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：
https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-6.4.1.tar.gz

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
https://artifacts.elastic.co/downloads/kibana/kibana-6.4.1-linux-x86_64.tar.gz

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

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 rock climbing"     ]    }   }  ] }}

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。