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

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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