Analiz edilen sorgulardan birisidir. Açıklaması şöyle
match queryThe standard query for performing full text queries, including fuzzy matching and phrase or proximity queries.
Açıklaması şöyle.
Creates a boolean query that returns results if the search term is present in the field.
Term queries vs Match query
Açıklaması şöyle
Term-level queries are not analyzed. The match queries that work on text fields, on the other hand, are analyzed. The same analyzers used during the indexing process (unless search queries were explicitly defined with different analyzers) process the search words in match queries. If a standard analyzer (default analyzer) is used during the indexing of our document, the search words are analyzed using the same standard analyzer before the search is executed.Additionally, the standard analyzer applies the same lowercase token filter (remember, the lowercase token filter is applied during the indexing) to the search words. Thus, if you provide the search keywords as uppercased, they are converted to lowercase letters and searched against the inverted index. For example, if we change the titlevalue to use uppercase criteria such as "title”: “JAVA”, for example, and rerun the query, the results are the same as the search query in listing 10.4. If you change the title value to lowercase or in any other way (e.g., java, jaVA, etc.), the query still returns the same results.
Standard analyzer
Açıklaması şöyle. Yani her kelimeyi küçük harfe çevirir.
Additionally, the standard analyzer applies the same lowercase token filter (remember, the lowercase token filter is applied during the indexing) to the search words. Thus, if you provide the search keywords as uppercased, they are converted to lowercase letters and searched against the inverted index. For example, if we change the title value to use uppercase criteria such as "title”: “JAVA”, for example, and rerun the query, the results are the same as the search query in listing 10.4. If you change the title value to lowercase or in any other way (e.g., java, jaVA, etc.), the query still returns the same results.
Söz dizimi
Kısa Form
Söz dizimi şöyle
GET books/_search
{
"query": {
"match": {
"FIELD": "SEARCH TEXT"
}
}
}Uzun Form
Söz dizimi şöyle
GET books/_search
{
"query": {
"match": {
"FIELD": {
"query":"<SEARCH TEXT>",
"<parameter>":"<MY_PARAM>",
}
}
}
}Açıklaması şöyle
As you can see in the snippet, the match query expects the search criteria to be defined in the form of a field value. The field can be any of the text fields present in a document, whose values are to be matched. The value can be a word or multiple words, given either as uppercase, lowercase, or camel case.
GET new_books,classics,top_sellers, crime* /_search
{
...
}Açıklaması şöyle
We can search across multiple indices by providing comma-separated indices in the search URLAs you can see, any number of indices can be provided when invoking the _search endpoint, including wildcards.Note : If we omit the index (or indices) in the search request, we effectively search the entire index. For example, GET _search{ ... } searches across all the indices in the cluster.
match Query Belirtilen Değerlerden Herhangi Birisi Varsa Eşleşir
match Query Or sorgusu olarak düşünülebilir. Sorgudaki tam kelimelerin herhangi birisinin belirtilen field'da olması durumunda doküman sonuca dahil edilir.
Örnek
Açıklaması şöyle
Keywords: “puerto baham”It will look for countries that have “puerto” or “baham” in their name, so it will return users from Puerto Rico and Bahamas, which is exactly what want.
Örnek
Elimizde şöyle bir arama olsun
GET books/_search
{
"query": {
"match": {
"title": {
"query": "Java Complete Guide"
}
}
},
"highlight": {
"fields": {
"title": {}
}
}
}Bu arama aslında şöyle Yani title alanın da Java veya Complete veya Guide geçen tüm kitapları döndürür
GET books/_search
{
"query": {
"match": {
"title": {
"query": "Java Complete Guide",
"operator": "OR"
}
}
}
}Bunu değiştirmek için şöyle yaparız
GET books/_search
{
"query": {
"match": {
"title": {
"query": "Java Complete Guide",
"operator": "AND"
}
}
}
}Örnek - minimum_should_matchattribute
Açıklaması şöyle
What if we want to find documents that match at least a few words from the given set of words? In the previous example, suppose we want at least two words out of three to match (say, Java and Guide, for example). This is where the minimum_should_matchattribute comes in handy.The minimum_should_matchattribute indicates the minimum number of words that should be used to match the documents.
Şöyle yaparız
GET books/_search
{
"query": {
"match": {
"title": {
"query": "Java Complete Guide",
"operator": "OR",
"minimum_should_match": 2
}
}
}
}Fuzzy Search
Açıklaması şöyle
Simply put, fuzziness is a mechanism to correct a user’s spelling mistakes in query criteria.Fuzziness makes character changes to string input so that it is the same as the string that may exist in the index. It employs the Levenshtein distance algorithm to fix incorrect spellings.A match query also allows us to add a fuzzinessparameter to fix spelling mistakes. We can set it as a numeric value, where the expected values are 0, 1, or 2, meaning none, one, or two character changes (insertions, deletions, modifications), respectively. In addition to setting these values, we also use an AUTO setting; we let the engine deal with the changes by setting AUTOas its fuzziness parameter.
Örnek
Şöyle yaparız
GET books/_search
{
"query": {
"match": {
"title": {
"query": "Kava",
"fuzziness": 1
}
}
}
}Tüm Alanlara Göre Aramak
Örnek
Şöyle yaparız
{“query”: { “match”: { “_all”: “meaning” } } }Açıklaması şöyle
...looks for the term “meaning” in all of the fields in all of the documents in your cluster.
Döndürülecek Alanları Belirtmek
Örnek
Şöyle yaparız
{“query”: {“match”: { “_all”: “meaning” }},“fields”: [“name”, “surname”, “age”],“from”: 100, “size”: 20}
Açıklaması şöyle
Here, we’re using the “fields” element to restrict which fields should be returned and the “from” and “size” elements to tell Elasticsearch we’re looking for documents 100 to 119 (starting at 100 and counting 20 documents).
Örnek - score
Elimizde şöyle bir sorgu olsun
GET /_search{"query" : {"match" : {"tweet" : "grow up"}}}
Çıktı olarak şunu alırız
{"took": 3,"timed_out": false,"_shards": {"total": 1,"successful": 1,"failed": 0},"hits": {"total": 2,"max_score": 1.9790175,"hits": [{"_index": "App3","_type": "tweets","_id": "2","_score": 1.9790175,"_source": {"name": "Katrina Kaif","age": 22,"tweet": "We never really grow up, we only learn how to act in public."}},{"_index": "App3","_type": "tweets","_id": "114","_score": 0.30432263,"_source": {"name": "Ajay Devgn","age": 62,"tweet": "Stress is when you wake up screaming and you realize you haven’t fallen asleep yet."}}]}}
Açıklaması şöyle
L2–8 shows meta information like it took 3ms for the query to return the result and some information about the shards.L9 onwards we see the actual query results.L10 We know that there are two matching results to the query.L11: We see the max relevance _score value as 1.979. This is followed by the two matching objects, the first with a _score value of 1.979 and the second with a _score value of 0.304. The drastic score difference is likely due to the fact that the second tweet doesn’t have “grow up” as a phrase. It only has the word “up”.
Hiç yorum yok:
Yorum Gönder