I got fields like that:
names: ["Red:123", "Blue:45", "Green:56"]
it's mapping is
"names": {
"type": "keyword"
},
how could I search like this
{
"query": {
"match": {
"names": "red"
}
}
}
to get all the documents where red is in element of names array?
Now it works only with
{
"query": {
"match": {
"names": "red:123"
}
}
}
You can add multi fields OR just change the type to text, to achieve your required result
Index Mapping using multi fields
{
"mappings": {
"properties": {
"names": {
"type": "text",
"fields": {
"raw": {
"type": "keyword"
}
}
}
}
}
}
Adding a working example with index data, mapping, search query, and search result
Index Mapping:
{
"mappings":{
"properties":{
"names":{
"type":"text"
}
}
}
}
Index Data:
{
"names": [
"Red:123",
"Blue:45",
"Green:56"
]
}
Search Query:
{
"query": {
"match": {
"names": "red"
}
}
}
Search Result:
"hits": [
{
"_index": "64665127",
"_type": "_doc",
"_id": "1",
"_score": 0.2876821,
"_source": {
"names": [
"Red:123",
"Blue:45",
"Green:56"
]
}
}
]
Related
i want to make Elasticsearch index for something KEYWORDS, like.. hashtag.
and make synonym filter for KEYWORDs.
i think two ways indexing keyword, first is make keyword type.
{
"settings": {
"keywordField": {
"type": "keyword"
}
}
}
if make a index with League of Legends
maybe this.
{
"keywordField": ["leagueoflegends", "league", "legends", "lol" /* synonym */]
}
or text type:
{
"settings": {
"keywordField": {
"type": "text",
"analyzer": "lowercase_and_whitespace_and_synonym_analyzer"
}
}
}
maybe this.
{
"keywordField": ["league of legends"](synonym: lol => leagueoflegends)
}
if use _analyzer api for this field, expects "leagueoflegends", "league", "legends"
search query: 'lol', 'league of legends', 'League of Legends' have to match this field.
which practice is best?
Adding a working example with index data, mapping, search query, and search result. In the below example, I have taken two synonyms lol and leagueoflegends
Index Mapping:
{
"settings": {
"index": {
"analysis": {
"filter": {
"synonym_filter": {
"type": "synonym",
"synonyms": [
"leagueoflegends, lol"
]
}
},
"analyzer": {
"synonym_analyzer": {
"filter": [
"lowercase",
"synonym_filter"
],
"tokenizer": "standard"
}
}
}
}
},
"mappings": {
"properties": {
"keywordField": {
"type": "text"
}
}
}
}
Index Data:
{
"keywordField": ["leagueoflegends", "league", "legends"]
}
Search Query:
{
"query": {
"match": {
"keywordField": {
"query": "lol",
"analyzer": "synonym_analyzer"
}
}
}
}
Search Result:
"hits": [
{
"_index": "66872989",
"_type": "_doc",
"_id": "1",
"_score": 0.19363807,
"_source": {
"keywordField": [
"leagueoflegends",
"league",
"legends"
]
}
}
]
I have the following query:
{
size: 6,
query: {
multi_match: {
query,
type: 'bool_prefix',
fields: ['recommendation', 'recommendation._2gram', 'recommendation._3gram'],
},
},
highlight: {
fields: {
recommendation: {},
},
},
}
I want to add fuzziness: 1 to this query, but it has issues with the type: 'bool_prefix'. I need the type: 'bool_prefix to remain there b/c its integral to how the query works, but I'd also like to add some fuzziness to it. Any ideas?
As mentioned in the official ES documentation of bool_prefix
The fuzziness, prefix_length, max_expansions, fuzzy_rewrite, and
fuzzy_transpositions parameters are supported for the terms that are
used to construct term queries, but do not have an effect on the
prefix query constructed from the final term.
Adding a working example with index mapping, data, search query, and search result
Index Mapping:
{
"mappings": {
"properties": {
"recommendation": {
"type": "search_as_you_type",
"max_shingle_size": 3
}
}
}
}
Index Data:
{
"recommendation":"good things"
}
{
"recommendation":"good"
}
Search Query:
You can add fuzziness parameter with bool_prefix, as shown below
{
"size": 6,
"query": {
"multi_match": {
"query": "goof q",
"type": "bool_prefix",
"fields": [
"recommendation",
"recommendation._2gram",
"recommendation._3gram"
],
"fuzziness": 1
}
},
"highlight": {
"fields": {
"recommendation": {}
}
}
}
Search Result:
"hits": [
{
"_index": "65817192",
"_type": "_doc",
"_id": "2",
"_score": 1.1203322,
"_source": {
"recommendation": "good things"
},
"highlight": {
"recommendation": [
"<em>good</em> things"
]
}
},
{
"_index": "65817192",
"_type": "_doc",
"_id": "1",
"_score": 0.1583319,
"_source": {
"recommendation": "good"
},
"highlight": {
"recommendation": [
"<em>good</em>"
]
}
}
]
I ended up with additional fuzzy query combined with multi_match by bool. In your case it would look like this:
{
"size": 6,
"query": {
"bool": {
"should": [
{
"multi_match": {
"query": "goof q",
"type": "bool_prefix",
"fields": [
"recommendation",
"recommendation._2gram",
"recommendation._3gram"
]
}
},
{
"fuzzy": {
"nameSearch": {
"value": "goof q",
"fuzziness": "AUTO"
}
}
}
]
}
},
"highlight": {
"fields": {
"recommendation": {}
}
}
}
It was mapped like this:
"parent_ids": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
}
The actual data looks like this:
parent_ids = ["asdf", "aeraeg", "A123"]
I want to filter all products with parent_ids "A123":
"filter":
{
"match": {
"parent_ids": "{{parent_ids}}"
}
}
But not working
You can use terms query that returns documents that contain one
or more exact terms in a provided field.
Search Query:
{
"query": {
"terms": {
"parent_ids.keyword": [ "A123"]
}
}
}
Search Result:
"hits": [
{
"_index": "64745756",
"_type": "_doc",
"_id": "1",
"_score": 1.0,
"_source": {
"parent_ids": [
"asdf",
"aeraeg",
"A123"
]
}
}
]
Search Query using bool query:
{
"query": {
"bool": {
"filter": {
"match": {
"parent_ids": "A123"
}
}
}
}
}
If you need to format your query to support a JSON array in parameters, you'll need to format your query like this:
{
"terms": {
"parent_ids.keyword": {{#toJson}}parent_ids{{/toJson}}
}
}
Note that the match query doesn't support an array of values, only the terms query does.
I have a field called that is inside a nested field "name" that is a "Keyword" in elastic search.
Name field contains 2 values.
Jagannathan Rajagopalan
Rajagopalan.
If I query "Rajagopalan", I should get only the item #2.
If I query the complete Jagannathan Rajagopalan, I should get #1.
How do I achieve it?
You need to use the term query which is used for exact search. Added a working example according to your use-case.
Index mapping
{
"mappings": {
"properties": {
"name": {
"type": "nested",
"properties": {
"keyword": {
"type": "keyword"
}
}
}
}
}
}
Index sample docs
{
"name" : {
"keyword" : "Jagannathan Rajagopalan"
}
}
And another doc
{
"name" : {
"keyword" : "Jagannathan"
}
}
And search query
{
"query": {
"nested": {
"path": "name",
"query": {
"bool": {
"must": [
{
"match": {
"name.keyword": "Jagannathan Rajagopalan"
}
}
]
}
}
}
}
}
Search result
"hits": [
{
"_index": "key",
"_type": "_doc",
"_id": "2",
"_score": 0.6931471,
"_source": {
"name": {
"keyword": "Jagannathan Rajagopalan"
}
}
}
]
I would like to create a raw nested aggregation in ElasticSearch, but I'm enable to get it working.
My documents look like this :
{
"_index": "items",
"_type": "frame_spec",
"_id": "19770602001",
"_score": 1,
"_source": {
"item_type_name": "frame_spec",
"status": "published",
"creation_date": "2016-02-18T11:19:15Z",
"last_change_date": "2016-02-18T11:19:15Z",
"publishing_date": "2016-02-18T11:19:15Z",
"attributes": [
{
"brand": "Sun"
},
{
"model": "Sunglasses1"
},
{
"eyesize": "56"
},
{
"opc": "19770602001"
},
{
"madein": "UNITED KINGDOM"
}
]
}
}
What I want to do is to aggregate based on one of the attributes. I can't do a normal aggregation with "attributes.model" (for example) because some of them contain spaces. So I've tried using the "raw" property but it appears that ES considers it as a normal property and does not return any result.
This is what I've tried :
{
"size": 0,
"aggs": {
"brand": {
"terms": {
"field": "attributes.brand.raw"
}
}
}
}
But I have no result.
Have you any solution I could use for this problem ?
You should use a dynamic_template in your mapping that will catch all attributes.* string fields and create a raw sub-field for all of them. For other types than string, you don't really need raw fields. You need to delete your current index and then recreate it with this:
DELETE items
PUT items
{
"mappings": {
"frame_spec": {
"dynamic_templates": [
{
"strings": {
"match_mapping_type": "string",
"path_match": "attributes.*",
"mapping": {
"type": "string",
"fields": {
"raw": {
"type": "string",
"index": "not_analyzed",
"ignore_above": 256
}
}
}
}
}
]
}
}
}
After that, you need to re-populate your index and then you'll be able to run this:
POST /items/_search
{
"size": 0,
"aggs": {
"brand": {
"terms": {
"field": "attributes.brand.raw"
}
}
}
}