cross_fields类型采用了一种以词条为中心(Term-centric)的方法，这种方法和best_fields及most_fields采用的以字段为中心(Field-centric)的方法有很大的区别。
它将所有的字段视为一个大的字段，然后在任一字段中搜索每个词条。
cross-fields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。
新增field
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"} }
响应结果
{
"took": 43,
"errors": false,
"items": [
{
"update": {
"_index": "forum",
"_type": "article",
"_id": "1",
"_version": 8,
"result": "updated",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"status": 200
}
},
{
"update": {
"_index": "forum",
"_type": "article",
"_id": "2",
"_version": 12,
"result": "updated",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"status": 200
}
},
{
"update": {
"_index": "forum",
"_type": "article",
"_id": "3",
"_version": 8,
"result": "updated",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"status": 200
}
},
{
"update": {
"_index": "forum",
"_type": "article",
"_id": "4",
"_version": 12,
"result": "updated",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"status": 200
}
},
{
"update": {
"_index": "forum",
"_type": "article",
"_id": "5",
"_version": 7,
"result": "updated",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"status": 200
}
}
]
}
搜索author_first_name或author_last_name叫Peter Smith的
GET /forum/article/_search
{
"query": {
"multi_match": {
"query": "Peter Smith",
"type": "most_fields",
"fields": [ "author_first_name", "author_last_name" ]
}
}
}
响应结果
{
"took": 0,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
"skipped": 0,
"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": "2021-11-11",
"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"
}
}
]
}
}
我们期望的结果可能是doc5排在第1，doc1排在第2，doc2排在第3.
但是结果却是doc2排在第1，doc1排在第2，doc5排在第3.
Peter Smith，匹配author_first_name，匹配到了Smith，这时候它的分数很高，为什么啊？？？ 因为IDF分数高，如果IDF分数要高，那么这个匹配到的term（Smith），在所有doc中的出现频率要低，在author_first_name field中，Smith就出现过1次。 doc 1，Smith在author_last_name中，但是author_last_name出现了两次Smith，所以导致doc 1的IDF分数较低
弊端1：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
弊端2：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
弊端3：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，导致Smith Williams反而会排在Peter Smith前面
