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。
POST /forum/_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"} }{ "update": { "_id": "6"} }{ "doc" : {"author_first_name" : "Tom", "author_last_name" : "Smith"} }{ "update": { "_id": "7"} }{ "doc" : {"author_first_name" : "June", "author_last_name" : "Lee"} }{ "update": { "_id": "8"} }{ "doc" : {"author_first_name" : "July", "author_last_name" : "Hoo"} }{ "update": { "_id": "9"} }{ "doc" : {"author_first_name" : "Peter", "author_last_name" : "Li"} }{ "update": { "_id": "10"} }{ "doc" : {"author_first_name" : "Summy", "author_last_name" : "Xi"} }{ "update": { "_id": "11"} }{ "doc" : {"author_first_name" : "Suny", "author_last_name" : "Xi"} }
GET /forum/_search
{
"query": {
"multi_match": {
"query": "Peter Smith",
"type": "most_fields",
"fields": [ "author_first_name", "author_last_name" ]
}
}
}
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，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，author_first_name field中，Smith就出现过1次
Peter Smith这个人，doc 1，Smith在author_last_name中，但是author_last_name出现了3次Smith，所以导致doc 1的IDF分数较低
不要有过多的疑问，一定是这样吗？
{
"took": 2,
"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": "2017-03-01",
"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"
}
}
]
}
}
问题1：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
问题2：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
问题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，可能Smith Williams反而会排在Peter Smith前面
以下结果是 ES7.x 中的，说明 ES7.x 做了优化：
{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 5,
"relation" : "eq"
},
"max_score" : 2.8442469,
"hits" : [
{
"_index" : "forum",
"_type" : "_doc",
"_id" : "1",
"_score" : 2.8442469,
"_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",
"author_first_name" : "Peter",
"author_last_name" : "Smith"
}
},
{
"_index" : "forum",
"_type" : "_doc",
"_id" : "5",
"_score" : 2.4696565,
"_source" : {
"articleID" : "XHDK-A-1293-#fJ5",
"userID" : 3,
"hidden" : false,
"postDate" : "2017-01-01",
"tag" : [
"Python",
"spark",
"flink"
],
"tag_cnt" : 3,
"view_cnt" : 70,
"title" : "this is python, spark, flink 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"
}
},
{
"_index" : "forum",
"_type" : "_doc",
"_id" : "2",
"_score" : 2.0794415,
"_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" : "_doc",
"_id" : "9",
"_score" : 1.5686159,
"_source" : {
"articleID" : "DHJK-B-1395-#Ky5",
"userID" : 3,
"hidden" : false,
"postDate" : "2020-06-01",
"tag" : [
"elasticsearch",
"flink"
],
"tag_cnt" : 1,
"view_cnt" : 90,
"title" : "this is elasticsearch and flink blog",
"content" : "elasticsearch and hadoop are all very good solution, i am a beginner",
"sub_title" : "the spark is good bigdata tool",
"author_first_name" : "Peter",
"author_last_name" : "Li"
}
},
{
"_index" : "forum",
"_type" : "_doc",
"_id" : "6",
"_score" : 1.2756311,
"_source" : {
"articleID" : "KDKE-B-9947-#kL1",
"userID" : 3,
"hidden" : false,
"postDate" : "2018-01-02",
"tag" : [
"java",
"flink"
],
"tag_cnt" : 5,
"view_cnt" : 50,
"title" : "this is java, flink blog",
"content" : "i like to write best elasticsearch article",
"sub_title" : "learn java",
"author_first_name" : "Tom",
"author_last_name" : "Smith"
}
}
]
}
}
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