Elasticsearch Query API

创建索引，并插入数据

1.创建一个索引为users，类型为userinfo的indices

PUT /users/userinfo/1
{
    "first_name" : "John",
    "last_name" :  "Smith",
    "age" :        25,
    "about" :      "I love to go rock climbing",
    "interests": [ "sports", "music" ]
}

查询

精准查询

当进行进准查询时，我们会使用过滤器（filters）。过滤器的执行速度非常快，不会计算相关度（直接跳过整个评分阶段）而且很容易被缓存。

非文本查询

GET users/userinfo/_search
{
  "query": {
    "constant_score": {
      "filter": {
        "term": {
          "_id": "1"
        }
      },
      "boost": 1
    }
  }
}
constant_score  以非评分模式来执行，并将term查询转化为过滤器

文本查询

1.根据about查询

request

GET users/userinfo/_search
{
  "query": {
    "constant_score": {
      "filter": {
        "term": {
          "about": "I love to go rock climbing"
        }
      },
      "boost": 1
    }
  }
}

response

{
  "took": 1,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "failed": 0
  },
  "hits": {
    "total": 0,
    "max_score": null,
    "hits": []
  }
}

可以发现，查询不到任何数据。

2.分析API

request

GET users/_analyze
{
  "field": "about"
  , "text": ["I love to go rock climbing"]
}

response

{
  "tokens": [
    {
      "token": "i",
      "start_offset": 0,
      "end_offset": 1,
      "type": "<ALPHANUM>",
      "position": 0
    },
    {
      "token": "love",
      "start_offset": 2,
      "end_offset": 6,
      "type": "<ALPHANUM>",
      "position": 1
    },
    {
      "token": "to",
      "start_offset": 7,
      "end_offset": 9,
      "type": "<ALPHANUM>",
      "position": 2
    },
    {
      "token": "go",
      "start_offset": 10,
      "end_offset": 12,
      "type": "<ALPHANUM>",
      "position": 3
    },
    {
      "token": "rock",
      "start_offset": 13,
      "end_offset": 17,
      "type": "<ALPHANUM>",
      "position": 4
    },
    {
      "token": "climbing",
      "start_offset": 18,
      "end_offset": 26,
      "type": "<ALPHANUM>",
      "position": 5
    }
  ]
}

可以得知，es使用了5个token来表示字段，根据倒排索引的原则，自动拆分，并且全部使用小写表示，丢失连字符和哈希符。

3.

聚合运算

1.根据时间，求和

GET syslog_2018.05.03/_search?size=0
{
    "aggs": {
         "sales_per_month" : {
            "date_histogram" : {
                "field" : "@timestamp",
                "interval" : "5s",
                "time_zone": "Asia/Shanghai"
                "extended_bounds": {
                            "min": "now/d",
                            "max": "now/d"
                        }
            },
    "aggs" : {
        "sum_price" : { 
          "sum" : { 
            "field" : "value" 
          } 
        }
      }
    }
  }
}