elasticsearch结构化查询

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在上一篇中我们介绍了DSL相关的知识,接下来我们将会学习elasticsearch的结构化查询,同时也实践一下上一篇的DSL的查询用法

什么是结构化搜索?

从《Elasticsearch权威指南》上摘取部分解释如下:

		结构化搜索是指查询包含内部结构的数据。日期,时间,和数字都是结构化的:它们有明确的格式给你执行逻辑
	操作。一般包括比较数字或日期的范围,或确定两个值哪个大。文本也可以被结构化。一包蜡笔有不同的颜色:
	红色 , 绿色 , 蓝色 。一篇博客可能被打上 分布式 和 搜索 的标签。电子商务产品有商品统一代码(UPCs)
	或其他有着严格格式的标识。
		通过结构化搜索,你的查询结果始终是是或非;是否应该属于集合。结构化搜索不关心文档的相关性或分数,
	它只是简单的包含或排除文档。这必须是有意义的逻辑,一个数字不能比同一个范围中的其他数字 更多。它只能
	包含在一个范围中或不在其中。类似的,对于结构化文本,一个值必须相等或不等。这里没有更匹配的概念。

从上面的定义我们可以看出来结构化查询最重要的就是是否匹配么人并不是很关心相关性和分值计算。所以接下来我们将会一一介绍不同的结构化查询。

Term查询

term 主要用于查找精确值,由于不需要计算分值,而且可以被缓存,所以速度很快,而且term查询主要针对的是数字,日期,布尔值或 not_analyzed 的字符串(未经分析的文本数据类型)。
首先我们使用term查询一下:

GET bank/_search
{
  "query": {
    "term": {
      "firstname": {
        "value": "Burton"
      }
    }
  },
      "profile": "true"
}

返回结果:

{
  "took" : 1,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 0,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [ ]
  },
  "profile" : {
    "shards" : [
      {
        "id" : "[jW8PbSdhTOOpESX13DRBJQ][bank][0]",
        "searches" : [
          {
            "query" : [
              {
                "type" : "TermQuery",
                "description" : "firstname:Burton",
                "time_in_nanos" : 8729,
                "breakdown" : {
                  "set_min_competitive_score_count" : 0,
                  "match_count" : 0,
                  "shallow_advance_count" : 0,
                  "set_min_competitive_score" : 0,
                  "next_doc" : 0,
                  "match" : 0,
                  "next_doc_count" : 0,
                  "score_count" : 0,
                  "compute_max_score_count" : 0,
                  "compute_max_score" : 0,
                  "advance" : 0,
                  "advance_count" : 0,
                  "score" : 0,
                  "build_scorer_count" : 2,
                  "create_weight" : 7274,
                  "shallow_advance" : 0,
                  "create_weight_count" : 1,
                  "build_scorer" : 1455
                }
              }
            ],
            "rewrite_time" : 1222,
            "collector" : [
              {
                "name" : "SimpleTopScoreDocCollector",
                "reason" : "search_top_hits",
                "time_in_nanos" : 3132
              }
            ]
          }
        ],
        "aggregations" : [ ]
      }
    ]
  }
}

我们发现结果好像并不尽如人意,那么我们再试一个,这次我们把他修改成年龄字段搜索:

GET bank/_search
{
  "query": {
    "term": {
      "age": {
        "value": 36
      }
    }
  },
      "profile": "true"
}

返回结果:

{
  "took" : 2,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 52,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "6",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 6,
          "balance" : 5686,
          "firstname" : "Hattie",
          "lastname" : "Bond",
          "age" : 36,
          "gender" : "M",
          "address" : "671 Bristol Street",
          "employer" : "Netagy",
          "email" : "hattiebond@netagy.com",
          "city" : "Dante",
          "state" : "TN"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "361",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 361,
          "balance" : 23659,
          "firstname" : "Noreen",
          "lastname" : "Shelton",
          "age" : 36,
          "gender" : "M",
          "address" : "702 Tillary Street",
          "employer" : "Medmex",
          "email" : "noreenshelton@medmex.com",
          "city" : "Derwood",
          "state" : "NH"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "378",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 378,
          "balance" : 27100,
          "firstname" : "Watson",
          "lastname" : "Simpson",
          "age" : 36,
          "gender" : "F",
          "address" : "644 Thomas Street",
          "employer" : "Wrapture",
          "email" : "watsonsimpson@wrapture.com",
          "city" : "Keller",
          "state" : "TX"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "397",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 397,
          "balance" : 37418,
          "firstname" : "Leonard",
          "lastname" : "Gray",
          "age" : 36,
          "gender" : "F",
          "address" : "840 Morgan Avenue",
          "employer" : "Recritube",
          "email" : "leonardgray@recritube.com",
          "city" : "Edenburg",
          "state" : "AL"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "455",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 455,
          "balance" : 39556,
          "firstname" : "Lynn",
          "lastname" : "Tran",
          "age" : 36,
          "gender" : "M",
          "address" : "741 Richmond Street",
          "employer" : "Optyk",
          "email" : "lynntran@optyk.com",
          "city" : "Clinton",
          "state" : "WV"
        }
      }
    ]
  },
  "profile" : {
    "shards" : [
      {
        "id" : "[jW8PbSdhTOOpESX13DRBJQ][bank][0]",
        "searches" : [
          {
            "query" : [
              {
                "type" : "PointRangeQuery",
                "description" : "age:[36 TO 36]",
                "time_in_nanos" : 101095,
                "breakdown" : {
                  "set_min_competitive_score_count" : 0,
                  "match_count" : 0,
                  "shallow_advance_count" : 0,
                  "set_min_competitive_score" : 0,
                  "next_doc" : 10111,
                  "match" : 0,
                  "next_doc_count" : 52,
                  "score_count" : 52,
                  "compute_max_score_count" : 0,
                  "compute_max_score" : 0,
                  "advance" : 2175,
                  "advance_count" : 2,
                  "score" : 4530,
                  "build_scorer_count" : 4,
                  "create_weight" : 2346,
                  "shallow_advance" : 0,
                  "create_weight_count" : 1,
                  "build_scorer" : 81933
                }
              }
            ],
            "rewrite_time" : 1248,
            "collector" : [
              {
                "name" : "SimpleTopScoreDocCollector",
                "reason" : "search_top_hits",
                "time_in_nanos" : 76285
              }
            ]
          }
        ],
        "aggregations" : [ ]
      }
    ]
  }
}

根据上面的结果,我们再一次验证了,term查询只针对精确值、布尔值、日期以及未经分析的字段,而如果我们用term去查询一些单词或者句子等,就会匹配不到对应的值,为什么呢?官网其实也给出了解释,在下图中就可以看到,其实是因为我们es默认的标准分词器可能将我们要查询的句子或者单词变成小写了,或者拆分了,而我们的term查询又是精确查询,所以不会分析我们的搜索字段的,从而导致term查询一些单词和短语时,很难得到满意的结果。当然这也是官方提醒我们要避免的,官方建议我们使用match来匹配查询一些单词和短语,效果要比term好。
传送门
如图所示:
elasticsearch结构化查询
elasticsearch结构化查询
这里我就不翻译了,如果想学习更多,可以自行阅读官方文档。

Terms查询

terms查询其实就是类似整合多个term查询。如例子所示:

GET bank/_search
{
  "query": {
    "terms": {
      "age": [36,27]
    }
  },
      "profile": "true"
}

返回结果:

{
  "took" : 3,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 91,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "6",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 6,
          "balance" : 5686,
          "firstname" : "Hattie",
          "lastname" : "Bond",
          "age" : 36,
          "gender" : "M",
          "address" : "671 Bristol Street",
          "employer" : "Netagy",
          "email" : "hattiebond@netagy.com",
          "city" : "Dante",
          "state" : "TN"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "20",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 20,
          "balance" : 16418,
          "firstname" : "Elinor",
          "lastname" : "Ratliff",
          "age" : 36,
          "gender" : "M",
          "address" : "282 Kings Place",
          "employer" : "Scentric",
          "email" : "elinorratliff@scentric.com",
          "city" : "Ribera",
          "state" : "WA"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "102",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 102,
          "balance" : 29712,
          "firstname" : "Dena",
          "lastname" : "Olson",
          "age" : 27,
          "gender" : "F",
          "address" : "759 Newkirk Avenue",
          "employer" : "Hinway",
          "email" : "denaolson@hinway.com",
          "city" : "Choctaw",
          "state" : "NJ"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "133",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 133,
          "balance" : 26135,
          "firstname" : "Deena",
          "lastname" : "Richmond",
          "age" : 36,
          "gender" : "F",
          "address" : "646 Underhill Avenue",
          "employer" : "Sunclipse",
          "email" : "deenarichmond@sunclipse.com",
          "city" : "Austinburg",
          "state" : "SC"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "222",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 222,
          "balance" : 14764,
          "firstname" : "Rachelle",
          "lastname" : "Rice",
          "age" : 36,
          "gender" : "M",
          "address" : "333 Narrows Avenue",
          "employer" : "Enaut",
          "email" : "rachellerice@enaut.com",
          "city" : "Wright",
          "state" : "AZ"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "239",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 239,
          "balance" : 25719,
          "firstname" : "Chang",
          "lastname" : "Boyer",
          "age" : 36,
          "gender" : "M",
          "address" : "895 Brigham Street",
          "employer" : "Qaboos",
          "email" : "changboyer@qaboos.com",
          "city" : "Belgreen",
          "state" : "NH"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "328",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 328,
          "balance" : 12523,
          "firstname" : "Good",
          "lastname" : "Campbell",
          "age" : 27,
          "gender" : "F",
          "address" : "438 Hicks Street",
          "employer" : "Gracker",
          "email" : "goodcampbell@gracker.com",
          "city" : "Marion",
          "state" : "CA"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "342",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 342,
          "balance" : 33670,
          "firstname" : "Vivian",
          "lastname" : "Wells",
          "age" : 36,
          "gender" : "M",
          "address" : "570 Cobek Court",
          "employer" : "Nutralab",
          "email" : "vivianwells@nutralab.com",
          "city" : "Fontanelle",
          "state" : "OK"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "361",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 361,
          "balance" : 23659,
          "firstname" : "Noreen",
          "lastname" : "Shelton",
          "age" : 36,
          "gender" : "M",
          "address" : "702 Tillary Street",
          "employer" : "Medmex",
          "email" : "noreenshelton@medmex.com",
          "city" : "Derwood",
          "state" : "NH"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "378",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 378,
          "balance" : 27100,
          "firstname" : "Watson",
          "lastname" : "Simpson",
          "age" : 36,
          "gender" : "F",
          "address" : "644 Thomas Street",
          "employer" : "Wrapture",
          "email" : "watsonsimpson@wrapture.com",
          "city" : "Keller",
          "state" : "TX"
        }
      }
    ]
  },
  "profile" : {
    "shards" : [
      {
        "id" : "[jW8PbSdhTOOpESX13DRBJQ][bank][0]",
        "searches" : [
          {
            "query" : [
              {
                "type" : "PointInSetQuery",
                "description" : "age:{27 36}",
                "time_in_nanos" : 864841,
                "breakdown" : {
                  "set_min_competitive_score_count" : 0,
                  "match_count" : 0,
                  "shallow_advance_count" : 0,
                  "set_min_competitive_score" : 0,
                  "next_doc" : 3060,
                  "match" : 0,
                  "next_doc_count" : 91,
                  "score_count" : 91,
                  "compute_max_score_count" : 0,
                  "compute_max_score" : 0,
                  "advance" : 2277,
                  "advance_count" : 2,
                  "score" : 2482,
                  "build_scorer_count" : 4,
                  "create_weight" : 52628,
                  "shallow_advance" : 0,
                  "create_weight_count" : 1,
                  "build_scorer" : 804394
                }
              }
            ],
            "rewrite_time" : 1505,
            "collector" : [
              {
                "name" : "SimpleTopScoreDocCollector",
                "reason" : "search_top_hits",
                "time_in_nanos" : 17392
              }
            ]
          }
        ],
        "aggregations" : [ ]
      }
    ]
  }
}

当然在term中需要注意的在这里同样需要。terms也是同样是精确查询。这里提一嘴terms lookup的用法,先看lookup的例子:

GET bank/_search
{
  "query": {
    "terms": {
      "age": {
        "index" : "bank",
            "id" : "342",
            "path" : "age"
      }
    }
  },
      "profile": "true"
}

返回结果:

{
  "took" : 4,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 52,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "6",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 6,
          "balance" : 5686,
          "firstname" : "Hattie",
          "lastname" : "Bond",
          "age" : 36,
          "gender" : "M",
          "address" : "671 Bristol Street",
          "employer" : "Netagy",
          "email" : "hattiebond@netagy.com",
          "city" : "Dante",
          "state" : "TN"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "20",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 20,
          "balance" : 16418,
          "firstname" : "Elinor",
          "lastname" : "Ratliff",
          "age" : 36,
          "gender" : "M",
          "address" : "282 Kings Place",
          "employer" : "Scentric",
          "email" : "elinorratliff@scentric.com",
          "city" : "Ribera",
          "state" : "WA"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "133",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 133,
          "balance" : 26135,
          "firstname" : "Deena",
          "lastname" : "Richmond",
          "age" : 36,
          "gender" : "F",
          "address" : "646 Underhill Avenue",
          "employer" : "Sunclipse",
          "email" : "deenarichmond@sunclipse.com",
          "city" : "Austinburg",
          "state" : "SC"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "222",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 222,
          "balance" : 14764,
          "firstname" : "Rachelle",
          "lastname" : "Rice",
          "age" : 36,
          "gender" : "M",
          "address" : "333 Narrows Avenue",
          "employer" : "Enaut",
          "email" : "rachellerice@enaut.com",
          "city" : "Wright",
          "state" : "AZ"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "239",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 239,
          "balance" : 25719,
          "firstname" : "Chang",
          "lastname" : "Boyer",
          "age" : 36,
          "gender" : "M",
          "address" : "895 Brigham Street",
          "employer" : "Qaboos",
          "email" : "changboyer@qaboos.com",
          "city" : "Belgreen",
          "state" : "NH"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "342",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 342,
          "balance" : 33670,
          "firstname" : "Vivian",
          "lastname" : "Wells",
          "age" : 36,
          "gender" : "M",
          "address" : "570 Cobek Court",
          "employer" : "Nutralab",
          "email" : "vivianwells@nutralab.com",
          "city" : "Fontanelle",
          "state" : "OK"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "361",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 361,
          "balance" : 23659,
          "firstname" : "Noreen",
          "lastname" : "Shelton",
          "age" : 36,
          "gender" : "M",
          "address" : "702 Tillary Street",
          "employer" : "Medmex",
          "email" : "noreenshelton@medmex.com",
          "city" : "Derwood",
          "state" : "NH"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "378",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 378,
          "balance" : 27100,
          "firstname" : "Watson",
          "lastname" : "Simpson",
          "age" : 36,
          "gender" : "F",
          "address" : "644 Thomas Street",
          "employer" : "Wrapture",
          "email" : "watsonsimpson@wrapture.com",
          "city" : "Keller",
          "state" : "TX"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "397",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 397,
          "balance" : 37418,
          "firstname" : "Leonard",
          "lastname" : "Gray",
          "age" : 36,
          "gender" : "F",
          "address" : "840 Morgan Avenue",
          "employer" : "Recritube",
          "email" : "leonardgray@recritube.com",
          "city" : "Edenburg",
          "state" : "AL"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "455",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 455,
          "balance" : 39556,
          "firstname" : "Lynn",
          "lastname" : "Tran",
          "age" : 36,
          "gender" : "M",
          "address" : "741 Richmond Street",
          "employer" : "Optyk",
          "email" : "lynntran@optyk.com",
          "city" : "Clinton",
          "state" : "WV"
        }
      }
    ]
  },
  "profile" : {
    "shards" : [
      {
        "id" : "[jW8PbSdhTOOpESX13DRBJQ][bank][0]",
        "searches" : [
          {
            "query" : [
              {
                "type" : "PointInSetQuery",
                "description" : "age:{36}",
                "time_in_nanos" : 137631,
                "breakdown" : {
                  "set_min_competitive_score_count" : 0,
                  "match_count" : 0,
                  "shallow_advance_count" : 0,
                  "set_min_competitive_score" : 0,
                  "next_doc" : 1715,
                  "match" : 0,
                  "next_doc_count" : 52,
                  "score_count" : 52,
                  "compute_max_score_count" : 0,
                  "compute_max_score" : 0,
                  "advance" : 1762,
                  "advance_count" : 2,
                  "score" : 1462,
                  "build_scorer_count" : 4,
                  "create_weight" : 1747,
                  "shallow_advance" : 0,
                  "create_weight_count" : 1,
                  "build_scorer" : 130945
                }
              }
            ],
            "rewrite_time" : 1454,
            "collector" : [
              {
                "name" : "SimpleTopScoreDocCollector",
                "reason" : "search_top_hits",
                "time_in_nanos" : 11493
              }
            ]
          }
        ],
        "aggregations" : [ ]
      }
    ]
  }
}

我理解的意思就是查询与指定文档id下的指定的字段的值一样的文档有哪些。效率虽然高,但是限制也比较多,可自行查看官网描述的相关限制,传送门

range查询

range查询即范围查询,主要包括数字查询和日期查询,当然也可以查询文本,但是要将search.allow_expensive_queries设置为true。

查询数字

查询的结构如下:

GET bank/_search
{
  "query": {
    "range": {
      "age": {
        "gte" : 27,
         "lt" : 36
      }
    }
  },
      "profile": "true"
}

其中范围查询的参数有:
gte:大于等于
gt:大于
lt:小于
lte:小于等于
format:主要用来格式化日期的
time_zone:时区
查询数字的返回结果为:

{
  "took" : 15,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 436,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "13",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 13,
          "balance" : 32838,
          "firstname" : "Nanette",
          "lastname" : "Bates",
          "age" : 28,
          "gender" : "F",
          "address" : "789 Madison Street",
          "employer" : "Quility",
          "email" : "nanettebates@quility.com",
          "city" : "Nogal",
          "state" : "VA"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "18",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 18,
          "balance" : 4180,
          "firstname" : "Dale",
          "lastname" : "Adams",
          "age" : 33,
          "gender" : "M",
          "address" : "467 Hutchinson Court",
          "employer" : "Boink",
          "email" : "daleadams@boink.com",
          "city" : "Orick",
          "state" : "MD"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "32",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 32,
          "balance" : 48086,
          "firstname" : "Dillard",
          "lastname" : "Mcpherson",
          "age" : 34,
          "gender" : "F",
          "address" : "702 Quentin Street",
          "employer" : "Quailcom",
          "email" : "dillardmcpherson@quailcom.com",
          "city" : "Veguita",
          "state" : "IN"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "51",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 51,
          "balance" : 14097,
          "firstname" : "Burton",
          "lastname" : "Meyers",
          "age" : 31,
          "gender" : "F",
          "address" : "334 River Street",
          "employer" : "Bezal",
          "email" : "burtonmeyers@bezal.com",
          "city" : "Jacksonburg",
          "state" : "MO"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "56",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 56,
          "balance" : 14992,
          "firstname" : "Josie",
          "lastname" : "Nelson",
          "age" : 32,
          "gender" : "M",
          "address" : "857 Tabor Court",
          "employer" : "Emtrac",
          "email" : "josienelson@emtrac.com",
          "city" : "Sunnyside",
          "state" : "UT"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "63",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 63,
          "balance" : 6077,
          "firstname" : "Hughes",
          "lastname" : "Owens",
          "age" : 30,
          "gender" : "F",
          "address" : "510 Sedgwick Street",
          "employer" : "Valpreal",
          "email" : "hughesowens@valpreal.com",
          "city" : "Guilford",
          "state" : "KS"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "70",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 70,
          "balance" : 38172,
          "firstname" : "Deidre",
          "lastname" : "Thompson",
          "age" : 33,
          "gender" : "F",
          "address" : "685 School Lane",
          "employer" : "Netplode",
          "email" : "deidrethompson@netplode.com",
          "city" : "Chestnut",
          "state" : "GA"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "94",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 94,
          "balance" : 41060,
          "firstname" : "Brittany",
          "lastname" : "Cabrera",
          "age" : 30,
          "gender" : "F",
          "address" : "183 Kathleen Court",
          "employer" : "Mixers",
          "email" : "brittanycabrera@mixers.com",
          "city" : "Cornucopia",
          "state" : "AZ"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "102",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 102,
          "balance" : 29712,
          "firstname" : "Dena",
          "lastname" : "Olson",
          "age" : 27,
          "gender" : "F",
          "address" : "759 Newkirk Avenue",
          "employer" : "Hinway",
          "email" : "denaolson@hinway.com",
          "city" : "Choctaw",
          "state" : "NJ"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "107",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 107,
          "balance" : 48844,
          "firstname" : "Randi",
          "lastname" : "Rich",
          "age" : 28,
          "gender" : "M",
          "address" : "694 Jefferson Street",
          "employer" : "Netplax",
          "email" : "randirich@netplax.com",
          "city" : "Bellfountain",
          "state" : "SC"
        }
      }
    ]
  },
  "profile" : {
    "shards" : [
      {
        "id" : "[jW8PbSdhTOOpESX13DRBJQ][bank][0]",
        "searches" : [
          {
            "query" : [
              {
                "type" : "IndexOrDocValuesQuery",
                "description" : "age:[27 TO 35]",
                "time_in_nanos" : 1905910,
                "breakdown" : {
                  "set_min_competitive_score_count" : 0,
                  "match_count" : 0,
                  "shallow_advance_count" : 0,
                  "set_min_competitive_score" : 0,
                  "next_doc" : 93290,
                  "match" : 0,
                  "next_doc_count" : 437,
                  "score_count" : 436,
                  "compute_max_score_count" : 0,
                  "compute_max_score" : 0,
                  "advance" : 4119,
                  "advance_count" : 2,
                  "score" : 87412,
                  "build_scorer_count" : 4,
                  "create_weight" : 4607,
                  "shallow_advance" : 0,
                  "create_weight_count" : 1,
                  "build_scorer" : 1716482
                }
              }
            ],
            "rewrite_time" : 10427,
            "collector" : [
              {
                "name" : "SimpleTopScoreDocCollector",
                "reason" : "search_top_hits",
                "time_in_nanos" : 447004
              }
            ]
          }
        ],
        "aggregations" : [ ]
      }
    ]
  }
}

日期范围

由于这里我们的例子中没有日期相关的数据,所以我们再导入一份含有日期数据的文件到es中。即logs.jsonl
同样通过命令行导入的方式:

 curl -H 'Content-Type: application/x-ndjson'  -s -XPOST ip:9200/_bulk --data-binary @logs.jsonl

因为这里导入的数据比较大,可能会报数据量过大的错误,此时需要修改es的内存大小,因为我们在前面使用docker安装es的时候,启动的时候,设置了es的java虚拟机内存使用大小为256m,所以这里我们需要将es集群关闭之后,重新启动,如下所示:

docker stop ES01
docker rm ES01
docker run -d --restart=always -e ES_JAVA_OPTS="-Xms1g -Xmx1g" -p 9200:9200 -p 9300:9300 -v /data/es/config/es1.yml:/usr/share/elasticsearch/config/elasticsearch.yml -v /data/es/node1/data:/usr/share/elasticsearch/data -v /data/es/node1/plugins:/usr/share/elasticsearch/plugins --name ES01 elasticsearch:7.9.3

我们将这句-e ES_JAVA_OPTS="-Xms1g -Xmx1g"已经修改为1g了,然后启动,其他两个节点同样需要这样的操作。
最后导入数据即可。

从图中可知我们已经导入成功了,
elasticsearch结构化查询
这里我们开始学习range的查询日期用法:
在这里我们查询日志的utc_time

GET logstash-2015.05.19/_search
{
  "query": {
    "range": {
      "utc_time": {
          "gte" : "2015-05-19T04:29:38.264Z",
          "lte" : "2015-05-30T04:29:38.264Z"
        }
    }
  }
}

返回结果:

{
  "took" : 4,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 4531,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "logstash-2015.05.19",
        "_type" : "log",
        "_id" : "B-kR0IcB2l-Q2jxdeb9E",
        "_score" : 1.0,
        "_source" : {
          "@timestamp" : "2015-05-19T04:29:38.264Z",
          "ip" : "33.36.12.33",
          "extension" : "png",
          "response" : "503",
          "geo" : {
            "coordinates" : {
              "lat" : 33.45033444,
              "lon" : -88.59136861
            },
            "src" : "NG",
            "dest" : "PK",
            "srcdest" : "NG:PK"
          },
          "@tags" : [
            "success",
            "security"
          ],
          "utc_time" : "2015-05-19T04:29:38.264Z",
          "referer" : "http://twitter.com/success/kent-rominger",
          "agent" : "Mozilla/5.0 (X11; Linux i686) AppleWebKit/534.24 (KHTML, like Gecko) Chrome/11.0.696.50 Safari/534.24",
          "clientip" : "33.36.12.33",
          "bytes" : 0,
          "host" : "media-for-the-masses.theacademyofperformingartsandscience.org",
          "request" : "/uploads/satoshi-furukawa.png",
          "url" : "https://media-for-the-masses.theacademyofperformingartsandscience.org/uploads/satoshi-furukawa.png",
          "@message" : "33.36.12.33 - - [2015-05-19T04:29:38.264Z] \"GET /uploads/satoshi-furukawa.png HTTP/1.1\" 503 0 \"-\" \"Mozilla/5.0 (X11; Linux i686) AppleWebKit/534.24 (KHTML, like Gecko) Chrome/11.0.696.50 Safari/534.24\"",
          "spaces" : "this   is   a   thing    with lots of     spaces       wwwwoooooo",
          "xss" : """<script>console.log("xss")</script>""",
          "headings" : [
            "<h3>kimiya-yui</h5>",
            "http://www.slate.com/success/roman-romanenko"
          ],
          "links" : [
            "christopher-ferguson@www.slate.com",
            "http://www.slate.com/info/karol-bobko",
            "www.twitter.com"
          ],
          "relatedContent" : [
            {
              "url" : "http://www.laweekly.com/music/jim-white-amoeba-3-24-2412442",
              "og:type" : "article",
              "og:title" : "Jim White, Amoeba, 3/24",
              "og:description" : "Jim White Amoeba, March 24 Jim White&#039;s songs depend on a good deal of atmosphere, and no doubt when he plays tonight (Tuesday) at the Silent Movie Theat...",
              "og:url" : "http://www.laweekly.com/music/jim-white-amoeba-3-24-2412442",
              "article:published_time" : "2008-03-25T06:37:10-07:00",
              "article:modified_time" : "2014-11-27T10:15:27-08:00",
              "article:section" : "Music",
              "og:image" : "http://images1.laweekly.com/imager/jim-white-amoeba-3-24/u/original/2475386/img_6075.jpg",
              "og:image:height" : "360",
              "og:image:width" : "480",
              "og:site_name" : "LA Weekly",
              "twitter:title" : "Jim White, Amoeba, 3/24",
              "twitter:description" : "Jim White Amoeba, March 24 Jim White&#039;s songs depend on a good deal of atmosphere, and no doubt when he plays tonight (Tuesday) at the Silent Movie Theat...",
              "twitter:card" : "summary",
              "twitter:image" : "http://images1.laweekly.com/imager/jim-white-amoeba-3-24/u/original/2475386/img_6075.jpg",
              "twitter:site" : "@laweekly"
            },
            {
              "url" : "http://www.laweekly.com/restaurants/the-vegan-beer-festival-returns-this-may-with-a-swanky-new-location-5470861",
              "og:type" : "article",
              "og:title" : "The Vegan Beer Festival Returns This May With a Swanky New Location",
              "og:description" : "Now in its 6th year, the Vegan Beer Festival will take place this May with more animal-free food and drinks at a much improved location.",
              "og:url" : "http://www.laweekly.com/restaurants/the-vegan-beer-festival-returns-this-may-with-a-swanky-new-location-5470861",
              "article:published_time" : "2015-04-03T06:55:00-07:00",
              "article:modified_time" : "2015-04-03T06:55:04-07:00",
              "article:section" : "Restaurants",
              "og:image" : "http://images1.laweekly.com/imager/u/original/5470943/vegan-beer.jpg",
              "og:image:height" : "798",
              "og:image:width" : "1200",
              "og:site_name" : "LA Weekly",
              "twitter:title" : "The Vegan Beer Festival Returns This May With a Swanky New Location",
              "twitter:description" : "Now in its 6th year, the Vegan Beer Festival will take place this May with more animal-free food and drinks at a much improved location.",
              "twitter:card" : "summary",
              "twitter:image" : "http://images1.laweekly.com/imager/u/original/5470943/vegan-beer.jpg",
              "twitter:site" : "@laweekly"
            }
          ],
          "machine" : {
            "os" : "osx",
            "ram" : 18253611008
          },
          "@version" : "1"
        }
      },
      {
        "_index" : "logstash-2015.05.19",
        "_type" : "log",
        "_id" : "COkR0IcB2l-Q2jxdeb9E",
        "_score" : 1.0,
        "_source" : {
          "@timestamp" : "2015-05-19T09:36:57.086Z",
          "ip" : "116.116.183.113",
          "extension" : "jpg",
          "response" : "200",
          "geo" : {
            "coordinates" : {
              "lat" : 31.36399028,
              "lon" : -109.8831286
            },
            "src" : "US",
            "dest" : "SL",
            "srcdest" : "US:SL"
          },
          "@tags" : [
            "success",
            "info"
          ],
          "utc_time" : "2015-05-19T09:36:57.086Z",
          "referer" : "http://facebook.com/success/aleksei-gubarev",
          "agent" : "Mozilla/5.0 (X11; Linux i686) AppleWebKit/534.24 (KHTML, like Gecko) Chrome/11.0.696.50 Safari/534.24",
          "clientip" : "116.116.183.113",
          "bytes" : 3715,
          "host" : "media-for-the-masses.theacademyofperformingartsandscience.org",
          "request" : "/uploads/vance-brand.jpg",
          "url" : "https://media-for-the-masses.theacademyofperformingartsandscience.org/uploads/vance-brand.jpg",
          "@message" : "116.116.183.113 - - [2015-05-19T09:36:57.086Z] \"GET /uploads/vance-brand.jpg HTTP/1.1\" 200 3715 \"-\" \"Mozilla/5.0 (X11; Linux i686) AppleWebKit/534.24 (KHTML, like Gecko) Chrome/11.0.696.50 Safari/534.24\"",
          "spaces" : "this   is   a   thing    with lots of     spaces       wwwwoooooo",
          "xss" : """<script>console.log("xss")</script>""",
          "headings" : [
            "<h3>frank-de-winne</h5>",
            "http://www.slate.com/success/grigori-nelyubov"
          ],
          "links" : [
            "gennady-strekalov@www.slate.com",
            "http://nytimes.com/login/gemini-6a",
            "www.facebook.com"
          ],
          "relatedContent" : [
            {
              "url" : "http://www.laweekly.com/news/mistakes-were-made-2397282",
              "og:type" : "article",
              "og:title" : "Mistakes Were Made",
              "og:description" : "Dysfunctional business as usual at Pellicano trial When we last left the trial of Anthony Pellicano and four co-defendants, the proceedings had been thr...",
              "og:url" : "http://www.laweekly.com/news/mistakes-were-made-2397282",
              "article:published_time" : "2008-04-28T14:26:06-07:00",
              "article:modified_time" : "2014-11-26T18:18:39-08:00",
              "article:section" : "News",
              "og:site_name" : "LA Weekly",
              "twitter:title" : "Mistakes Were Made",
              "twitter:description" : "Dysfunctional business as usual at Pellicano trial When we last left the trial of Anthony Pellicano and four co-defendants, the proceedings had been thr...",
              "twitter:card" : "summary",
              "twitter:site" : "@laweekly"
            }
          ],
          "machine" : {
            "os" : "ios",
            "ram" : 20401094656
          },
          "@version" : "1"
        }
      },
      {
        "_index" : "logstash-2015.05.19",
        "_type" : "log",
        "_id" : "CekR0IcB2l-Q2jxdeb9E",
        "_score" : 1.0,
        "_source" : {
          "@timestamp" : "2015-05-19T06:19:55.080Z",
          "ip" : "22.50.26.143",
          "extension" : "jpg",
          "response" : "200",
          "geo" : {
            "coordinates" : {
              "lat" : 35.02397611,
              "lon" : -80.08127333
            },
            "src" : "IN",
            "dest" : "IN",
            "srcdest" : "IN:IN"
          },
          "@tags" : [
            "success",
            "info"
          ],
          "utc_time" : "2015-05-19T06:19:55.080Z",
          "referer" : "http://twitter.com/success/aleksandr-laveykin",
          "agent" : "Mozilla/5.0 (X11; Linux x86_64; rv:6.0a1) Gecko/20110421 Firefox/6.0a1",
          "clientip" : "22.50.26.143",
          "bytes" : 8852,
          "host" : "media-for-the-masses.theacademyofperformingartsandscience.org",
          "request" : "/uploads/barbara-morgan.jpg",
          "url" : "https://media-for-the-masses.theacademyofperformingartsandscience.org/uploads/barbara-morgan.jpg",
          "@message" : "22.50.26.143 - - [2015-05-19T06:19:55.080Z] \"GET /uploads/barbara-morgan.jpg HTTP/1.1\" 200 8852 \"-\" \"Mozilla/5.0 (X11; Linux x86_64; rv:6.0a1) Gecko/20110421 Firefox/6.0a1\"",
          "spaces" : "this   is   a   thing    with lots of     spaces       wwwwoooooo",
          "xss" : """<script>console.log("xss")</script>""",
          "headings" : [
            "<h3>james-p-bagian</h5>",
            "http://www.slate.com/success/judith-resnik"
          ],
          "links" : [
            "liu-boming@www.slate.com",
            "http://facebook.com/info/svetlana-savitskaya",
            "www.www.slate.com"
          ],
          "relatedContent" : [
            {
              "url" : "http://www.laweekly.com/music/the-whole-shootin-match-2404141",
              "og:type" : "article",
              "og:title" : "The whole shootin&#039; match",
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            "http://twitter.com/security/william-frederick-fisher",
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          "headings" : [
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            "http://www.slate.com/success/jing-haipeng"
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          "@tags" : [
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          "referer" : "http://twitter.com/success/richard-linnehan",
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}

准确的输出了日期2015-05-19T04:29:38.264Z与2015-05-30T04:29:38.264Z之间的数据。

小结

关于range的查询基本就这么多,还有部分关于字符串的,过于简单就不做详细的叙述,因为字符串的查询是直接利用的字典序查询,不如是用其他方式直接查询字符串。还有关于ip的范围查询,但是实在聚合查询中体现的,在range中好像并没法使用。

Exists查询

exists查询的主要意思是字段是否存在值,如果不存在值,则不会被查询出来,如果有值,则会被查询出来,null的情况下也不会被查询出来。
这里我就直接查询了,因为数据量比较大,所以只展示用法,数据是否为null等就算了,嘿嘿

GET bank/_search
{
  "query": {
    "exists": {
      "field": "firstname"
    }
  },
      "profile": "true"
}

返回结果:

{
  "took" : 2,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 1000,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "6",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 6,
          "balance" : 5686,
          "firstname" : "Hattie",
          "lastname" : "Bond",
          "age" : 36,
          "gender" : "M",
          "address" : "671 Bristol Street",
          "employer" : "Netagy",
          "email" : "hattiebond@netagy.com",
          "city" : "Dante",
          "state" : "TN"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "13",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 13,
          "balance" : 32838,
          "firstname" : "Nanette",
          "lastname" : "Bates",
          "age" : 28,
          "gender" : "F",
          "address" : "789 Madison Street",
          "employer" : "Quility",
          "email" : "nanettebates@quility.com",
          "city" : "Nogal",
          "state" : "VA"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "18",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 18,
          "balance" : 4180,
          "firstname" : "Dale",
          "lastname" : "Adams",
          "age" : 33,
          "gender" : "M",
          "address" : "467 Hutchinson Court",
          "employer" : "Boink",
          "email" : "daleadams@boink.com",
          "city" : "Orick",
          "state" : "MD"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "20",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 20,
          "balance" : 16418,
          "firstname" : "Elinor",
          "lastname" : "Ratliff",
          "age" : 36,
          "gender" : "M",
          "address" : "282 Kings Place",
          "employer" : "Scentric",
          "email" : "elinorratliff@scentric.com",
          "city" : "Ribera",
          "state" : "WA"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "25",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 25,
          "balance" : 40540,
          "firstname" : "Virginia",
          "lastname" : "Ayala",
          "age" : 39,
          "gender" : "F",
          "address" : "171 Putnam Avenue",
          "employer" : "Filodyne",
          "email" : "virginiaayala@filodyne.com",
          "city" : "Nicholson",
          "state" : "PA"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "32",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 32,
          "balance" : 48086,
          "firstname" : "Dillard",
          "lastname" : "Mcpherson",
          "age" : 34,
          "gender" : "F",
          "address" : "702 Quentin Street",
          "employer" : "Quailcom",
          "email" : "dillardmcpherson@quailcom.com",
          "city" : "Veguita",
          "state" : "IN"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "37",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 37,
          "balance" : 18612,
          "firstname" : "Mcgee",
          "lastname" : "Mooney",
          "age" : 39,
          "gender" : "M",
          "address" : "826 Fillmore Place",
          "employer" : "Reversus",
          "email" : "mcgeemooney@reversus.com",
          "city" : "Tooleville",
          "state" : "OK"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "44",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 44,
          "balance" : 34487,
          "firstname" : "Aurelia",
          "lastname" : "Harding",
          "age" : 37,
          "gender" : "M",
          "address" : "502 Baycliff Terrace",
          "employer" : "Orbalix",
          "email" : "aureliaharding@orbalix.com",
          "city" : "Yardville",
          "state" : "DE"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "49",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 49,
          "balance" : 29104,
          "firstname" : "Fulton",
          "lastname" : "Holt",
          "age" : 23,
          "gender" : "F",
          "address" : "451 Humboldt Street",
          "employer" : "Anocha",
          "email" : "fultonholt@anocha.com",
          "city" : "Sunriver",
          "state" : "RI"
        }
      },
      {
        "_index" : "bank",
        "_type" : "account",
        "_id" : "51",
        "_score" : 1.0,
        "_source" : {
          "account_number" : 51,
          "balance" : 14097,
          "firstname" : "Burton",
          "lastname" : "Meyers",
          "age" : 31,
          "gender" : "F",
          "address" : "334 River Street",
          "employer" : "Bezal",
          "email" : "burtonmeyers@bezal.com",
          "city" : "Jacksonburg",
          "state" : "MO"
        }
      }
    ]
  },
  "profile" : {
    "shards" : [
      {
        "id" : "[pgvIy_S0QwiNETOTSEWFtw][bank][0]",
        "searches" : [
          {
            "query" : [
              {
                "type" : "ConstantScoreQuery",
                "description" : "ConstantScore(NormsFieldExistsQuery [field=firstname])",
                "time_in_nanos" : 274491,
                "breakdown" : {
                  "set_min_competitive_score_count" : 0,
                  "match_count" : 0,
                  "shallow_advance_count" : 0,
                  "set_min_competitive_score" : 0,
                  "next_doc" : 82208,
                  "match" : 0,
                  "next_doc_count" : 1001,
                  "score_count" : 1000,
                  "compute_max_score_count" : 0,
                  "compute_max_score" : 0,
                  "advance" : 2270,
                  "advance_count" : 2,
                  "score" : 27152,
                  "build_scorer_count" : 4,
                  "create_weight" : 32473,
                  "shallow_advance" : 0,
                  "create_weight_count" : 1,
                  "build_scorer" : 130388
                },
                "children" : [
                  {
                    "type" : "NormsFieldExistsQuery",
                    "description" : "NormsFieldExistsQuery [field=firstname]",
                    "time_in_nanos" : 65654,
                    "breakdown" : {
                      "set_min_competitive_score_count" : 0,
                      "match_count" : 0,
                      "shallow_advance_count" : 0,
                      "set_min_competitive_score" : 0,
                      "next_doc" : 27263,
                      "match" : 0,
                      "next_doc_count" : 1001,
                      "score_count" : 0,
                      "compute_max_score_count" : 0,
                      "compute_max_score" : 0,
                      "advance" : 1265,
                      "advance_count" : 2,
                      "score" : 0,
                      "build_scorer_count" : 4,
                      "create_weight" : 6826,
                      "shallow_advance" : 0,
                      "create_weight_count" : 1,
                      "build_scorer" : 30300
                    }
                  }
                ]
              }
            ],
            "rewrite_time" : 2103,
            "collector" : [
              {
                "name" : "SimpleTopScoreDocCollector",
                "reason" : "search_top_hits",
                "time_in_nanos" : 101852
              }
            ]
          }
        ],
        "aggregations" : [ ]
      }
    ]
  }
}

总结

结构化查询的第一部分基本就这么多,后面还会学习第二部分的,关于模糊查询方面的。文章来源地址https://www.toymoban.com/news/detail-428089.html

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