从best-fields换成most-fields策略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，主要是说尽可能返回更多field匹配到某个关键词的doc，优先返回回来
POST /forum/_mapping{"properties": {"sub_title": {"type": "string","analyzer": "english","fields": {"std": {"type": "string","analyzer": "standard"}}}}}
POST /forum/_mapping
{
"properties": {
"sub_title": {
"type": "text",
"analyzer": "english",
"fields": {
"std": {
"type": "text",
"analyzer": "standard"
}
}
}
}
}
POST /forum/_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"} }
{ "update": { "_id": "6"} }
{ "doc" : {"sub_title" : "learn java"} }
{ "update": { "_id": "7"} }
{ "doc" : {"sub_title" : "learned a lot of hadoop"} }
{ "update": { "_id": "8"} }
{ "doc" : {"sub_title" : "learning of flink"} }
{ "update": { "_id": "9"} }
{ "doc" : {"sub_title" : "the spark is good bigdata tool"} }
{ "update": { "_id": "10"} }
{ "doc" : {"sub_title" : "haha, hello world"} }
{ "update": { "_id": "11"} }
{ "doc" : {"sub_title" : "flink, hello world"} }
GET /forum/_search
{
"query": {
"match": {
"sub_title": "learning courses"
}
}
}
{
"took": 3,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"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"
}
}
]
}
}
sub_title用的是enligsh analyzer，所以还原了单词
为什么，因为如果我们用的是类似于english analyzer这种分词器的话，就会将单词还原为其最基本的形态，stemmer
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
GET /forum/_search
{
"query": {
"match": {
"sub_title": "learning courses"
}
}
}
{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 5,
"relation" : "eq"
},
"max_score" : 2.3487744,
"hits" : [
{
"_index" : "forum",
"_type" : "_doc",
"_id" : "1",
"_score" : 2.3487744,
"_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 hadoop blog",
"content" : "i like to write best elasticsearch article",
"sub_title" : "learning more courses"
}
},
{
"_index" : "forum",
"_type" : "_doc",
"_id" : "2",
"_score" : 2.3487744,
"_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"
}
}
]
}
}
在 ES7.x 是{ "doc" : {"sub_title" : "learning more courses"} }，就排在了{ "doc" : {"sub_title" : "learned a lot of course"} }的前面，应该是 ES7.x 对分词做了优化，搜索的更加的进准
GET /forum/_search
{
"query": {
"multi_match": {
"query": "learning courses",
"type": "most_fields",
"fields": [ "sub_title", "sub_title.std" ]
}
}
}
{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 5,
"relation" : "eq"
},
"max_score" : 6.4124336,
"hits" : [
{
"_index" : "forum",
"_type" : "_doc",
"_id" : "1",
"_score" : 6.4124336,
"_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 hadoop blog",
"content" : "i like to write best elasticsearch article",
"sub_title" : "learning more courses"
}
},
{
"_index" : "forum",
"_type" : "_doc",
"_id" : "8",
"_score" : 2.6506605,
"_source" : {
"articleID" : "QQPX-R-3956-#aD0",
"userID" : 5,
"hidden" : true,
"postDate" : "2019-06-02",
"tag" : [
"spark",
"elasticsearch",
"flink"
],
"tag_cnt" : 7,
"view_cnt" : 80,
"title" : "this is spark, elasticsearch, flink blog",
"content" : "i am only an elasticsearch beginner",
"sub_title" : "learning of flink"
}
},
{
"_index" : "forum",
"_type" : "_doc",
"_id" : "2",
"_score" : 2.3487744,
"_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"
}
}
]
}
}
你问我，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
若有收获，就点个赞吧
0 人点赞
