"function_score": { "query": { "match": { "title": "ES" } }, "field_value_factor": { "field": "like" } } } } "hits": [ { "_score": 1.6540513, //原本是0.16540512 "_source": { "title": "ES 最高难度", "like": 10 } }, { "_score": 1.0154619, //原本是0.20309238 ...
"relation" : "eq" }, "max_score" : 0.15120466, "hits" : [ { "_index" : "demo", "_type" : "_doc", "_id" : "narolH0Byb0W9gti_JAl", "_score" : 0.15120466, "_source" : { "content
结果: 可看到是按照score分数降序排序。 {"took":53,"timed_out":false,"_shards": {"total":16,"successful":16,"skipped":0,"failed":0},"hits": {"total": {"value":5,"relation":"eq"},"max_score":1.3862942,"hits": [ {"_index":"news","_type":"_doc","_id":"NQILB3QBfZgDx-...
3.最终结果的 score 分数即result_score=query_score*func_score,对应假设的三个搜索结果最终的 score 分别就是0.3 * 1 = 0.3、0.2 * 3 = 0.6、0.1 * 5 = 0.5,至此我们完成了新的打分过程,而搜索结果也会按照最终的 score 降序排列。 最终的分数result_score是由query_score与func_score进行计算而来,计算...
倒排索引的基础原理就是将文档进行分词(分词是指将文档按词拆分,如“倒排索引”可以分为“倒排”、“索引”、“倒”、“排”等词,英文则通常按单词划分),然后记录分词对应的文档,当用户搜索某个关键词时,ES会使用分词为每个文档“计分”(score),文档中该关键词出现的越多,得分就会越高,反之则越低,而文档分数...
GET /_search { "query": { "function_score": { "gauss": { "@timestamp": { "origin": "2013-09-17", "scale": "10d", "offset": "5d", "decay": 0.5 } } } } } 中心点是 2013-09-17 日期,scale 是 10d 意味着日期范围是 2013-09-12 到 2013-09-22 的文档分数权重是 1 ,日期...
score(q,d)=queryNorm(q)·coord(q,d)·∑(tf(tind)·idf(t)² · t.getBoost()·norm(t,d))(tinq) score(q,d)文档d对查询q的相关性得分queryNorm(q)查询的规范化因子coord(q,d)协调因子∑文档d的查询q中每个词t的权重之和tf(t in d)文档d中t词的词频(出现次数)idf(t)t词的逆文档频率...
索引总数量超过数千个，单索引采用单分片设计，单分片不超过50~70GB，属于官方建议的合理性范围。
es分数_score衰减函数 es分数_score衰减函数 1.按⽇期衰变 GET news/doc/_search { "query" : { "function_score": { "query": {"multi_match" : { "query" : "⽣物医药","fields": ["title"],"type":"phrase"} },"functions": [{ "gauss": { "pdate": { "origin": "2019-07-01"...