cross-fields搜索，一個唯一標識，跨了多個field。比如一個人，標識，是姓名；一個建筑，它的標識是地址。姓名可以散落在多個field中，比如first_name和last_name中，地址可以散落在country，province，city中。

跨多個field搜索一個標識，比如搜索一個人名，或者一個地址，就是cross-fields搜索

初步來說，如果要實現，可能用most_fields比較合適。因為best_fields是優先搜索單個field最匹配的結果，cross-fields本身就不是一個field的問題了。

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"} }

GET /forum/article/_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，這時候它的分數很高，為什么啊？？？
因為IDF分數高，IDF分數要高，那么這個匹配到的term（Smith），在所有doc中的出現頻率要低，author_first_name field中，Smith就出現過1次
Peter Smith這個人，doc 1，Smith在author_last_name中，但是author_last_name出現了兩次Smith，所以導致doc 1的IDF分數較低

不要有過多的疑問，一定是這樣嗎？

{
  "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"
        }
      }
    ]
  }
}

問題1：只是找到盡可能多的field匹配的doc，而不是某個field完全匹配的doc

問題2：most_fields，沒辦法用minimum_should_match去掉長尾數據，就是匹配的特別少的結果

問題3：TF/IDF算法，比如Peter Smith和Smith Williams，搜索Peter Smith的時候，由于first_name中很少有Smith的，所以query在所有document中的頻率很低，得到的分數很高，可能Smith Williams反而會排在Peter Smith前面