检索

轻量搜索

在序章中员工的背景下进行下面的搜索

全部内容

通过_search返回某个索引库的全部内容

请求URL GET /megacorp/employee/_search

返回JSON

{
    "took": 18,
    "timed_out": false,
    "_shards": {
        "total": 5,
        "successful": 5,
        "failed": 0
    },
    "hits": {
        "total": 3,
        "max_score": 1.0,
        "hits": [
            {
                "_index": "megacorp",
                "_type": "employee",
                "_id": "2",
                "_score": 1.0,
                "_source": {
                    "first_name": "Jane",
                    "last_name": "Smith",
                    "age": 32,
                    "about": "I like to collect rock albums",
                    "interests": [
                        "music"
                    ]
                }
            },
            .........
        ]
    }
}

高亮搜索

在_search的基础上，追加地址栏参数的方式进行检索

ps：此步骤采用lastName为Fir作为条件进行检索

请求URL：GET /megacorp/employee/_search?q=last_name:Fir

响应JSON

{
    "took": 2,
    "timed_out": false,
    "_shards": {
        "total": 5,
        "successful": 5,
        "failed": 0
    },
    "hits": {
        "total": 1,
        "max_score": 0.30685282,
        "hits": [
            {
                "_index": "megacorp",
                "_type": "employee",
                "_id": "3",
                "_score": 0.30685282,
                "_source": {
                    "first_name": "Douglas",
                    "last_name": "Fir",
                    "age": 35,
                    "about": "I like to build cabinets",
                    "interests": [
                        "forestry"
                    ]
                }
            }
        ]
    }
}

查询表达式搜索

查询表达式搜索是ES在搜索中的领域特定语言(DSL)，它支持构建复杂且健壮的语言。虽然轻量搜索可以进行检索，但是使用的场景极为局限

使用方式

探讨使用方式这里，直接对之前的轻量查询进行改造

URL：/megacorp/employee/_search
请求体JSON
```
{
    "query" : {
        "match" : {
            "last_name" : "Smith"
        }
    }
}
```
可以看到在请求体中对原来的轻量级查询进行改造，由地址传参，采用在query,match下的字段进行匹配查询条件，进而搜索

响应体JSON

{
    "took": 2,
    "timed_out": false,
    "_shards": {
        "total": 5,
        "successful": 5,
        "failed": 0
    },
    "hits": {
        "total": 1,
        "max_score": 0.30685282,
        "hits": [
            {
                "_index": "megacorp",
                "_type": "employee",
                "_id": "3",
                "_score": 0.30685282,
                "_source": {
                    "first_name": "Douglas",
                    "last_name": "Fir",
                    "age": 35,
                    "about": "I like to build cabinets",
                    "interests": [
                        "forestry"
                    ]
                }
            }
        ]
    }
}

更复杂的搜索

基本的搜索显然是不符合强大的ES的，所以采用更复杂的搜索，比如lastName是Smith，且年龄大于30的数据

使用方式

请求URL：/megacorp/employee/_search

请求JSON

{
    "query" : {
        "bool": {
            "must": {
                "match" : {
                    "last_name" : "smith" 
                }
            },
            "filter": {
                "range" : {
                    "age" : { "gt" : 30 } 
                }
            }
        }
    }
}

请求JSON中在Match的基础上我们增加了filter字段进行过滤，filter中采用range表示范围，故此在range中指定要过滤的字段age，进行范围的确定

响应JSON

{
    "took": 2,
    "timed_out": false,
    "_shards": {
        "total": 5,
        "successful": 5,
        "failed": 0
    },
    "hits": {
        "total": 1,
        "max_score": 0.30685282,
        "hits": [
            {
                "_index": "megacorp",
                "_type": "employee",
                "_id": "2",
                "_score": 0.30685282,
                "_source": {
                    "first_name": "Jane",
                    "last_name": "Smith",
                    "age": 32,
                    "about": "I like to collect rock albums",
                    "interests": [
                        "music"
                    ]
                }
            }
        ]
    }
}

显然年龄小于32岁的并能没有筛选出来

全文搜索

根据年龄筛选，根据姓名检索在传统的关系型数据库中都可以做到，接下来尝试传统关系型数据库做不到的事情

使用方式

需求 搜索关于字段中所有喜欢攀岩的员工

请求URL：GET /megacorp/employee/_search

请求JSON：依然采用查询表达式搜索 中的请求体

{
    "query" : {
        "match" : {
            "about" : "rock climbing"
        }
    }
}

响应JSON：结果符合预期，在about属性中有2个员工喜欢爬山所以被检索出来

{
    "took": 38,
    "timed_out": false,
    "_shards": {
        "total": 5,
        "successful": 5,
        "failed": 0
    },
    "hits": {
        "total": 2,
        "max_score": 0.16273327,
        "hits": [
            {
                "_index": "megacorp",
                "_type": "employee",
                "_id": "1",
                "_score": 0.16273327,
                "_source": {
                    "first_name": "John",
                    "last_name": "Smith",
                    "age": 25,
                    "about": "I love to go rock climbing",
                    "interests": [
                        "sports",
                        "music"
                    ]
                }
            },
            {
                "_index": "megacorp",
                "_type": "employee",
                "_id": "2",
                "_score": 0.016878016,
                "_source": {
                    "first_name": "Jane",
                    "last_name": "Smith",
                    "age": 32,
                    "about": "I like to collect rock albums",
                    "interests": [
                        "music"
                    ]
                }
            }
        ]
    }
}

值得注意，每条匹配记录中包含相关性得分字段"_score": 0.016878016

显然第2条记录其实并不是完全匹配，此情况若在关系型数据库中的like 查询将不会匹配，所以ES的相关性分数直接在全文检索场景下，与传统数据库拉开差距。我们可以使用相关性做很多的事情

ElasticSearch(二)

检索

轻量搜索

全部内容

高亮搜索

查询表达式搜索

使用方式

更复杂的搜索

使用方式

全文搜索

使用方式