Is there any solution for searching exact word and containing word both in elasticsearch - node.js

Question

index: process.env.elasticSearchIndexName,
body: {
query: {
bool: {
must: [
{
match_phrase: {
title: `${searchKey}`,
},
},
],
},
},
},
from: (page || constants.pager.page),
size: (limit || constants.pager.limit),
i am using above method but problem in that is it only search exact matched words in whole text.
it can't search containing word.. for example if title = "sweatshirt" than if i type word "shirt" it should come the result but currently not got the result using above method

Answer 1

Standard analyzer(default analyzer if none is specified) breaks texts in tokens.
For sentence "this is a test" tokens generated are [this,is,a,test]
Match_pharse query breaks text in tokens using same analyzer as indexing analyzer and returns documents which 1. contain all the tokens 2. tokens appear in same order.
Since you text is sweatshirt there is single token in inverted index for it "sweatshirt" which will not match with either sweat or shirt
NGram tokenizer
The ngram tokenizer first breaks text down into words whenever it encounters one of a list of specified characters, then it emits N-grams of each word of the specified length
Mapping
PUT my_index
{
"settings": {
"analysis": {
"analyzer": {
"my_analyzer": {
"tokenizer": "my_tokenizer"
}
},
"tokenizer": {
"my_tokenizer": {
"type": "ngram",
"min_gram": 3,
"max_gram": 3,
"token_chars": [
"letter",
"digit"
]
}
}
}
},
"mappings": {
"properties": {
"text":{
"type": "text",
"analyzer": "my_analyzer"
}
}
}
}
Query:
{
"query": {
"match": {
"text": "shirt"
}
}
}
If you will run _analyze query
GET my_index/_analyze
{
"text": ["sweatshirt"],
"analyzer": "my_analyzer"
}
you will see below token are generated for the text sweatshirt. Size of tokens can be adjusted using min_gram and max_gram
{
"tokens" : [
{
"token" : "swe",
"start_offset" : 0,
"end_offset" : 3,
"type" : "word",
"position" : 0
},
{
"token" : "wea",
"start_offset" : 1,
"end_offset" : 4,
"type" : "word",
"position" : 1
},
{
"token" : "eat",
"start_offset" : 2,
"end_offset" : 5,
"type" : "word",
"position" : 2
},
{
"token" : "ats",
"start_offset" : 3,
"end_offset" : 6,
"type" : "word",
"position" : 3
},
{
"token" : "tsh",
"start_offset" : 4,
"end_offset" : 7,
"type" : "word",
"position" : 4
},
{
"token" : "shi",
"start_offset" : 5,
"end_offset" : 8,
"type" : "word",
"position" : 5
},
{
"token" : "hir",
"start_offset" : 6,
"end_offset" : 9,
"type" : "word",
"position" : 6
},
{
"token" : "irt",
"start_offset" : 7,
"end_offset" : 10,
"type" : "word",
"position" : 7
}
]
}
Warning:Ngrams increase the size of the inverted index so use with appropriate value of min_gram and max_gram
Another option is to use wildcard query. For wildcard all the documents have to scanned to check if text matches the pattern. They have low performance.
When using wildcard search on not_analyzed fields in case you want to include whitespace ex text.keyword
{
"query": {
"wildcard": {
"text": {
"value": "*shirt*"
}
}
}
}