1、根据用户ID、是否隐藏、帖子ID、发帖日期来搜索帖子

(1)插入一些测试帖子数据

  1. POST /forum/_bulk
  2. { "index": { "_id": 1 }}
  3. { "articleID" : "XHDK-A-1293-#fJ3", "userID" : 1, "hidden": false, "postDate": "2017-01-01" }
  4. { "index": { "_id": 2 }}
  5. { "articleID" : "KDKE-B-9947-#kL5", "userID" : 1, "hidden": false, "postDate": "2017-01-02" }
  6. { "index": { "_id": 3 }}
  7. { "articleID" : "JODL-X-1937-#pV7", "userID" : 2, "hidden": false, "postDate": "2017-01-01" }
  8. { "index": { "_id": 4 }}
  9. { "articleID" : "QQPX-R-3956-#aD8", "userID" : 2, "hidden": true, "postDate": "2017-01-02" }

初步来说,就先搞4个字段,因为整个es是支持json document格式的,所以说扩展性和灵活性非常之好。如果后续随着业务需求的增加,要在document中增加更多的field,那么我们可以很方便的随时添加field。但是如果是在关系型数据库中,比如mysql,我们建立了一个表,现在要给表中新增一些column,那就很坑爹了,必须用复杂的修改表结构的语法去执行。而且可能对系统代码还有一定的影响。

GET /forum/_mapping
{
  "forum" : {
    "mappings" : {
      "properties" : {
        "articleID" : {
          "type" : "text",
          "fields" : {
            "keyword" : {
              "type" : "keyword",
              "ignore_above" : 256
            }
          }
        },
        "hidden" : {
          "type" : "boolean"
        },
        "postDate" : {
          "type" : "date"
        },
        "userID" : {
          "type" : "long"
        }
      }
    }
  }
}

现在es 5.2版本,type=text,默认会设置两个field,一个是field本身,比如articleID,就是分词的;还有一个的话,就是fields.keywordarticleID.keyword,默认不分词,会最多保留256个字符
(2)根据用户ID搜索帖子

GET /forum/_search
{
    "query" : {
        "constant_score" : { 
            "filter" : {
                "term" : { 
                    "userID" : 1
                }
            }
        }
    }
}

term filter/query:对搜索文本不分词,直接拿去倒排索引中匹配,你输入的是什么,就去匹配什么
比如说,如果对搜索文本进行分词的话,“helle world” --> “hello”“world”,两个词分别去倒排索引中匹配
term“hello world” --> “hello world”,直接去倒排索引中匹配“hello world”
(3)搜索没有隐藏的帖子

GET /forum/_search
{
    "query" : {
        "constant_score" : { 
            "filter" : {
                "term" : { 
                    "hidden" : false
                }
            }
        }
    }
}

(4)根据发帖日期搜索帖子

GET /forum/_search
{
    "query" : {
        "constant_score" : { 
            "filter" : {
                "term" : { 
                    "postDate" : "2017-01-01"
                }
            }
        }
    }
}

(5)根据帖子ID搜索帖子

GET /forum/_search
{
    "query" : {
        "constant_score" : { 
            "filter" : {
                "term" : { 
                    "articleID" : "XHDK-A-1293-#fJ3"
                }
            }
        }
    }
}
{
  "took": 1,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "failed": 0
  },
  "hits": {
    "total": 0,
    "max_score": null,
    "hits": []
  }
}
GET /forum/_search
{
    "query" : {
        "constant_score" : { 
            "filter" : {
                "term" : { 
                    "articleID.keyword" : "XHDK-A-1293-#fJ3"
                }
            }
        }
    }
}
{
  "took": 2,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "failed": 0
  },
  "hits": {
    "total": 1,
    "max_score": 1,
    "hits": [
      {
        "_index": "forum",
        "_type": "article",
        "_id": "1",
        "_score": 1,
        "_source": {
          "articleID": "XHDK-A-1293-#fJ3",
          "userID": 1,
          "hidden": false,
          "postDate": "2017-01-01"
        }
      }
    ]
  }
}

articleID.keyword,是es最新版本内置建立的field,就是不分词的。所以一个articleID过来的时候,会建立两次索引,一次是自己本身,是要分词的,分词后放入倒排索引;另外一次是基于articleID.keyword,不分词,保留256个字符最多,直接一个字符串放入倒排索引中。
所以term filter,对text过滤,可以考虑使用内置的field.keyword来进行匹配。但是有个问题,默认就保留256个字符。所以尽可能还是自己去手动建立索引,指定not_analyzed吧。在最新版本的es中,不需要指定not_analyzed也可以,将type=keyword即可。
(6)查看分词

GET /forum/_analyze
{
  "field": "articleID",
  "text": "XHDK-A-1293-#fJ3"
}

结果:

{
  "tokens" : [
    {
      "token" : "xhdk",
      "start_offset" : 0,
      "end_offset" : 4,
      "type" : "<ALPHANUM>",
      "position" : 0
    },
    {
      "token" : "a",
      "start_offset" : 5,
      "end_offset" : 6,
      "type" : "<ALPHANUM>",
      "position" : 1
    },
    {
      "token" : "1293",
      "start_offset" : 7,
      "end_offset" : 11,
      "type" : "<NUM>",
      "position" : 2
    },
    {
      "token" : "fj3",
      "start_offset" : 13,
      "end_offset" : 16,
      "type" : "<ALPHANUM>",
      "position" : 3
    }
  ]
}

默认是analyzedtext类型的field,建立倒排索引的时候,就会对所有的articleID分词,分词以后,原本的articleID就没有了,只有分词后的各个word存在于倒排索引中。
term,是不对搜索文本分词的,XHDK-A-1293-#fJ3 —> XHDK-A-1293-#fJ3;但是articleID建立索引的时候,XHDK-A-1293-#fJ3 --> xhdk,a,1293,fj3
(7)重建索引

DELETE /forum
PUT /forum
{
  "mappings": {
      "properties": {
        "articleID": {
          "type": "keyword"
        }
      }
  }
}
POST /forum/_bulk
{ "index": { "_id": 1 }}
{ "articleID" : "XHDK-A-1293-#fJ3", "userID" : 1, "hidden": false, "postDate": "2017-01-01" }
{ "index": { "_id": 2 }}
{ "articleID" : "KDKE-B-9947-#kL5", "userID" : 1, "hidden": false, "postDate": "2017-01-02" }
{ "index": { "_id": 3 }}
{ "articleID" : "JODL-X-1937-#pV7", "userID" : 2, "hidden": false, "postDate": "2017-01-01" }
{ "index": { "_id": 4 }}
{ "articleID" : "QQPX-R-3956-#aD8", "userID" : 2, "hidden": true, "postDate": "2017-01-02" }

(8)重新根据帖子ID和发帖日期进行搜索

GET /forum/_search
{
    "query" : {
        "constant_score" : {
            "filter" : {
                "term" : { 
                    "articleID" : "XHDK-A-1293-#fJ3"
                }
            }
        }
    }
}

2、梳理学到的知识点
(1)term filter:根据exact value进行搜索,数字、boolean、date天然支持
(2)text需要建索引时指定为not_analyzed,才能用term query, es6.x 之后只需要指定为 keyword 即可
(3)相当于SQL中的单个where条件

select *
from forum.article
where articleID='XHDK-A-1293-#fJ3'