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Elasticsearch 入门教程

Elasticsearch 入门教程

本文根据 指南，基于​

​docker​

​ 容器快速搭建 ​

​Elasticsearch​

​ 环境，并结   ​

​Elasticsearch​

​ 快速入门进行总结。 

安装

官网安装教程地址：

​

​https://www.elastic.co/guide/en/elasticsearch/reference/current/getting-started.html​

基本概念

1. Node 与 Cluster

​Elastic​

​ 本质上是一个分布式数据库，允许多台服务器协同工作，每台服务器可以运行多个 ​

​Elastic​

​ 实例，单个 ​

​Elastic​

​ 实例称为一个节点（​

​node​

​），一组节点构成一个集群（​

​cluster​

​）。

2. Index

​Elastic​

​ 会索引所有字段，经过处理后写入一个反向索引（​

​Inverted Index​

​）。查找数据的时候，直接查找该索引。

所以，​

​Elastic​

​ 数据管理的顶层单位就叫做 ​

​Index​

​（索引）。它是单个数据库的同义词。每个 ​

​Index​

​ （即数据库）的名字必须是小写。

下面的命令可以查看当前节点的所有 ​

​Index​

​。

$ curl -X GET 'http://localhost:9200/_cat/indices?v'

3. 添加单个数据 

POST logs-my_app-default/_doc
{
"@timestamp": "2099-05-06T16:21:15.000Z",
"event": {
"original": "192.0.2.42 - - [06/May/2099:16:21:15 +0000] \"GET /images/bg.jpg HTTP/1.0\" 200 24736"
  }
}

结果： 

{
"_index": ".ds-logs-my_app-default-2099-05-06-000001",
"_type": "_doc",
"_id": "gl5MJXMBMk1dGnErnBW8",
"_version": 1,
"result": "created",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
  },
"_seq_no": 0,
"_primary_term": 1
}

4. 添加多个数据 

PUT logs-my_app-default/_bulk
{ "create": { } }
{ "@timestamp": "2099-05-07T16:24:32.000Z", "event": { "original": "192.0.2.242 - - [07/May/2020:16:24:32 -0500] \"GET /images/hm_nbg.jpg HTTP/1.0\" 304 0" } }
{ "create": { } }
{ "@timestamp": "2099-05-08T16:25:42.000Z", "event": { "original": "192.0.2.255 - - [08/May/2099:16:25:42 +0000] \"GET /favicon.ico HTTP/1.0\" 200 3638" } }

5. 搜索数据

查询所有匹配数据：​

​logs-my_app-default​

​，并以​

​@timestamp​

​ 降序显示

GET logs-my_app-default/_search
{
"query": {
"match_all": { }
  },
"sort": [
    {
"@timestamp": "desc"
    }
  ]
}
      

{
"took": 2,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
  },
"hits": {
"total": {
"value": 3,
"relation": "eq"
    },
"max_score": null,
"hits": [
      {
"_index": ".ds-logs-my_app-default-2099-05-06-000001",
"_type": "_doc",
"_id": "PdjWongB9KPnaVm2IyaL",
"_score": null,
"_source": {
"@timestamp": "2099-05-08T16:25:42.000Z",
"event": {
"original": "192.0.2.255 - - [08/May/2099:16:25:42 +0000] \"GET /favicon.ico HTTP/1.0\" 200 3638"
          }
        },
"sort": [
4081940742000
        ]
      },
      ...
    ]
  }
}

6. 解析固定字段，去除一些字段： 

GET logs-my_app-default/_search
{
"query": {
"match_all": { }
  },
"fields": [
"@timestamp"
  ],
"_source": false,
"sort": [
    {
"@timestamp": "desc"
    }
  ]
}

      

{
  ...
"hits": {
    ...
"hits": [
      {
"_index": ".ds-logs-my_app-default-2099-05-06-000001",
"_type": "_doc",
"_id": "PdjWongB9KPnaVm2IyaL",
"_score": null,
"fields": {
"@timestamp": [
"2099-05-08T16:25:42.000Z"
          ]
        },
"sort": [
4081940742000
        ]
      },
      ...
    ]
  }
}

​"fields"​

​挑选字段解析，​

​'_source':false,​

​该字段不再显示

7. 范围搜索 ​

​range​

 

GET logs-my_app-default/_search
{
"query": {
"range": {
"@timestamp": {
"gte": "2099-05-05",
"lt": "2099-05-08"
      }
    }
  },
"fields": [
"@timestamp"
  ],
"_source": false,
"sort": [
    {
"@timestamp": "desc"
    }
  ]
}

查询过去一天的数据

GET logs-my_app-default/_search
{
"query": {
"range": {
"@timestamp": {
"gte": "now-1d/d",
"lt": "now/d"
      }
    }
  },
"fields": [
"@timestamp"
  ],
"_source": false,
"sort": [
    {
"@timestamp": "desc"
    }
  ]
}

8. 新建 索引 ​

​Index​

 

PUT my_index
{
"mappings": 
  {
"properties": 
    {
"address":
      {
"type": "ip"
      },
"port":
      {
"type": "long"
      }
    }
  }
}
      

{
"acknowledged" : true,
"shards_acknowledged" : true,
"index" : "my_index"
}

9. 将一些文档加载到其中: 

POST my_index/_bulk
{"index":{"_id":"1"}}
{"address":"1.2.3.4","port":"80"}
{"index":{"_id":"2"}}
{"address":"1.2.3.4","port":"8080"}
{"index":{"_id":"3"}}
{"address":"2.4.8.16","port":"80"}

返回结果: 

{
"took" : 8,
"errors" : false,
"items" : [
    {
"index" : {
"_index" : "my_index",
"_type" : "_doc",
"_id" : "1",
"_version" : 1,
"result" : "created",
"_shards" : {
"total" : 2,
"successful" : 1,
"failed" : 0
        },
"_seq_no" : 0,
"_primary_term" : 1,
"status" : 201
      }
    },
    {
"index" : {
"_index" : "my_index",
"_type" : "_doc",
"_id" : "2",
"_version" : 1,
"result" : "created",
"_shards" : {
"total" : 2,
"successful" : 1,
"failed" : 0
        },
"_seq_no" : 1,
"_primary_term" : 1,
"status" : 201
      }
    },
    {
"index" : {
"_index" : "my_index",
"_type" : "_doc",
"_id" : "3",
"_version" : 1,
"result" : "created",
"_shards" : {
"total" : 2,
"successful" : 1,
"failed" : 0
        },
"_seq_no" : 2,
"_primary_term" : 1,
"status" : 201
      }
    }
  ]
}

10. 使用静态字符串创建两个 

GET my_index/_search
 {
"runtime_mappings": {
"socket": {
"type": "keyword",
"script": {
"source": "emit(doc['address'].value + ':' + doc['port'].value)"
       }
     }
   },
"fields": [
"socket"
   ],
"query": {
"match": {
"socket": "1.2.3.4:8080"
     }
   }
}

      

{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
  },
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
    },
"max_score" : 1.0,
"hits" : [
      {
"_index" : "my_index",
"_type" : "_doc",
"_id" : "2",
"_score" : 1.0,
"_source" : {
"address" : "1.2.3.4",
"port" : "8080"
        },
"fields" : {
"socket" : [
"1.2.3.4:8080"
          ]
        }
      }
    ]
  }
}

上面代码中，返回结果的 ​

​took​

​字段表示该操作的耗时（单位为毫秒），​

​timed_out​

​字段表示是否超时，​

​hits​

​字段表示命中的记录，里面子字段的含义如下：

  • ​total​

     ​：返回记录数，本例是2条。

  • ​max_score​

     ​：最高的匹配程度，本例是1.0。

  • ​hits​

     ​：返回的记录组成的数组。

返回的数据中，​

​found​

​字段表示查询成功，​

​_source​

​字段返回原始记录。

我们在 ​

​runtime_mappings​

​ 部分中定义了字段 ​

​socket​

​。 我们使用了一个简短的 ​

​painless script​

​，该脚本定义了每个文档将如何计算 ​

​socket​

​ 的值（使用 + 表示 ​

​address​

​ 字段的值与静态字符串 “:” 和 ​

​port​

​ 字段的值的串联）。 然后，我们在查询中使用了字段 ​

​socket​

​。 字段 ​

​socket​

​ 是一个临时运行时字段，仅对于该查询存在，并且在运行查询时进行计算。 在定义要与 ​

​runtime fields​

​ 一起使用的 ​

​painless script​

​ 时，必须包括 ​

​emit​

​ 以返回计算出的值。

​socket​

​ :运行时加入的字段。​

​source​

​, ​

​id​
官方文档：​

​The script itself, which you specify as source for an inline script or id for a stored script. Use the stored script APIs to create and manage stored scripts.​

​"source": "emit(doc['address'].value + ':' + doc['port'].value)" 为内嵌脚本​

11. 如果我们发现 ​

​socket​

 ​ 是一个我们想在多个查询中使用的字段，而不必为每个查询定义它，则可以通过调用简单地将其添加到映射中： 

PUT my_index/_mapping
 {
"runtime": {
"socket": {
"type": "keyword",
"script": {
"source": "emit(doc['address'].value + ':' + doc['port'].value)"
       }
     } 
   } 
}

结果: 

{
"acknowledged" : true
}

此时在​

​Index mapping​

​ 文件里已经存在​

​socket​

​字段,然后查询,不必在运行时定义包含 ​

​socket​

​ 字段，例如

GET my_index/_search
{
"fields": [
"socket"
  ],
"query": {
"match": {
"socket": "1.2.3.4:8080"
    }
  }
}

结果（和使用静态字符串创建两个结果一样）: 

{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
  },
"hits" : {
"total" : {
"value" : 1,
"relation" : "eq"
    },
"max_score" : 1.0,
"hits" : [
      {
"_index" : "my_index",
"_type" : "_doc",
"_id" : "2",
"_score" : 1.0,
"_source" : {
"address" : "1.2.3.4",
"port" : "8080"
        },
"fields" : {
"socket" : [
"1.2.3.4:8080"
          ]
        }
      }
    ]
  }
}

仅在要显示 ​

​socket​

​ 字段的值时才需要语句 ​

​"fields": ["socket"]​

​。 现在，字段查询可用于任何查询，但它不存在于索引中，并且不会增加索引的大小。 仅在查询需要 ​

​socket​

​ 以及需要它的文档时才计算 ​

​socket​

12. ​

​runtime​

 ​和 ​

​runtime_mapping​

 ​区别：

使用​

​runtime​

​ 时定义的字段会存储到​

​Index​

​映射中，而​

​runtime_mapping​

​ 定义的字段只存在运行查询中。

映射字段：​

​https://www.elastic.co/guide/en/elasticsearch/reference/7.11/runtime-mapping-fields.html#runtime-mapping-fields​

请求字段： ​

​https://www.elastic.co/guide/en/elasticsearch/reference/7.11/runtime-search-request.html#runtime-search-request​

13. 在查询时覆盖字段值 

PUT my_raw_index
{
"mappings": {
"properties": {
"raw_message": {
"type": "keyword"
      },
"address": {
"type": "ip"
      }
    }
  }
}

      

{
"acknowledged" : true,
"shards_acknowledged" : true,
"index" : "my_raw_index"
}
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