• 相信我们很多人做中文搜索的时候,在Github找了ik中分分词插件
• 然后建立mapping的时候,很自然的使用这样的参数(参照官方分词文档实例)

{         "properties": {             "title": {                 "type": "text",                 "analyzer": "ik_max_word",                 "search_analyzer": "ik_smart"             }         } } 

• 那么我们来看一下全部数据(打火车和火车两条数据)

curl 127.0.0.1:9200/test/_search | jq {   "took": 1,   "timed_out": false,   "_shards": {     "total": 1,     "successful": 1,     "skipped": 0,     "failed": 0   },   "hits": {     "total": {       "value": 2,       "relation": "eq"     },     "max_score": 1,     "hits": [       {         "_index": "test",         "_type": "_doc",         "_id": "Video_1",         "_score": 1,         "_source": {           "id": 1,           "title": "打火车"         }       },       {         "_index": "test",         "_type": "_doc",         "_id": "Video_2",         "_score": 1,         "_source": {           "id": 2,           "title": "火车"         }       }     ]   } } 

• 这时候我们开始搜索(打火车)

curl 127.0.0.1:9200/test/_search?q=打火车 | jq {   "took": 2,   "timed_out": false,   "_shards": {     "total": 1,     "successful": 1,     "skipped": 0,     "failed": 0   },   "hits": {     "total": {       "value": 2,       "relation": "eq"     },     "max_score": 0.21110919,     "hits": [       {         "_index": "test",         "_type": "_doc",         "_id": "Video_2",         "_score": 0.21110919,         "_source": {           "id": 2,           "title": "火车"         }       },       {         "_index": "test",         "_type": "_doc",         "_id": "Video_1",         "_score": 0.160443,         "_source": {           "id": 1,           "title": "打火车"         }       }     ]   } } 

• 这时候我们惊奇的发现火车的分值是0.21110919居然比打火车的0.160443还高

• 中间经过一路排查, 首先感谢https://github.com/mobz/elasticsearch-head插件, 让排查数据的时候减少很多操作.
• 之后查看文档分词结果就得知了答案

curl 127.0.0.1:9200/test/_doc/Video_1/_termvectors?fields=title | jq {   "_index": "test",   "_type": "_doc",   "_id": "Video_1",   "_version": 1,   "found": true,   "took": 0,   "term_vectors": {     "title": {       "field_statistics": {         "sum_doc_freq": 3,         "doc_count": 2,         "sum_ttf": 3       },       "terms": {         "打火": {           "term_freq": 1,           "tokens": [             {               "position": 0,               "start_offset": 0,               "end_offset": 2             }           ]         },         "火车": {           "term_freq": 1,           "tokens": [             {               "position": 1,               "start_offset": 1,               "end_offset": 3             }           ]         }       }     }   } } 

• 很惊奇的发现打火车被划分成打火和火车两个词, 所以这之中肯定有问题了(当然对于搜索引擎是没有问题的).
• 打火车文档中的火车得到了分值,但打火会使搜索得分下降, 导致火车文档的排名靠前
• 所以我决定把两个分词器设置成一样

{         "properties": {             "title": {                 "type": "text",                 "analyzer": "ik_smart",                 "search_analyzer": "ik_smart"             }         } } 

• 然后再看一下分词数据(这次分词的数据的确是我们预想的)

curl 127.0.0.1:9200/test/_doc/Video_1/_termvectors?fields=title | jq {   "_index": "test",   "_type": "_doc",   "_id": "Video_1",   "_version": 1,   "found": true,   "took": 0,   "term_vectors": {     "title": {       "field_statistics": {         "sum_doc_freq": 3,         "doc_count": 2,         "sum_ttf": 3       },       "terms": {         "打": {           "term_freq": 1,           "tokens": [             {               "position": 0,               "start_offset": 0,               "end_offset": 1             }           ]         },         "火车": {           "term_freq": 1,           "tokens": [             {               "position": 1,               "start_offset": 1,               "end_offset": 3             }           ]         }       }     }   } } 

• 这时我们再搜索一次数据排名, 看到得分值排名的确是我们想要的了.

curl  127.0.0.1:9200/test/_search?q=打火车 | jq {   "took": 1,   "timed_out": false,   "_shards": {     "total": 1,     "successful": 1,     "skipped": 0,     "failed": 0   },   "hits": {     "total": {       "value": 2,       "relation": "eq"     },     "max_score": 0.77041256,     "hits": [       {         "_index": "test",         "_type": "_doc",         "_id": "Video_1",         "_score": 0.77041256,         "_source": {           "id": 1,           "title": "打火车"         }       },       {         "_index": "test",         "_type": "_doc",         "_id": "Video_2",         "_score": 0.21110919,         "_source": {           "id": 2,           "title": "火车"         }       }     ]   } } 

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