Centos7安装ElasticSearch实例分析-创新互联
今天小编给大家分享一下Centos7安装ElasticSearch实例分析的相关知识点，内容详细，逻辑清晰，相信大部分人都还太了解这方面的知识，所以分享这篇文章给大家参考一下，希望大家阅读完这篇文章后有所收获，下面我们一起来了解一下吧。
1.下载elasticsearch 6.4.1安装包
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 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“Centos7安装ElasticSearch实例分析”这篇文章的所有内容，感谢各位的阅读！相信大家阅读完这篇文章都有很大的收获，小编每天都会为大家更新不同的知识，如果还想学习更多的知识，请关注创新互联-成都网站建设公司行业资讯频道。
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