Centos7安装ElasticSearch 6.4.1入门教程详解
人气:01.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 <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 } ] } } }
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