โฌ๏ธ Main Note
https://docs.google.com/document/d/17KKtCXe_nuQsuk9E_6050v01H_IAHBfYQtG11G06jas/edit
โ ๏ธ Warning โ ๏ธ Today's post contains a lot of images
๐ฟ Inverted Text
(์ญ์์ธ)
"Hello this is Monstershop. We are the best shop"
โค Every single words are tokenized, and they are now tokens.
By tokenizing, elasticsearch can quickly search the words from the context.
โค Faster than mysql (mysql searches in this format : "title":%Hello%)
โค Full-text search (efficient for searching a sentence)
โค "Hello" is saved as a token, so only need to get that "Hello" token.
๐ฟ Analyzer
โค Analyzes how the searching works
Default Anlayzers
< Character-Filter > โค removes things like ["!","~","@", "#"]
< Tokenizer > โค splits by spaces
< Token-Filter > โค Uppercase to lowercase
๐ฟ Elasticsearch Settings vs. Mappings
Settings:Analyzer, Tokenizer, Token-filter settings
Mappings: Set which analyzer the devloper wants to use for analyzing column
- For mappings, once the change is set, developer cannot commit any changes.
- But developer can add mappings.
๐ฟ Query [match / prefix]
โฌ๏ธ Match: Get the result that literally matches the user input(search word the use types in).
โฌ๏ธ Prefix: More like automatic complete search.
๐ฟ VS Code Practice
{
"template": "*",
"settings": {
"analysis": {
"analyzer": {
"tattoo_ngram_analyzer": {
"type": "custom",
"tokenizer": "tattoo_ngram_tokenizer",
"filter": ["lowercase", "my_stop_filter"]
}
},
"tokenizer": {
"tattoo_ngram_tokenizer": {
"type": "nGram",
"min_gram": "1",
"max_gram": "10"
}
},
"filter": {
"my_stop_filter": {
"type": "stop",
"stopwords": ["the", "in", "..."]
}
}
},
"max_ngram_diff": "20"
},
"mappings": {
"properties": {
"name": {
"type": "text"
},
"description": {
"type": "text",
"analyzer": "tattoo_ngram_analyzer"
},
"price": {
"type": "long"
}
}
}
}๐ฟ Applying to Nest.js
11๊ฐ์ ๋๊ธ
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