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Centos7安裝ElasticSearch 6.4.1入門教程詳解
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1.下載ElasticSearch 6.4.1安裝包 下載地址：
https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-6.4.1.tar.gz

2.解壓壓縮包

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

3.啟動ElasticSearch

[root@localhost bin]# ./elasticsearch

以后臺方式啟動

[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用戶角色啟動，因此需要將安裝目錄授權(quán)給其他用戶，用其他用戶來啟動

啟動成功后，驗證，打開新的終端，執(zhí)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 ~]#

返回信息則表示安裝成功！

4.安裝Kibana

Sense 是一個 Kibana 應用 它提供交互式的控制臺，通過你的瀏覽器直接向 Elasticsearch 提交請求。 這本書的在線版本包含有一個 View in Sense 的鏈接，里面有許多代碼示例。當點擊的時候，它會打開一個代碼示例的Sense控制臺。 你不必安裝 Sense，但是它允許你在本地的 Elasticsearch 集群上測試示例代碼，從而使本書更具有交互性。

下載kibana

Kibana是一個為 ElasticSearch 提供的數(shù)據(jù)分析的 Web 接口?？墒褂盟鼘θ罩具M行高效的搜索、可視化、分析等各種操作
https://artifacts.elastic.co/downloads/kibana/kibana-6.4.1-linux-x86_64.tar.gz

下載完成解壓Kibana

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

修改  配置config目錄下的kibana.yml 文件,配置elasticsearch地址和kibana地址信息

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

啟動 Kibana

[root@localhost bin]# ./kibana

安裝Kibana本機訪問：http://localhost:5601/

選擇Dev Tools菜單，即可實現(xiàn)可視化請求

5.安裝LogStash

下載logStash
https://artifacts.elastic.co/downloads/logstash/logstash-7.0.1.tar.gz

下載完成解壓后，config目錄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}"
 }
}

配置成功后啟動logstatsh

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

ES  一些基礎(chǔ)知識：

索引（名詞）：

如前所述，一個 索引 類似于傳統(tǒng)關(guān)系數(shù)據(jù)庫中的一個 數(shù)據(jù)庫 ，是一個存儲關(guān)系型文檔的地方。 索引 (index) 的復數(shù)詞為 indices 或 indexes 。

索引（動詞）：

索引一個文檔 就是存儲一個文檔到一個 索引 （名詞）中以便它可以被檢索和查詢到。這非常類似于 SQL 語句中的 INSERT 關(guān)鍵詞，除了文檔已存在時新文檔會替換舊文檔情況之外。

倒排索引：

關(guān)系型數(shù)據(jù)庫通過增加一個 索引 比如一個 B樹（B-tree）索引 到指定的列上，以便提升數(shù)據(jù)檢索速度。Elasticsearch 和 Lucene 使用了一個叫做 倒排索引 的結(jié)構(gòu)來達到相同的目的。

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

返回結(jié)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
}

路徑 /megacorp/employee/1 包含了三部分的信息：

megacorp 索引名稱

employee  類型名稱

1        特定雇員的ID

放置第二個雇員信息：

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

返回結(jié)果：

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

放置第三個雇員信息

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

5.檢索文檔

檢索到單個雇員的數(shù)據(jù)

GET /megacorp/employee/1

返回結(jié)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.輕量搜索

一個 GET 是相當簡單的，可以直接得到指定的文檔。 現(xiàn)在嘗試點兒稍微高級的功能，比如一個簡單的搜索！

第一個嘗試的幾乎是最簡單的搜索了。我們使用下列請求來搜索所有雇員：

GET /megacorp/employee/_search

返回結(jié)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"
     ]
    }
   }
  ]
 }
}

通過姓名模糊匹配來獲得結(jié)果

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

返回結(jié)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.使用查詢表達式搜索

領(lǐng)域特定語言 （DSL）， 指定了使用一個 JSON 請求

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

返回結(jié)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.更復雜的搜索

搜索姓氏為 Smith 的雇員，但這次我們只需要年齡大于 30 的，使用過濾器 filter ，它支持高效地執(zhí)行一個結(jié)構(gòu)化查詢

GET /megacorp/employee/_search
{
  "query" : {
    "bool": {
      "must": {
        "match" : {
          "last_name" : "smith" 
        }
      },
      "filter": {
        "range" : {
          "age" : { "gt" : 30 } 
        }
      }
    }
  }
}

其中：range 過濾器 ， 它能找到年齡大于 30 的文檔，其中 gt 表示_大于(_great than)

返回結(jié)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

搜索下所有喜歡攀巖（rock climbing）的雇員

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

返回結(jié)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

找出一個屬性中的獨立單詞是沒有問題的，但有時候想要精確匹配一系列單詞或者短語 。 比如， 我們想執(zhí)行這樣一個查詢，僅匹配同時包含 “rock” 和 “climbing” ，并且 二者以短語 “rock climbing” 的形式緊挨著的雇員記錄。

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

返回結(jié)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

許多應用都傾向于在每個搜索結(jié)果中 高亮 部分文本片段，以便讓用戶知道為何該文檔符合查詢條件。在 Elasticsearch 中檢索出高亮片段也很容易。

增加參數(shù)： highlight

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

返回結(jié)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>"
     ]
    }
   }
  ]
 }
}

其中高亮模塊為highlight屬性

12.分析

Elasticsearch 有一個功能叫聚合（aggregations），允許我們基于數(shù)據(jù)生成一些精細的分析結(jié)果。聚合與 SQL 中的 GROUP BY 類似但更強大。

舉個例子，挖掘出雇員中最受歡迎的興趣愛好：

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

返回結(jié)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內(nèi)容，希望對大家的學習有所幫助，也希望大家多多支持腳本之家。

標簽：張家界 廊坊 永州 滄州 松原 北京 洛陽 玉樹

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