Centos7安装ElasticSearch 6.4.1入门教程详解

1.下载ElasticSearch 6.4.1安装包下载地址：

2.解压压缩包

  [root@localhost ElasticSearch]# tar -zxvf elasticsearch-6.4.1.tar.gz

3.启动ElasticSearch

  [root@localhost bin]# ./elasticsearch

以后台方式启动

  [root@localhost bin]# ./elasticsearch -d

TIPS:

  [root@localhost bin]# ./elasticsearch  [2018-09-19T19:46:09,817][WARN ][o.e.b.ElasticsearchUncaughtExceptionHandler] [] uncaught exception in thread [main]  org.elasticsearch.bootstrap.StartupException: java.lang.RuntimeException: can not run elasticsearch as root    at org.elasticsearch.bootstrap.Elasticsearch.init(Elasticsearch.java:140) ~[elasticsearch-6.4.1.jar:6.4.1]    at org.elasticsearch.bootstrap.Elasticsearch.execute(Elasticsearch.java:127) ~[elasticsearch-6.4.1.jar:6.4.1]    at org.elasticsearch.cli.EnvironmentAwareCommand.execute(EnvironmentAwareCommand.java:86) ~[elasticsearch-6.4.1.jar:6.4.1]    at org.elasticsearch.cli.Command.mainWithoutErrorHandling(Command.java:124) ~[elasticsearch-cli-6.4.1.jar:6.4.1]    at org.elasticsearch.cli.Command.main(Command.java:90) ~[elasticsearch-cli-6.4.1.jar:6.4.1]    at org.elasticsearch.bootstrap.Elasticsearch.main(Elasticsearch.java:93) ~[elasticsearch-6.4.1.jar:6.4.1]    at org.elasticsearch.bootstrap.Elasticsearch.main(Elasticsearch.java:86) ~[elasticsearch-6.4.1.jar:6.4.1]  Caused by: java.lang.RuntimeException: can not run elasticsearch as root    at org.elasticsearch.bootstrap.Bootstrap.initializeNatives(Bootstrap.java:104) ~[elasticsearch-6.4.1.jar:6.4.1]    at org.elasticsearch.bootstrap.Bootstrap.setup(Bootstrap.java:171) ~[elasticsearch-6.4.1.jar:6.4.1]    at org.elasticsearch.bootstrap.Bootstrap.init(Bootstrap.java:326) ~[elasticsearch-6.4.1.jar:6.4.1]    at org.elasticsearch.bootstrap.Elasticsearch.init(Elasticsearch.java:136) ~[elasticsearch-6.4.1.jar:6.4.1]

ElasticSearch 不能以root用户角色启动，因此需要将安装目录授权给其他用户，用其他用户来启动

启动成功后，验证，打开新的终端，执行如下命令：

  [root@localhost ~]# curl 'http://localhost:9200/?pretty'  {   "name" : "O5BAVYE",   "cluster_name" : "elasticsearch",   "cluster_uuid" : "rw1yjlzkSgODXkUVgIxmxg",   "version" : {    "number" : "6.4.1",    "build_flavor" : "default",    "build_type" : "tar",    "build_hash" : "e36acdb",    "build_date" : "2018-09-13T22:18:07.696808Z",    "build_snapshot" : false,    "lucene_version" : "7.4.0",    "minimum_wire_compatibility_version" : "5.6.0",    "minimum_index_compatibility_version" : "5.0.0"   },   "tagline" : "You Know, for Search"  }  [root@localhost ~]#

返回信息则表示安装成功！

4.安装Kibana

Sense 是一个 Kibana 应用它提供交互式的控制台，通过你的浏览器直接向 Elasticsearch 提交请求。这本书的在线版本包含有一个 View in Sense 的链接，里面有许多代码示例。当点击的时候，它会打开一个代码示例的Sense控制台。你不必安装 Sense，但是它允许你在本地的 Elasticsearch 集群上测试示例代码，从而使本书更具有交互性。

下载kibana

Kibana是一个为 ElasticSearch 提供的数据分析的 Web 接口。可使用它对日志进行高效的搜索、可视化、分析等各种操作

下载完成解压Kibana

  [root@localhost ElasticSearch]# tar -zxvf kibana-6.4.1-linux-x86_64.tar.gz

修改配置config目录下的kibana.yml 文件,配置elasticsearch地址和kibana地址信息

  server.host: "192.168.92.50" # kibana 服务器地址  elasticsearch.url: "http://192.168.92.50:9200"  # ES 地址

启动 Kibana

  [root@localhost bin]# ./kibana

安装Kibana本机访问：http://localhost:5601/

选择Dev Tools菜单，即可实现可视化请求

5.安装LogStash

下载logStash

下载完成解压后，config目录下配置日志收集日志配置文件 logstash.conf

  # Sample Logstash configuration for creating a simple  # Beats -> Logstash -> Elasticsearch pipeline.    input {   tcp {    mode => "server"    host => "192.168.92.50"    port => 4560    codec => json_lines   }  }  output {   elasticsearch {    hosts => "192.168.92.50:9200"    index => "springboot-logstash-%{+YYYY.MM.dd}"   }  }

配置成功后启动logstatsh

  [root@localhost bin]# ./logstash -f ../config/logstash.conf

ES 一些基础知识：

索引（名词）：

如前所述，一个索引类似于传统关系数据库中的一个数据库，是一个存储关系型文档的地方。索引 (index) 的复数词为 indices 或 indexes 。

索引（动词）：

索引一个文档就是存储一个文档到一个索引（名词）中以便它可以被检索和查询到。这非常类似于 SQL 语句中的 INSERT 关键词，除了文档已存在时新文档会替换旧文档情况之外。

倒排索引：

关系型数据库通过增加一个索引比如一个 B树（B-tree）索引到指定的列上，以便提升数据检索速度。Elasticsearch 和 Lucene 使用了一个叫做倒排索引的结构来达到相同的目的。

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

返回结果：

  #! Deprecation: the default number of shards will change from [5] to [1] in 7.0.0; if you wish to continue using the default of [5] shards, you must manage this on the create index request or with an index template  {   "_index": "megacorp",   "_type": "employee",   "_id": "1",   "_version": 1,   "result": "created",   "_shards": {    "total": 2,    "successful": 1,    "failed": 0   },   "_seq_no": 0,   "_primary_term": 1  }

路径 /megacorp/employee/1 包含了三部分的信息：

megacorp 索引名称

employee 类型名称

1 特定雇员的ID

放置第二个雇员信息：

  {   "_index": "megacorp",   "_type": "employee",   "_id": "2",   "_version": 1,   "result": "created",   "_shards": {    "total": 2,    "successful": 1,    "failed": 0   },   "_seq_no": 0,   "_primary_term": 1  }

返回结果：

  {   "_index": "megacorp",   "_type": "employee",   "_id": "2",   "_version": 1,   "result": "created",   "_shards": {    "total": 2,    "successful": 1,    "failed": 0   },   "_seq_no": 0,   "_primary_term": 1  }

放置第三个雇员信息

  {   "_index": "megacorp",   "_type": "employee",   "_id": "3",   "_version": 1,   "result": "created",   "_shards": {    "total": 2,    "successful": 1,    "failed": 0   },   "_seq_no": 0,   "_primary_term": 1  }

5.检索文档

检索到单个雇员的数据

GET /megacorp/employee/1

返回结果：

  {   "_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"    ]   }  }

6.轻量搜索

一个 GET 是相当简单的，可以直接得到指定的文档。现在尝试点儿稍微高级的功能，比如一个简单的搜索！

第一个尝试的几乎是最简单的搜索了。我们使用下列请求来搜索所有雇员：

GET /megacorp/employee/_search

返回结果：

  {   "took": 31,   "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"       ]      }     }    ]   }  }

通过姓名模糊匹配来获得结果

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

返回结果：

  {   "took": 414,   "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"       ]      }     }    ]   }  }

7.使用查询表达式搜索

领域特定语言（DSL），指定了使用一个 JSON 请求

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

返回结果：

  {   "took": 7,   "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"       ]      }     }    ]   }  }

8.更复杂的搜索

搜索姓氏为 Smith 的雇员，但这次我们只需要年龄大于 30 的，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" : { "gt" : 30 }           }        }      }    }  }

其中：range 过滤器，它能找到年龄大于 30 的文档，其中 gt 表示_大于(_great than)

返回结果：

  {   "took": 44,   "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"       ]      }     }    ]   }  }

9.全文搜索

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": 17,   "timed_out": false,   "_shards": {    "total": 5,    "successful": 5,    "skipped": 0,    "failed": 0   },   "hits": {    "total": 2,    "max_score": 0.5753642,    "hits": [     {      "_index": "megacorp",      "_type": "employee",      "_id": "1",      "_score": 0.5753642,      "_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": 0.2876821,      "_source": {       "first_name": "Jane",       "last_name": "Smith",       "age": 32,       "about": "I like to collect rock albums",       "interests": [        "music"       ]      }     }    ]   }  }

10.全文搜索

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。

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

返回结果：

  {   "took": 142,   "timed_out": false,   "_shards": {    "total": 5,    "successful": 5,    "skipped": 0,    "failed": 0   },   "hits": {    "total": 1,    "max_score": 0.5753642,    "hits": [     {      "_index": "megacorp",      "_type": "employee",      "_id": "1",      "_score": 0.5753642,      "_source": {       "first_name": "John",       "last_name": "Smith",       "age": 25,       "about": "I love to go rock climbing",       "interests": [        "sports",        "music"       ]      }     }    ]   }  }

11.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。

增加参数： highlight

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

返回结果:

  {   "took": 250,   "timed_out": false,   "_shards": {    "total": 5,    "successful": 5,    "skipped": 0,    "failed": 0   },   "hits": {    "total": 1,    "max_score": 0.5753642,    "hits": [     {      "_index": "megacorp",      "_type": "employee",      "_id": "1",      "_score": 0.5753642,      "_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>"       ]      }     }    ]   }  }

其中高亮模块为highlight属性

12.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" }    }   }  }

返回结果：

  {    ...    "hits": { ... },    "aggregations": {     "all_interests": {       "buckets": [        {          "key":    "music",          "doc_count": 2        },        {          "key":    "forestry",          "doc_count": 1        },        {          "key":    "sports",          "doc_count": 1        }       ]     }    }  }

以上就是本文的全部内容，希望对大家的学习有所帮助，也希望大家多多支持脚本之家。

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