best-fields策略，主要是说将某一个field匹配尽可能多的关键词的doc优先返回回来 most-fields策略，顾名思义，就是匹配词干的字段数越多，分数越高，优先返回，也可设置权重boost。
下面是简易公式（详细评分算法请参考：http://m.blog.csdn.net/article/details?id=50623948）：
下面是简易公式（详细评分算法请参考：http://m.blog.csdn.net/article/details?id=50623948）：
score=match_field1_score*boost+match_field2_score*boost+...match_fieldN_score*boost
在很多情况下，这种搜索很有效，但存在一个弱点，就是当文档中的字段冗余信息过多，将会影响那些文档比较精炼，而且意思较为全面的分值，
不能使用operator和minimum_should_match来减少相关性低的doc的长尾问题，简单的来说就是按term匹配的个数取胜
添加一个field : sub_title 使用english analyzer，sub_title 的子field: std使用standard analyzer
POST /forum/_mapping/article
{
"properties": {
"sub_title": {
"type": "text",
"analyzer": "english",
"fields": {
"std": {
"type": "text",
"analyzer": "standard"
}
}
}
}
}
填充数据
POST /forum/article/_bulk
{ "update": { "_id": "1"} }
{ "doc" : {"sub_title" : "learning more courses"} }
{ "update": { "_id": "2"} }
{ "doc" : {"sub_title" : "learned a lot of course"} }
{ "update": { "_id": "3"} }
{ "doc" : {"sub_title" : "we have a lot of fun"} }
{ "update": { "_id": "4"} }
{ "doc" : {"sub_title" : "both of them are good"} }
{ "update": { "_id": "5"} }
{ "doc" : {"sub_title" : "haha, hello world"} }
响应结果
{
"took": 62,
"errors": false,
"items": [
{
"update": {
"_index": "forum",
"_type": "article",
"_id": "1",
"_version": 7,
"result": "updated",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"status": 200
}
},
{
"update": {
"_index": "forum",
"_type": "article",
"_id": "2",
"_version": 11,
"result": "updated",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"status": 200
}
},
{
"update": {
"_index": "forum",
"_type": "article",
"_id": "3",
"_version": 7,
"result": "updated",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"status": 200
}
},
{
"update": {
"_index": "forum",
"_type": "article",
"_id": "4",
"_version": 11,
"result": "updated",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"status": 200
}
},
{
"update": {
"_index": "forum",
"_type": "article",
"_id": "5",
"_version": 6,
"result": "updated",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"status": 200
}
}
]
}
搜索sub_title 包含learning courses的doc
doc1包含courses ，其余doc均不包含learning courses关键字
GET /forum/article/_search
{
"query": {
"match": {
"sub_title": "learning courses"
}
}
}
响应结果
{
"took": 7,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 1.219939,
"hits": [
{
"_index": "forum",
"_type": "article",
"_id": "2",
"_score": 1.219939,
"_source": {
"articleID": "KDKE-B-9947-#kL5",
"userID": 1,
"hidden": false,
"postDate": "2017-01-02",
"tag": [
"java"
],
"tag_cnt": 1,
"view_cnt": 50,
"title": "this is java blog",
"content": "i think java is the best programming language",
"sub_title": "learned a lot of course"
}
},
{
"_index": "forum",
"_type": "article",
"_id": "1",
"_score": 0.5063205,
"_source": {
"articleID": "XHDK-A-1293-#fJ3",
"userID": 1,
"hidden": false,
"postDate": "2017-01-01",
"tag": [
"java",
"hadoop"
],
"tag_cnt": 2,
"view_cnt": 30,
"title": "this is java and elasticsearch blog",
"content": "i like to write best elasticsearch article",
"sub_title": "learning more courses"
}
}
]
}
}
为什么doc2能被搜索出来，而且score还比doc1高？
我们可以看看learning coureses在enligsh analyzer下的分词
GET /forum/_analyze
{
"field": "sub_title",
"text": "learning coureses"
}
响应结果
{
"tokens": [
{
"token": "learn",
"start_offset": 0,
"end_offset": 8,
"type": "",
"position": 0
},
{
"token": "coures",
"start_offset": 9,
"end_offset": 17,
"type": "",
"position": 1
}
]
}
因为sub_title使用的是enligsh analyzer，将单词还原为其最基本的形态 learning --> learn learned --> learn courses --> course
sub_titile: learning coureses --> learn course
{ “doc” : {“sub_title” : “learned a lot of course”} }，就排在了{ “doc” : {“sub_title” : “learning more courses”} }的前面
使用most-fields策略
在"sub_title", "sub_title.std"中匹配learning courses越多的doc优先返回
GET /forum/article/_search
{
"query": {
"multi_match": {
"query": "learning courses",
"type": "most_fields",
"fields": [ "sub_title", "sub_title.std" ]
}
}
}
响应结果
{
"took": 4,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"failed": 0
},
"hits": {
"total": 2,
"max_score": 1.219939,
"hits": [
{
"_index": "forum",
"_type": "article",
"_id": "2",
"_score": 1.219939,
"_source": {
"articleID": "KDKE-B-9947-#kL5",
"userID": 1,
"hidden": false,
"postDate": "2017-01-02",
"tag": [
"java"
],
"tag_cnt": 1,
"view_cnt": 50,
"title": "this is java blog",
"content": "i think java is the best programming language",
"sub_title": "learned a lot of course"
}
},
{
"_index": "forum",
"_type": "article",
"_id": "1",
"_score": 1.012641,
"_source": {
"articleID": "XHDK-A-1293-#fJ3",
"userID": 1,
"hidden": false,
"postDate": "2017-01-01",
"tag": [
"java",
"hadoop"
],
"tag_cnt": 2,
"view_cnt": 30,
"title": "this is java and elasticsearch blog",
"content": "i like to write best elasticsearch article",
"sub_title": "learning more courses"
}
}
]
}
}
虽然仍然是doc2排在doc1的前面，但是发现doc1的score已经发生变化，这是因为在sub_title.std中doc2匹配了learning，提高了分数。
具体的分数怎么算出来的，很难说，因为这个东西很复杂， 还不只是TF/IDF算法。因为不同的query，不同的语法，都有不同的计算score的细节。
与best_fields的区别
（1）best_fields，是对多个field进行搜索，挑选某个field匹配度最高的那个分数，同时在多个query最高分相同的情况下，在一定程度上考虑其他query的分数。简单来说，你对多个field进行搜索，就想搜索到某一个field尽可能包含更多关键字的数据
优点：通过best_fields策略，以及综合考虑其他field，还有minimum_should_match支持，可以尽可能精准地将匹配的结果推送到最前面 缺点：除了那些精准匹配的结果，其他差不多大的结果，排序结果不是太均匀，没有什么区分度了
实际的例子：百度之类的搜索引擎，最匹配的到最前面，但是其他的就没什么区分度了
（2）most_fields，综合多个field一起进行搜索，尽可能多地让所有field的query参与到总分数的计算中来，此时就会是个大杂烩，出现类似best_fields案例最开始的那个结果，结果不一定精准，某一个document的一个field包含更多的关键字，但是因为其他document有更多field匹配到了，所以排在了前面；所以需要建立类似sub_title.std这样的field，尽可能让某一个field精准匹配query string，贡献更高的分数，将更精准匹配的数据排到前面
优点：将尽可能匹配更多field的结果推送到最前面，整个排序结果是比较均匀的 缺点：可能那些精准匹配的结果，无法推送到最前面
实际的例子：wiki，明显的most_fields策略，搜索结果比较均匀，但是的确要翻好几页才能找到最匹配的结果
