match all query elasticsearch

order to meet our expectations. But: {"Query": {"term ": {"name": "Cancer Medicine"} } } The results are still empty, we change the search conditions, enter "internal medicine", the resulting is still empty. However, when we enter any word in "swollen" or "oncology", we can get the result of "oncology", and at the same time, "Inside" and "section" will have all the data containing the word "I

This chapter is translated from the partial matching chapter of the official Elasticsearch guide.Instant Search during query (Query-time search-as-you-type)Now let's look at how prefix matching can help with full-text search. The user is accustomed to seeing the search results before completing the input-this is called an Instant Search (Instant search, or Search

[Elasticsearch] adjacent match (3)-performance, associated word query and ShinglesImprove Performance Phrase and closeness queries are more expensive than simple match queries. The match query only checks whether the entry exists

work exactly the same way as prefix queries. They also need to traverse the list of entries in the inverted index to find all the matching entries, and then collect the corresponding document IDs on a per-entry basis. The only difference between them and prefix queries is that they can support more complex schemas.This also means that there is the same risk of using them. It is very resource-intensive to run such queries on a field that contains many

This article is translated from the proximity matching chapter of the official Elasticsearch guide.Proximity matches (Proximity Matching)A standard full-text search using TF/IDF the document, or at least every field in the document, as a "big bag of words" (big bags of Words). The match query tells us if our search terms are included in this bag, but this is only

This article is translated from the proximity matching chapter of the official Elasticsearch guide.Proximity matches (Proximity Matching)A standard full-text search using TF/IDF the document, or at least every field in the document, as a "big bag of words" (big bags of Words). The match query tells us whether our search terms are included in this bag, but this is

gets the list of documents that contain the entry, in which case the document is 1 2 3 returned. Score each documenttermThe query calculates its relevance score for each matching document, which is calculated _score by taking into account the frequency of the entry (term Frequency) (the frequency of occurrences in the "quick" field of each document that matches title ), and the frequency of the rewind (inverted document Frequency) (the extent to

,"cityname": "温岭","description": "温岭是个好城市"}}The following verify the implementation of the weighted sub-query Search interface: GET http://localhost:8080/api/city/search?pageNumber=0pageSize=10searchContent= wenlingThe data will appear[{"id": 1,"provinceid": 1,"cityname": "温岭","description": "温岭是个好城市"},{"id": 2,"provinceid": 2,"cityname": "温州","description": "温州是个热城市"}]From the background Console can be seen, print out the corresponding DSL statement:

[Elasticsearch] adjacent match (2)-multi-value field, degree of closeness and relevanceMultivalue Fields) Using phrase matching on multi-value fields produces odd behavior: PUT /my_index/groups/1{ "names": [ "John Abraham", "Lincoln Smith"]} Run a phrase query for Abraham Lincoln: GET /my_index/groups/_search{ "query