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ElasticSearch学习之多条件组合查询验证及示例分析

ElasticSearch 多条件组合查询 ElasticSearch 组合查询 2023-02-02 18:02:27 306人浏览泡泡鱼

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摘要

目录多条件组合查询boolconstant_score查询验证 & 分析验证分析排序默认排序自定义排序tips单字段排序多字段scroll分页初始化快照 & 快照保存

多条件组合查询

bool

es中使用bool来控制多条件查询，bool查询支持以下参数:

must：被查询的数据必须满足当前条件
mush_not：被查询的数据必须不满足当前条件
should：被查询的数据应该满足当前条件。should查询被用于修正查询结果的评分。需要注意的是，如果组合查询中没有must，那么被查询的数据至少要匹配一条should。如果有must语句，那么就无须匹配should，should将完全用于修正查询结果的评分
filter：被查询的数据必须满足当前条件，但是filter操作不涉及查询结果评分。仅用于条件过滤

下面通过一个例子来看下如何使用:

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
          }
        }}
      ]
    }
  }
}

结果返回:

{
  "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查询可以通过boost指定一个固定的评分，通常来说，constant_score的作用是代替一个只有filter的bool查询

下面看具体使用：

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

{
  "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中通过/_validate/query路由来验证查询条件的正确性, 这里要注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"
        }}
      ]
    }
  }
}

正常返回:

{
  "_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]")"""
    }
  ]
}

可以看到报了异常错误

分析

es中通过/_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"
        }}
      ]
    }
  }
}

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

可以看到"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

默认排序

在前面的几个例子中，我们可以看到它的默认排序是按照_score降序，也就是匹配度高的比较靠前,但是_socre的计算是很占用查询性能的,这个不难理解。

当我们不需要进行_score计算，可以通过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
        }}
      ]
    }
  }
}

{
  "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
        }
      }
    ]
  }
}

通过查询结果我们发现score都为0.0了，说明没有进行score计算

constant_score示例：

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

{
  "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
        }
      }
    ]
  }
}

可以看到，对应返回的分值，都是使用boost属性指定的分值

自定义排序

自定义可以用于大部分场景，那么es中怎么进行自定义排序呢? es中使用sort参数来自定义排序顺序，默认为升序，那么降序怎么操作呢？

升序

{"sort":["num"]}

降序, desc代表降序

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

tips

es中使用doc value列式存储来实现字段的排序功能
text字段默认不创建doc value，因此无法针对text字段进行排序
可以通过设置text字段属性fielddata=true来开启对text字段的排序功能，但是不建议开启，对text字段排序及其消耗查询性能且不符合需求

单字段排序

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

{
  "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
        ]
      }
    ]
  }
}

可以看到是按照num默认升序排序

再看下降序:

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

{
  "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

还记得之前给大家讲的from+size的分页方式吗，es中默认允许from+size的分页的最大数据量为10000。当我们想要批量获取更大的数据量时，使用from+size就会十分的耗费性能。

然而大部分应用场景下的数据量是极其庞大的，比如你要查询某些系统日志数据。es中可以使用/scorll路由来进行滚动分页查询，它类似于在查询初始时间点创建了一个当前服务集群的数据快照（包含每一个分片），并保留它一段时间。在时间超过了设置的过期时间以后，快照将在es空闲时被删除。

需要注意的是，因为是进行快照查询，因此在快照创建后数据的变更在本次的滚动查询中，不可见

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
}

{
  "_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，当前共返回2条数据，并且返回了一个快照ID，后续可以根据快照ID进行滚动查询:

根据快照ID滚动查询

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

{
  "_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
        }
      }
    ]
  }
}

在滚动一次:

{
  "_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" : [ ]
  }
}

有的小伙伴可能不知道怎么滚动的，因为后续滚动都是同一个scroll_id,其实通过结果，我们不难发现:

首先创建了一个10分钟的快照，规定了每次返回的数据量为2条,并且初始化的时候，返回了2条
通过scroll_id进行滚动操作，返回了1条数据,原因是快照的数据量总共只有3条，初始化的时候返回了2条，所以现在只有1条
再次滚动的时候，发现返回了空，因为数据已经被查完了

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ElasticSearch学习之多条件组合查询验证及示例分析

目录

多条件组合查询

bool

constant_score

查询验证 & 分析

验证

分析

排序

默认排序

自定义排序

tips

单字段排序

多字段

scroll分页

初始化快照 & 快照保存10分钟

根据快照ID滚动查询

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