# most_fields 策略 cross-fields 搜索弊端
cross-fields （跨字段）搜索：一个唯一标识，跨了多个 field。比如一个人，标识，是姓名； 一个建筑，它的标识是地址。姓名可以散落在多个 field 中，比如 first_name 和 last_name 中， 地址可以散落在 country、province、city中。
跨多个 field 搜索一个标识，比如搜索一个人名，或者一个地址，就是 cross-fields 搜索
初步来说，如果要实现，可能用 most_fields 比较合适。因为 best_fields 是优先搜索单个 field 最匹配的结果， cross-fields 本身就不是一个 field 的问题了。
增加字段数据
POST /forum/article/_bulk
{ "update": { "_id": "1"} }
{ "doc" : {"author_first_name" : "Peter", "author_last_name" : "Smith"} }
{ "update": { "_id": "2"} }
{ "doc" : {"author_first_name" : "Smith", "author_last_name" : "Williams"} }
{ "update": { "_id": "3"} }
{ "doc" : {"author_first_name" : "Jack", "author_last_name" : "Ma"} }
{ "update": { "_id": "4"} }
{ "doc" : {"author_first_name" : "Robbin", "author_last_name" : "Li"} }
{ "update": { "_id": "5"} }
{ "doc" : {"author_first_name" : "Tonny", "author_last_name" : "Peter Smith"} }
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查询
GET /forum/article/_search
{
"query": {
"multi_match": {
"query": "Peter Smith",
"type": "most_fields",
"fields": [ "author_first_name", "author_last_name" ]
}
}
}
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响应结果
{
"took": 118,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"failed": 0
},
"hits": {
"total": 3,
"max_score": 0.6931472,
"hits": [
{
"_index": "forum",
"_type": "article",
"_id": "2",
"_score": 0.6931472,
"_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",
"author_first_name": "Smith",
"author_last_name": "Williams"
}
},
{
"_index": "forum",
"_type": "article",
"_id": "1",
"_score": 0.5753642,
"_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",
"author_first_name": "Peter",
"author_last_name": "Smith"
}
},
{
"_index": "forum",
"_type": "article",
"_id": "5",
"_score": 0.51623213,
"_source": {
"articleID": "DHJK-B-1395-#Ky5",
"userID": 3,
"hidden": false,
"postDate": "2019-01-28",
"tag": [
"elasticsearch"
],
"tag_cnt": 1,
"view_cnt": 10,
"title": "this is spark blog",
"content": "spark is best big data solution based on scala ,an programming language similar to java",
"sub_title": "haha, hello world",
"author_first_name": "Tonny",
"author_last_name": "Peter Smith"
}
}
]
}
}
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会发现 id=5 的 "author_last_name": "Peter Smith" 居然是排在最后面的，
我们想要的结果应该就是 id=5 这条数据了，造成这个结果的原因如下：
只是找到尽可能多的 field 匹配的 doc，而不是某个 field 完全匹配的 doc
most_fields 没办法用 minimum_should_match 去掉长尾数据，就是匹配的特别少的结果
TF/IDF算法，
比如 Peter Smith 和 Smith Williams，搜索 Peter Smith 的时候，由于 first_name 中很少有 Smith 的， 所以 query 在所有 document 中的频率很低，得到的分数很高，可能 Smith Williams 反而会排在 Peter Smith 前面
注意：该算法在本教程中有时候的解释并不是完全正确，只大概是这样