],
"operator": "and"
}
}
]
}
},
"highlight": {
"boundary_chars": ".,!? \t\n",
"tag_schema": "styled",
"pre_tags": [
"<b>"
],
"post_tags": [
"</b>"
],
"fields": {
"description": {
"fragment_size": 200,
"number_of_fragments": 3
}
}
},
"sort": [
{
"premium": {
"order": "desc"
}
},
{
"bid": {
"order": "desc"
}
}
]
}
Server parameters:
CPU 1 vCPU
RAM 1 GB
System Disk 40 GB
Network 120 Mb/s

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