Why ElasticSearch is not able to search when special characters are available? - tokenize

Question

I have an ElasticSearch index with below configuration:
{
"my_ind": {
"settings": {
"index": {
"mapping": {
"total_fields": {
"limit": "10000000"
}
},
"number_of_shards": "3",
"provided_name": "my_ind",
"creation_date": "1539773409246",
"analysis": {
"analyzer": {
"default": {
"filter": [
"lowercase"
],
"type": "custom",
"tokenizer": "whitespace"
}
}
},
"number_of_replicas": "1",
"uuid": "3wC7i-E_Q9mSDjnTN2gxrg",
"version": {
"created": "5061299"
}
}
}
}
}
I want to search below content with plain search:
DL-1234170386456
This contents are available in the below field:
DNumber
This filed has mapping like below:
{
"DNumber": {
"type": "text",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
}
}
I am trying to implement it in JAVA language. I came across the ElasticSearch Analyzers and Tokenizers so I made use of "whitespace" tokenizer.
I am trying to search with below query:
{
"query": {
"multi_match": {
"query": "DL-1234170386456",
"fields": [
"_all"
],
"type": "best_fields",
"operator": "OR",
"analyzer": "default",
"slop": 0,
"prefix_length": 0,
"max_expansions": 50,
"lenient": false,
"zero_terms_query": "NONE",
"boost": 1
}
}
}
What wrong I am doing?

Answer 1

After doing lot of research and Trial & Error, found out the answer!
Some basic but important points:
We need to specify Analyzers and Tokenizers while creating/indexing the index/data.
In specified string i.e. "DL-1234170386456", special character (i.e. "-") is available and ElasticSearch is using by default Standard Analyzer.
Standard Analyzer contains Standard Tokenizer which is based on the Unicode Text Segmentation algorithm.
Actual Problem:
ElasticSearch is separating the String ("DL-1234170386456") into two different parts like "DL" and "1234170386456".
Solution:
We need to specify Whitespace Analyzer which contains Whitespace Tokenizer.
It will split the word whenever space is encountered. So, String ("DL-1234170386456") will kept as it is by ElasticSearch and we are able to find it out.