十五、Elasticsearch使用most-fields策略进

1、cross-fields解释
crosss-fields搜索，一个唯一标志，跨了多个field。比如一个人，标识是姓名；一个建筑，他的标识是地址。姓名可以散落在多个 field中，比如first-name和last-name；地址可以散落在country，province和city中。

跨多个field搜索一个标识，比如搜索一个人名或地址就是cross-fields搜索

初步来讲，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本身就不是单field，而是多field搜索。

2、数据准备

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"} }

3、实战

GET /forum/article/_search
{
  "query": {
    "multi_match": {
      "query": "Peter Smith",
      "type": "most_fields", 
      "fields": ["author_first_name", "author_last_name"]
    }
  }
}

结果

{
  "took": 7,
  "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"
        }
      }
    ]
  }
}

结果score解释
按照我们理想的结果来看，应该是现在结果的倒序排序才合理。
为什么会这样呢？
因为Perter Smith，匹配author_first_name，匹配到了Smith。他在所有doc中author_first_name只出现了一次Smith，所以根据IDF算法，他的分数较高。
Peter Smith这个人，doc1中,Smith在author_last_name中，但是author_last_name出现了两次Smith，所以导致doc1的IDF分数较低。

4、问题（弊端）：
（1）只是找到尽可能多的field匹配doc，而不是某个field完全匹配的doc

（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）。

（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

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