ES 搜索结果expalain 可以类似数据库性能调优来看排序算法的选择

When we run a simple

term

query with

explain

set to

true

(see Understanding the Score), you will see that the only factors involved in calculating the score are the ones explained in the preceding sections:

PUT /my_index/doc/1
{ "text" : "quick brown fox" }

GET /my_index/doc/_search?explain
{
"query": {
"term": {
"text": "fox"
}
}
}

The (abbreviated)

explanation

from the preceding request is as follows:

weight(text:fox in 0) [PerFieldSimilarity]:  0.15342641 

result of:
fieldWeight in 0                         0.15342641
product of:
tf(freq=1.0), with freq of 1:        1.0 

idf(docFreq=1, maxDocs=1):           0.30685282 

fieldNorm(doc=0):                    0.5 

	The final score for term fox in field text in the document with internal Lucene doc ID 0 .
	The term fox appears once in the text field in this document.
	The inverse document frequency of fox in the text field in all documents in this index.
	The field-length normalization factor for this field.

Of course, queries usually consist of more than one term, so we need a way of combining the weights of multiple terms. For this, we turn to the vector space model.

见：https://www.elastic.co/guide/en/elasticsearch/guide/current/scoring-theory.html