一起来学ElasticSearch(七)

前言

目前正在出一个Es专题系列教程, 篇幅会较多, 喜爱的话，给个重视❤️ ~

承接上文，本节把上节留传的条件查询操作给咱们讲一下~

为了方便学习, 本节中所有示例沿用上节的索引。本文偏实战一些，好了, 废话不多说直接开整吧~

多条件组合查询

bool

es中运用bool来操控多条件查询，bool查询支持以下参数:

• must：被查询的数据有必要满足当时条件
• mush_not：被查询的数据有必要不满足当时条件
• should：被查询的数据应该满足当时条件。should查询被用于批改查询成果的评分。需求留意的是，假如组合查询中没有must，那么被查询的数据至少要匹配一条should。假如有must句子，那么就无须匹配should，should将彻底用于批改查询成果的评分
• filter：被查询的数据有必要满足当时条件，可是filter操作不涉及查询成果评分。仅用于条件过滤

下面经过一个例子来看下怎么运用:

GET class_1/_search
{
  "query": {
    "bool": {
      "must": [
        {"match": {
          "name": "apple"
        }}
      ],
      "must_not": [
        {"term": {
          "num": {
            "value": "5"
          }
        }}
      ],
      "should": [
        {"match": {
          "name": "k"
        }}
      ],"filter": [
        {"range": {
          "num": {
            "gte": 0,
            "lte": 10
          }
        }}
      ]
    }
  }
}

成果回来:

{
  "took" : 9,
  "timed_out" : false,
  "_shards" : {
    "total" : 3,
    "successful" : 3,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 3,
      "relation" : "eq"
    },
    "max_score" : 0.752627,
    "hits" : [
      {
        "_index" : "class_1",
        "_type" : "_doc",
        "_id" : "b8fcCoYB090miyjed7YE",
        "_score" : 0.752627,
        "_source" : {
          "name" : "I eat apple so haochi1~",
          "num" : 1
        }
      },
      {
        "_index" : "class_1",
        "_type" : "_doc",
        "_id" : "ccfcCoYB090miyjed7YE",
        "_score" : 0.752627,
        "_source" : {
          "name" : "I eat apple so haochi3~",
          "num" : 1
        }
      },
      {
        "_index" : "class_1",
        "_type" : "_doc",
        "_id" : "cMfcCoYB090miyjed7YE",
        "_score" : 0.7389809,
        "_source" : {
          "name" : "I eat apple so zhen haochi2~",
          "num" : 1
        }
      }
    ]
  }
}

constant_score

constant_score查询能够经过boost指定一个固定的评分，一般来说，constant_score的作用是替代一个只要filter的bool查询

下面看具体运用：

GET class_1/_search
{
  "query": {
    "constant_score": {
      "filter": {
        "term": {
          "num": 6
        }
      },
      "boost": 1.2
    }
  }
}

回来:

{
  "took" : 7,
  "timed_out" : false,
  "_shards" : {
    "total" : 3,
    "successful" : 3,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 2,
      "relation" : "eq"
    },
    "max_score" : 1.2,
    "hits" : [
      {
        "_index" : "class_1",
        "_type" : "_doc",
        "_id" : "h2Fg-4UBECmbBdQA6VLg",
        "_score" : 1.2,
        "_source" : {
          "name" : "b",
          "num" : 6
        }
      },
      {
        "_index" : "class_1",
        "_type" : "_doc",
        "_id" : "1",
        "_score" : 1.2,
        "_source" : {
          "name" : "l",
          "num" : 6
        }
      }
    ]
  }
}

查询验证 & 分析

验证

es中经过/_validate/query路由来验证查询条件的正确性, 这里要留意是验证查询条件是否准确

示例:

GET class_1/_validate/query?explain
{
  "query": {
    "bool": {
      "must": [
        {"match": {
          "name": "apple"
        }}
      ]
    }
  }
}

正常回来:

{
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "failed" : 0
  },
  "valid" : true,
  "explanations" : [
    {
      "index" : "class_1",
      "valid" : true,
      "explanation" : "+name:apple"
    }
  ]
}

将name字段改为 name1再查询:

{
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "failed" : 0
  },
  "valid" : true,
  "explanations" : [
    {
      "index" : "class_1",
      "valid" : true,
      "explanation" : """+MatchNoDocsQuery("unmapped fields [name1]")"""
    }
  ]
}

能够看到报了反常错误

分析

es中经过/_validate/query?explain路由来进行查询分析

示例:

GET class_1/_validate/query?explain
{
  "query": {
    "bool": {
      "must": [
        {"match": {
          "name": "apple so"
        }}
      ]
    }
  }
}

回来:

{
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "failed" : 0
  },
  "valid" : true,
  "explanations" : [
    {
      "index" : "class_1",
      "valid" : true,
      "explanation" : "+(name:apple name:so)"
    }
  ]
}

能够看到"explanation" : "+(name:apple name:so)"，查询的短语apple so被进行了分词，分成了name:apple, name: so

排序

默许排序

在前面的几个例子中，咱们能够看到它的默许排序是依照_score降序，也便是匹配度高的比较靠前,可是_socre的核算是很占用查询功能的,这个不难理解。

当咱们不需求进行_score核算，能够经过filter或constant_score来进行构建查询条件

filter示例:

GET class_1/_search
{
  "query": {
    "bool": {
      "filter": [
        {"term": {
          "num": 1
        }}
      ]
    }
  }
}

回来:

{
  "took" : 5,
  "timed_out" : false,
  "_shards" : {
    "total" : 3,
    "successful" : 3,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 3,
      "relation" : "eq"
    },
    "max_score" : 0.0,
    "hits" : [
      {
        "_index" : "class_1",
        "_type" : "_doc",
        "_id" : "b8fcCoYB090miyjed7YE",
        "_score" : 0.0,
        "_source" : {
          "name" : "I eat apple so haochi1~",
          "num" : 1
        }
      },
      {
        "_index" : "class_1",
        "_type" : "_doc",
        "_id" : "ccfcCoYB090miyjed7YE",
        "_score" : 0.0,
        "_source" : {
          "name" : "I eat apple so haochi3~",
          "num" : 1
        }
      },
      {
        "_index" : "class_1",
        "_type" : "_doc",
        "_id" : "cMfcCoYB090miyjed7YE",
        "_score" : 0.0,
        "_source" : {
          "name" : "I eat apple so zhen haochi2~",
          "num" : 1
        }
      }
    ]
  }
}

经过查询成果咱们发现score都为0.0了，说明没有进行score核算

constant_score示例：

GET class_1/_search
{
  "query": {
    "constant_score": {
      "filter": {
        "term": {
          "num": 1
        }
      },
      "boost": 1.2
    }
  }
}

回来:

{
  "took" : 3,
  "timed_out" : false,
  "_shards" : {
    "total" : 3,
    "successful" : 3,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 3,
      "relation" : "eq"
    },
    "max_score" : 1.2,
    "hits" : [
      {
        "_index" : "class_1",
        "_type" : "_doc",
        "_id" : "b8fcCoYB090miyjed7YE",
        "_score" : 1.2,
        "_source" : {
          "name" : "I eat apple so haochi1~",
          "num" : 1
        }
      },
      {
        "_index" : "class_1",
        "_type" : "_doc",
        "_id" : "ccfcCoYB090miyjed7YE",
        "_score" : 1.2,
        "_source" : {
          "name" : "I eat apple so haochi3~",
          "num" : 1
        }
      },
      {
        "_index" : "class_1",
        "_type" : "_doc",
        "_id" : "cMfcCoYB090miyjed7YE",
        "_score" : 1.2,
        "_source" : {
          "name" : "I eat apple so zhen haochi2~",
          "num" : 1
        }
      }
    ]
  }
}

能够看到，对应回来的分值，都是运用boost特点指定的分值

自定义排序

自定义能够用于大部分场景，那么es中怎样进行自定义排序呢? es中运用sort参数来自定义排序次序，默许为升序，那么降序怎样操作呢？

• 升序

{"sort":["num"]}

• 降序, desc代表降序

{"sort":[{"num":{"order":"desc"}}]}

tips

• es中运用doc value列式存储来完成字段的排序功能
• text字段默许不创立doc value，因而无法针对text字段进行排序
• 能够经过设置text字段特点fielddata=true来开启对text字段的排序功能，可是不主张开启，对text字段排序及其消耗查询功能且不契合需求

单字段排序

GET class_1/_search
{
    "sort": [
        "num"
    ]
}

回来:

{
  "took" : 6,
  "timed_out" : false,
  "_shards" : {
    "total" : 3,
    "successful" : 3,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 11,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [
      {
        "_index" : "class_1",
        "_type" : "_doc",
        "_id" : "b8fcCoYB090miyjed7YE",
        "_score" : null,
        "_source" : {
          "name" : "I eat apple so haochi1~",
          "num" : 1
        },
        "sort" : [
          1
        ]
      },
      {
        "_index" : "class_1",
        "_type" : "_doc",
        "_id" : "ccfcCoYB090miyjed7YE",
        "_score" : null,
        "_source" : {
          "name" : "I eat apple so haochi3~",
          "num" : 1
        },
        "sort" : [
          1
        ]
      },
      {
        "_index" : "class_1",
        "_type" : "_doc",
        "_id" : "cMfcCoYB090miyjed7YE",
        "_score" : null,
        "_source" : {
          "name" : "I eat apple so zhen haochi2~",
          "num" : 1
        },
        "sort" : [
          1
        ]
      },
      {
        "_index" : "class_1",
        "_type" : "_doc",
        "_id" : "h2Fg-4UBECmbBdQA6VLg",
        "_score" : null,
        "_source" : {
          "name" : "b",
          "num" : 6
        },
        "sort" : [
          6
        ]
      },
      {
        "_index" : "class_1",
        "_type" : "_doc",
        "_id" : "1",
        "_score" : null,
        "_source" : {
          "name" : "l",
          "num" : 6
        },
        "sort" : [
          6
        ]
      },
      {
        "_index" : "class_1",
        "_type" : "_doc",
        "_id" : "3",
        "_score" : null,
        "_source" : {
          "num" : 9,
          "name" : "e",
          "age" : 9,
          "desc" : [
            "hhhh"
          ]
        },
        "sort" : [
          9
        ]
      },
      {
        "_index" : "class_1",
        "_type" : "_doc",
        "_id" : "4",
        "_score" : null,
        "_source" : {
          "name" : "f",
          "age" : 10,
          "num" : 10
        },
        "sort" : [
          10
        ]
      },
      {
        "_index" : "class_1",
        "_type" : "_doc",
        "_id" : "RWlfBIUBDuA8yW5cu9wu",
        "_score" : null,
        "_source" : {
          "name" : "一年级",
          "num" : 20
        },
        "sort" : [
          20
        ]
      },
      {
        "_index" : "class_1",
        "_type" : "_doc",
        "_id" : "iGFt-4UBECmbBdQAnVJe",
        "_score" : null,
        "_source" : {
          "name" : "g",
          "age" : 8
        },
        "sort" : [
          9223372036854775807
        ]
      },
      {
        "_index" : "class_1",
        "_type" : "_doc",
        "_id" : "iWFt-4UBECmbBdQAnVJg",
        "_score" : null,
        "_source" : {
          "name" : "h",
          "age" : 9
        },
        "sort" : [
          9223372036854775807
        ]
      }
    ]
  }
}

能够看到是依照num默许升序排序

再看下降序:

GET class_1/_search
{
    "sort": [
        {"num": {"order":"desc"}}
    ]
}

回来：

{
  "took" : 15,
  "timed_out" : false,
  "_shards" : {
    "total" : 3,
    "successful" : 3,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 11,
      "relation" : "eq"
    },
    "max_score" : null,
    "hits" : [
      {
        "_index" : "class_1",
        "_type" : "_doc",
        "_id" : "RWlfBIUBDuA8yW5cu9wu",
        "_score" : null,
        "_source" : {
          "name" : "一年级",
          "num" : 20
        },
        "sort" : [
          20
        ]
      },
      {
        "_index" : "class_1",
        "_type" : "_doc",
        "_id" : "4",
        "_score" : null,
        "_source" : {
          "name" : "f",
          "age" : 10,
          "num" : 10
        },
        "sort" : [
          10
        ]
      },
      {
        "_index" : "class_1",
        "_type" : "_doc",
        "_id" : "3",
        "_score" : null,
        "_source" : {
          "num" : 9,
          "name" : "e",
          "age" : 9,
          "desc" : [
            "hhhh"
          ]
        },
        "sort" : [
          9
        ]
      },
      {
        "_index" : "class_1",
        "_type" : "_doc",
        "_id" : "h2Fg-4UBECmbBdQA6VLg",
        "_score" : null,
        "_source" : {
          "name" : "b",
          "num" : 6
        },
        "sort" : [
          6
        ]
      },
      {
        "_index" : "class_1",
        "_type" : "_doc",
        "_id" : "1",
        "_score" : null,
        "_source" : {
          "name" : "l",
          "num" : 6
        },
        "sort" : [
          6
        ]
      },
      {
        "_index" : "class_1",
        "_type" : "_doc",
        "_id" : "b8fcCoYB090miyjed7YE",
        "_score" : null,
        "_source" : {
          "name" : "I eat apple so haochi1~",
          "num" : 1
        },
        "sort" : [
          1
        ]
      },
      {
        "_index" : "class_1",
        "_type" : "_doc",
        "_id" : "ccfcCoYB090miyjed7YE",
        "_score" : null,
        "_source" : {
          "name" : "I eat apple so haochi3~",
          "num" : 1
        },
        "sort" : [
          1
        ]
      },
      {
        "_index" : "class_1",
        "_type" : "_doc",
        "_id" : "cMfcCoYB090miyjed7YE",
        "_score" : null,
        "_source" : {
          "name" : "I eat apple so zhen haochi2~",
          "num" : 1
        },
        "sort" : [
          1
        ]
      },
      {
        "_index" : "class_1",
        "_type" : "_doc",
        "_id" : "iGFt-4UBECmbBdQAnVJe",
        "_score" : null,
        "_source" : {
          "name" : "g",
          "age" : 8
        },
        "sort" : [
          -9223372036854775808
        ]
      },
      {
        "_index" : "class_1",
        "_type" : "_doc",
        "_id" : "iWFt-4UBECmbBdQAnVJg",
        "_score" : null,
        "_source" : {
          "name" : "h",
          "age" : 9
        },
        "sort" : [
          -9223372036854775808
        ]
      }
    ]
  }
}

这下就降序排序了

多字段

GET class_1/_search
{
    "sort": [
        "num", "age"
    ]
}

scroll分页

还记得之前给咱们讲的from+size的分页方式吗，es中默许答应from+size的分页的最大数据量为10000。当咱们想要批量获取更大的数据量时，运用from+size就会十分的消耗功能。

但是大部分应用场景下的数据量是极端巨大的，比方你要查询某些系统日志数据。es中能够运用/scorll路由来进行翻滚分页查询，它类似于在查询初始时刻点创立了一个当时服务集群的数据快照（包括每一个分片），并保留它一段时刻。在时刻超过了设置的过期时刻以后，快照将在es空闲时被删去。

需求留意的是，由于是进行快照查询，因而在快照创立后数据的变更在本次的翻滚查询中，不行见

初始化快照 & 快照保存10分钟

查询示例:

GET class_1/_search?scroll=10m
{
"query": {
 "match_phrase": {
   "name": "apple"
 }
},
"size": 2
}

回来：

{
  "_scroll_id" : "DnF1ZXJ5VGhlbkZldGNoAwAAAAAAAAXoFjEwWkdOMkxLUTVPZEMzM01ZdHhPc1EAAAAAAAACABZjUy1CemQwQVFfU3BUeGs2OGk0R1Z3AAAAAAAAAgEWY1MtQnpkMEFRX1NwVHhrNjhpNEdWdw==",
  "took" : 6,
  "timed_out" : false,
  "_shards" : {
    "total" : 3,
    "successful" : 3,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 3,
      "relation" : "eq"
    },
    "max_score" : 0.752627,
    "hits" : [
      {
        "_index" : "class_1",
        "_type" : "_doc",
        "_id" : "b8fcCoYB090miyjed7YE",
        "_score" : 0.752627,
        "_source" : {
          "name" : "I eat apple so haochi1~",
          "num" : 1
        }
      },
      {
        "_index" : "class_1",
        "_type" : "_doc",
        "_id" : "ccfcCoYB090miyjed7YE",
        "_score" : 0.752627,
        "_source" : {
          "name" : "I eat apple so haochi3~",
          "num" : 1
        }
      }
    ]
  }
}

如图，当时共回来2条数据，并且回来了一个快照ID，后续能够依据快照ID进行翻滚查询:

依据快照ID翻滚查询

GET /_search/scroll
{
 "scroll": "10m", 
 "scroll_id" : "DnF1ZXJ5VGhlbkZldGNoAwAAAAAAAAXoFjEwWkdOMkxLUTVPZEMzM01ZdHhPc1EAAAAAAAACABZjUy1CemQwQVFfU3BUeGs2OGk0R1Z3AAAAAAAAAgEWY1MtQnpkMEFRX1NwVHhrNjhpNEdWdw=="
}

回来:

{
  "_scroll_id" : "DnF1ZXJ5VGhlbkZldGNoAwAAAAAAAAXoFjEwWkdOMkxLUTVPZEMzM01ZdHhPc1EAAAAAAAACABZjUy1CemQwQVFfU3BUeGs2OGk0R1Z3AAAAAAAAAgEWY1MtQnpkMEFRX1NwVHhrNjhpNEdWdw==",
  "took" : 6,
  "timed_out" : false,
  "_shards" : {
    "total" : 3,
    "successful" : 3,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 3,
      "relation" : "eq"
    },
    "max_score" : 0.752627,
    "hits" : [
      {
        "_index" : "class_1",
        "_type" : "_doc",
        "_id" : "cMfcCoYB090miyjed7YE",
        "_score" : 0.7389809,
        "_source" : {
          "name" : "I eat apple so zhen haochi2~",
          "num" : 1
        }
      }
    ]
  }
}

在翻滚一次:

{
  "_scroll_id" : "DnF1ZXJ5VGhlbkZldGNoAwAAAAAAAAXoFjEwWkdOMkxLUTVPZEMzM01ZdHhPc1EAAAAAAAACABZjUy1CemQwQVFfU3BUeGs2OGk0R1Z3AAAAAAAAAgEWY1MtQnpkMEFRX1NwVHhrNjhpNEdWdw==",
  "took" : 1,
  "timed_out" : false,
  "_shards" : {
    "total" : 3,
    "successful" : 3,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 3,
      "relation" : "eq"
    },
    "max_score" : 0.752627,
    "hits" : [ ]
  }
}

有的小伙伴或许不知道怎样翻滚的，由于后续翻滚都是同一个scroll_id,其实经过成果，咱们不难发现:

• 首先创立了一个10分钟的快照，规定了每次回来的数据量为2条,并且初始化的时分，回来了2条
• 经过scroll_id进行翻滚操作，回来了1条数据,原因是快照的数据量一共只要3条，初始化的时分回来了2条，所以现在只要1条
• 再次翻滚的时分，发现回来了空，由于数据现已被查完了

结束语

本节就到此结束了，咱们一定要多去操练。下节咱们进入进阶查询部分内容 ~

本着把自己知道的都告诉咱们，假如本文对您有所帮助，点赞+重视鼓励一下呗~

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