elasticsearch索引并查询文档

2017-10-13 1702点热度 0人点赞 0条评论

在线文档地址

https://elasticsearch.cn/book/elasticsearch_definitive_guide_2.x/index.html

索引文档

在Elasticsearch集群中添加3条雇员信息:

PUT /megacorp/employee/1
{
    "first_name" : "John",
    "last_name" :  "Smith",
    "age" :        25,
    "about" :      "I love to go rock climbing",
    "interests": [ "sports", "music" ]
}
PUT /megacorp/employee/2
{
    "first_name" :  "Jane",
    "last_name" :   "Smith",
    "age" :         32,
    "about" :       "I like to collect rock albums",
    "interests":  [ "music" ]
}

PUT /megacorp/employee/3
{
    "first_name" :  "Douglas",
    "last_name" :   "Fir",
    "age" :         35,
    "about":        "I like to build cabinets",
    "interests":  [ "forestry" ]
}

检索文档

检索到单个雇员的数据

GET /megacorp/employee/1

将 HTTP 命令由 PUT 改为 GET 可以用来检索文档,同样的,可以使用 DELETE 命令来删除文档,以及使用 HEAD 指令来检查文档是否存在。如果想更新已存在的文档,只需再次 PUT 。
返回信息如下:

{
  "_index" :   "megacorp",
  "_type" :    "employee",
  "_id" :      "1",
  "_version" : 1,
  "found" :    true,
  "_source" :  {
      "first_name" :  "John",
      "last_name" :   "Smith",
      "age" :         25,
      "about" :       "I love to go rock climbing",
      "interests":  [ "sports", "music" ]
  }
}

轻量搜索

搜索所有雇员

GET /megacorp/employee/_search

可以看到,我们仍然使用索引库 megacorp 以及类型 employee,但与指定一个文档 ID 不同,这次使用_search 。返回结果包括了所有三个文档,放在数组 hits 中。一个搜索默认返回十条结果。
返回信息如下:

{
   "took":      6,
   "timed_out": false,
   "_shards": { ... },
   "hits": {
      "total":      3,
      "max_score":  1,
      "hits": [
         {
            "_index":         "megacorp",
            "_type":          "employee",
            "_id":            "3",
            "_score":         1,
            "_source": {
               "first_name":  "Douglas",
               "last_name":   "Fir",
               "age":         35,
               "about":       "I like to build cabinets",
               "interests": [ "forestry" ]
            }
         },
         {
            "_index":         "megacorp",
            "_type":          "employee",
            "_id":            "1",
            "_score":         1,
            "_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":         1,
            "_source": {
               "first_name":  "Jane",
               "last_name":   "Smith",
               "age":         32,
               "about":       "I like to collect rock albums",
               "interests": [ "music" ]
            }
         }
      ]
   }
}

搜索姓氏为"Smith"的雇员

为此,我们将使用一个 高亮 搜索,很容易通过命令行完成。这个方法一般涉及到一个 查询字符串 (query-string) 搜索,因为我们通过一个URL参数来传递查询信息给搜索接口:

GET /megacorp/employee/_search?q=last_name:Smith

我们仍然在请求路径中使用 _search 端点,并将查询本身赋值给参数 q= 。返回结果给出了所有的 Smith:

{
   ...
   "hits": {
      "total":      2,
      "max_score":  0.30685282,
      "hits": [
         {
            ...
            "_source": {
               "first_name":  "John",
               "last_name":   "Smith",
               "age":         25,
               "about":       "I love to go rock climbing",
               "interests": [ "sports", "music" ]
            }
         },
         {
            ...
            "_source": {
               "first_name":  "Jane",
               "last_name":   "Smith",
               "age":         32,
               "about":       "I like to collect rock albums",
               "interests": [ "music" ]
            }
         }
      ]
   }
}

使用查询表达式搜索

Query-string 搜索通过命令非常方便地进行临时性的即席搜索 ,但它有自身的局限性(参见 轻量 搜索 )。Elasticsearch 提供一个丰富灵活的查询语言叫做 查询表达式 , 它支持构建更加复杂和健壮的查询。

领域特定语言 (DSL), 指定了使用一个 JSON 请求。我们可以像这样重写之前的查询所有 Smith 的搜索 :

GET /megacorp/employee/_search
{
    "query" : {
        "match" : {
            "last_name" : "Smith"
        }
    }
}

返回结果与之前的查询一样,但还是可以看到有一些变化。其中之一是,不再使用 query-string 参数,而是一个请求体替代。这个请求使用 JSON 构造,并使用了一个 match 查询(属于查询类型之一)

更复杂的搜索

同样搜索姓氏为 Smith 的雇员,但这次我们只需要年龄大于 30 的。查询需要稍作调整,使用过滤器 filter ,它支持高效地执行一个结构化查询:
GET /megacorp/employee/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"last_name": "Smith"
}
}
],
"filter": {
"range": {
"age": {
"gte": 30
}
}
}
}
}
}

查询结果:

{
  "took": 10,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": 1,
    "max_score": 0.2876821,
    "hits": [
      {
        "_index": "megacorp",
        "_type": "employee",
        "_id": "2",
        "_score": 0.2876821,
        "_source": {
          "first_name": "Jane",
          "last_name": "Smith",
          "age": 32,
          "about": "I like to collect rock albums",
          "interests": [
            "music"
          ]
        }
      }
    ]
  }
}

全文搜索

截止目前的搜索相对都很简单:单个姓名,通过年龄过滤。现在尝试下稍微高级点儿的全文搜索——一项 传统数据库确实很难搞定的任务。

搜索下所有喜欢攀岩(rock climbing)的雇员:

GET /megacorp/employee/_search
{
    "query" : {
        "match" : {
            "about" : "rock climbing"
        }
    }
}

返回结果:

{
  "took": 13,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": 2,
    "max_score": 0.53484553,
    "hits": [
      {
        "_index": "megacorp",
        "_type": "employee",
        "_id": "1",
        "_score": 0.53484553,
        "_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": 0.26742277,
        "_source": {
          "first_name": "Jane",
          "last_name": "Smith",
          "age": 32,
          "about": "I like to collect rock albums",
          "interests": [
            "music"
          ]
        }
      }
    ]
  }
}

Elasticsearch 默认按照相关性得分排序,即每个文档跟查询的匹配程度。第一个最高得分的结果很明显:John Smith 的 about 属性清楚地写着 “rock climbing” 。

但为什么 Jane Smith 也作为结果返回了呢?原因是她的 about 属性里提到了 “rock” 。因为只有 “rock” 而没有 “climbing” ,所以她的相关性得分低于 John 的。

这是一个很好的案例,阐明了 Elasticsearch 如何 在 全文属性上搜索并返回相关性最强的结果。Elasticsearch中的 相关性 概念非常重要,也是完全区别于传统关系型数据库的一个概念,数据库中的一条记录要么匹配要么不匹配。

短语搜索

找出一个属性中的独立单词是没有问题的,但有时候想要精确匹配一系列单词或者短语 。 比如, 我们想执行这样一个查询,仅匹配同时包含 “rock” 和 “climbing” ,并且 二者以短语 “rock climbing” 的形式紧挨着的雇员记录。

为此对 match 查询稍作调整,使用一个叫做 match_phrase 的查询:

GET /megacorp/employee/_search
{
  "query": {
    "match_phrase": {
      "about": "rock climbing"
    }
  }
}

毫无悬念,返回结果仅有 John Smith 的文档:

{
  "took": 19,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": 1,
    "max_score": 0.53484553,
    "hits": [
      {
        "_index": "megacorp",
        "_type": "employee",
        "_id": "1",
        "_score": 0.53484553,
        "_source": {
          "first_name": "John",
          "last_name": "Smith",
          "age": 25,
          "about": "I love to go rock climbing",
          "interests": [
            "sports",
            "music"
          ]
        }
      }
    ]
  }
}

高亮搜索

许多应用都倾向于在每个搜索结果中 高亮 部分文本片段,以便让用户知道为何该文档符合查询条件。在 Elasticsearch 中检索出高亮片段也很容易。

再次执行前面的查询,并增加一个新的 highlight 参数:

GET /megacorp/employee/_search
{
  "query": {
    "match_phrase": {
      "about": "rock climbing"
    }
  },
  "highlight": {
    "fields": {
      "about": {}
    }
  }
}

当执行该查询时,返回结果与之前一样,与此同时结果中还多了一个叫做 highlight 的部分。这个部分包含了 about 属性匹配的文本片段,并以 HTML 标签 封装:

{
  "took": 144,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": 1,
    "max_score": 0.53484553,
    "hits": [
      {
        "_index": "megacorp",
        "_type": "employee",
        "_id": "1",
        "_score": 0.53484553,
        "_source": {
          "first_name": "John",
          "last_name": "Smith",
          "age": 25,
          "about": "I love to go rock climbing",
          "interests": [
            "sports",
            "music"
          ]
        },
        "highlight": {
          "about": [
            "I love to go <em>rock</em> <em>climbing</em>"
          ]
        }
      }
    ]
  }
}

关于高亮搜索片段,可以在 highlighting reference documentation 了解更多信息。

分析

终于到了最后一个业务需求:支持管理者对雇员目录做分析。 Elasticsearch 有一个功能叫聚合(aggregations),允许我们基于数据生成一些精细的分析结果。聚合与 SQL 中的 GROUP BY 类似但更强大。

举个例子,挖掘出雇员中最受欢迎的兴趣爱好:

GET /megacorp/employee/_search
{
  "aggs": {
    "all_interests": {
      "terms": { "field": "interests" }
    }
  }
}

这个时候报错,返回结果如下:

{
  "error": {
    "root_cause": [
      {
        "type": "illegal_argument_exception",
        "reason": "Fielddata is disabled on text fields by default. Set fielddata=true on [interests] in order to load fielddata in memory by uninverting the inverted index. Note that this can however use significant memory. Alternatively use a keyword field instead."
      }
    ],
    "type": "search_phase_execution_exception",
    "reason": "all shards failed",
    "phase": "query",
    "grouped": true,
    "failed_shards": [
      {
        "shard": 0,
        "index": "megacorp",
        "node": "_7Llp2cwTx-o5QnDaf5hww",
        "reason": {
          "type": "illegal_argument_exception",
          "reason": "Fielddata is disabled on text fields by default. Set fielddata=true on [interests] in order to load fielddata in memory by uninverting the inverted index. Note that this can however use significant memory. Alternatively use a keyword field instead."
        }
      }
    ]
  },
  "status": 400
}

聚合这些操作用单独的数据结构(fielddata)缓存到内存里了,需要单独开启,官方解释在此fielddata,简单来说就是在聚合前执行如下操作:

PUT megacorp/_mapping/employee/
{
  "properties": {
    "interests": {
      "type":     "text",
      "fielddata": true
    }
  }
}

返回结果:

{
  "acknowledged": true
}

再次执行聚合查询,返回结果如下:

{
  "took": 40,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": 3,
    "max_score": 1,
    "hits": [
      {
        "_index": "megacorp",
        "_type": "employee",
        "_id": "2",
        "_score": 1,
        "_source": {
          "first_name": "Jane",
          "last_name": "Smith",
          "age": 32,
          "about": "I like to collect rock albums",
          "interests": [
            "music"
          ]
        }
      },
      {
        "_index": "megacorp",
        "_type": "employee",
        "_id": "1",
        "_score": 1,
        "_source": {
          "first_name": "John",
          "last_name": "Smith",
          "age": 25,
          "about": "I love to go rock climbing",
          "interests": [
            "sports",
            "music"
          ]
        }
      },
      {
        "_index": "megacorp",
        "_type": "employee",
        "_id": "3",
        "_score": 1,
        "_source": {
          "first_name": "Douglas",
          "last_name": "Fir",
          "age": 35,
          "about": "I like to build cabinets",
          "interests": [
            "forestry"
          ]
        }
      }
    ]
  },
  "aggregations": {
    "all_interests": {
      "doc_count_error_upper_bound": 0,
      "sum_other_doc_count": 0,
      "buckets": [
        {
          "key": "music",
          "doc_count": 2
        },
        {
          "key": "forestry",
          "doc_count": 1
        },
        {
          "key": "sports",
          "doc_count": 1
        }
      ]
    }
  }
}

可以看到,两位员工对音乐感兴趣,一位对林地感兴趣,一位对运动感兴趣。这些聚合并非预先统计,而是从匹配当前查询的文档中即时生成。如果想知道叫 Smith 的雇员中最受欢迎的兴趣爱好,可以直接添加适当的查询来组合查询:

GET /megacorp/employee/_search
{
  "query": {
    "match": {
      "last_name": "smith"
    }
  },
  "aggs": {
    "all_interests": {
      "terms": {
        "field": "interests"
      }
    }
  }
}

返回结果:

{
  "took": 8,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": 2,
    "max_score": 0.2876821,
    "hits": [
      {
        "_index": "megacorp",
        "_type": "employee",
        "_id": "2",
        "_score": 0.2876821,
        "_source": {
          "first_name": "Jane",
          "last_name": "Smith",
          "age": 32,
          "about": "I like to collect rock albums",
          "interests": [
            "music"
          ]
        }
      },
      {
        "_index": "megacorp",
        "_type": "employee",
        "_id": "1",
        "_score": 0.2876821,
        "_source": {
          "first_name": "John",
          "last_name": "Smith",
          "age": 25,
          "about": "I love to go rock climbing",
          "interests": [
            "sports",
            "music"
          ]
        }
      }
    ]
  },
  "aggregations": {
    "all_interests": {
      "doc_count_error_upper_bound": 0,
      "sum_other_doc_count": 0,
      "buckets": [
        {
          "key": "music",
          "doc_count": 2
        },
        {
          "key": "sports",
          "doc_count": 1
        }
      ]
    }
  }
}

聚合还支持分级汇总 。比如,查询特定兴趣爱好员工的平均年龄:

GET /megacorp/employee/_search
{
    "aggs" : {
        "all_interests" : {
            "terms" : { "field" : "interests" },
            "aggs" : {
                "avg_age" : {
                    "avg" : { "field" : "age" }
                }
            }
        }
    }
}

返回结果:

{
  "took": 11,
  "timed_out": false,
  "_shards": {
    "total": 5,
    "successful": 5,
    "skipped": 0,
    "failed": 0
  },
  "hits": {
    "total": 3,
    "max_score": 1,
    "hits": [
      {
        "_index": "megacorp",
        "_type": "employee",
        "_id": "2",
        "_score": 1,
        "_source": {
          "first_name": "Jane",
          "last_name": "Smith",
          "age": 32,
          "about": "I like to collect rock albums",
          "interests": [
            "music"
          ]
        }
      },
      {
        "_index": "megacorp",
        "_type": "employee",
        "_id": "1",
        "_score": 1,
        "_source": {
          "first_name": "John",
          "last_name": "Smith",
          "age": 25,
          "about": "I love to go rock climbing",
          "interests": [
            "sports",
            "music"
          ]
        }
      },
      {
        "_index": "megacorp",
        "_type": "employee",
        "_id": "3",
        "_score": 1,
        "_source": {
          "first_name": "Douglas",
          "last_name": "Fir",
          "age": 35,
          "about": "I like to build cabinets",
          "interests": [
            "forestry"
          ]
        }
      }
    ]
  },
  "aggregations": {
    "all_interests": {
      "doc_count_error_upper_bound": 0,
      "sum_other_doc_count": 0,
      "buckets": [
        {
          "key": "music",
          "doc_count": 2,
          "avg_age": {
            "value": 28.5
          }
        },
        {
          "key": "forestry",
          "doc_count": 1,
          "avg_age": {
            "value": 35
          }
        },
        {
          "key": "sports",
          "doc_count": 1,
          "avg_age": {
            "value": 25
          }
        }
      ]
    }
  }
}

输出基本是第一次聚合的加强版。依然有一个兴趣及数量的列表,只不过每个兴趣都有了一个附加的 avg_age 属性,代表有这个兴趣爱好的所有员工的平均年龄。

王显锋

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