搜索引擎：
索引组件：获取数据-->建立文档-->文档分析-->文档索引（倒排索引）
搜索组件：用户搜索接口-->建立查询（将用户键入的信息转换为可处理的查询对象）-->搜索查询-->展现结果
索引组件：Lucene
搜索组件：Solr, ElasticSearch
注意：mysql数据库中的myisam引擎支持全文索引，但是格式比较复杂，不适于作为搜索
引擎的组件； Lucene Core： Apache LuceneTM is a high-performance, full-featured text search engine library written entirely in Java. It is a technology suitable for nearly any application that requires full-text search, especially cross-platform. Solr： SolrTM is a high performance search server built using Lucene Core, with XML/HTTP and JSON/Python/Ruby APIs, hit highlighting, faceted search, caching, replication, and a web admin interface. ElasticSearch： Elasticsearch is a distributed, RESTful search and analytics engine capable of solving a growing number of use cases. As the heart of the Elastic Stack, it centrally stores your data so you can discover the expected and uncover the unexpected.
Elastic Stack：
ElasticSearch
Logstash
Logstash is an open source, server-side data processing pipeline that ingests data from a multitude of sources simultaneously, transforms it, and then sends it to your favorite “stash.” (Ours is Elasticsearch, naturally.)
Beats：
Filebeat：Log Files
Metricbeat：Metrics
Packetbeat：Network Data
Winlogbeat：Windows Event Logs
Heartbeat：Uptime Monitoring
Kibana：
Kibana lets you visualize your Elasticsearch data and navigate the Elastic Stack, so you can do anything from learning why you're getting paged at 2:00 a.m. to understanding the impact rain might have on your quarterly numbers.
TF/IDF算法：
https://zh.wikipedia.org/wiki/Tf-idf
ES的核心组件：
物理组件：
集群：
状态：green, yellow, red
节点：
Shard：
Lucene的核心组件：
索引（index）：数据库(database)
类型（type）：表(table)
文档（Document）：行(row)
映射（Mapping）：
ElasticSearch 5的程序环境：
配置文件：
/etc/elasticsearch/elasticsearch.yml
/etc/elasticsearch/jvm.options
/etc/elasticsearch/log4j2.properties
Unit File：elasticsearch.service
程序文件：
/usr/share/elasticsearch/bin/elasticsearch
/usr/share/elasticsearch/bin/elasticsearch-keystore：
/usr/share/elasticsearch/bin/elasticsearch-plugin：管理插件程序
搜索服务：
9200/tcp
集群服务：
9300/tcp
els集群的工作逻辑：
多播、单播：9300/tcp
关键因素：clustername
所有节点选举一个主节点，负责管理整个集群的状态(green/yellow/red)，以及各shards的分布方式；
插件：
elk实现框图：
注意：elk是由elastic stack search、logstash和kibana组成的，如图中间颜色比较暗的是elastic stack search实现的部分，而下面的数据收集部分由logstash实现，最后kibana负责上方的图形搜 索界面接口；但是logstash数据收集器是由JRuby语言开发的，是用ruby语言先通过java解释器将 其翻译成java语言，之后进行编译执行，效率很低，故而出现了filebeat轻量级组件来代替它； logstash是通过在每个要采集的日志服务器植入agent组件，一旦日志有变化就将改变的数据拉取 到logstash服务器进行数据的文档化，之后将文档化的数据交给elastic stack search集群进行相 关处理。由于基于lucene的solr搜索引擎在后期没有支持大数据分布式的存储，被elk所取代； http://lucene.apache.org/ 将数据文档化之后数据形成索引的lucene网址 https://www.elastic.co/ elk访问地址，可以下载els镜像 https://db-engines.com/en/ 体现数据库地位的网址 elasticsearch集群： elasticsearch是由java开发的 准备工作：关闭防火墙、配置chrony时间同步、用本地文件进行dns解析 https://mirrors.cnnic.cn 清华大学的elastic stack search的镜像网站，下载速度快 yum install java-1.8.0-openjdk-devel -y rpm -ivh elasticsearch-5.6.8.rpm java编写的 scp elasticsearch-5.6.8.rpm server2:/root/ 复制过去后进行rpm安装 scp elasticsearch-5.6.8.rpm server3:/root/ cd /etc/elasticsearch/ vim elasticsearch.yml cluster.name: myels node.name: server1 path.data: /els/data path.logs: /els/logs 需要在外面创建目录，设置属组和属主为elasticsearch用户 network.host: 192.168.43.60 discovery.zen.ping.unicast.hosts: ["server1","server2","server3"] discovery.zen.minimum_master_nodes: 1 2个节点正常就可以正常使用
vim jvm.options -Xms1g 注意初始化值和最大值要相同 -Xmx1g mkdir /els/{data,logs} -pv && chown -R elasticsearch.elasticsearch /els/* scp elasticsearch.yml jvm.options server2:/etc/elasticsearch/ vim elasticsearch.yml network.host: 192.168.43.63 node.name: server2 scp elasticsearch.yml jvm.options server3:/etc/elasticsearch/ vim elasticsearch.yml network.host: 192.168.43.62 node.name: server3 java -version systemctl daemon-reload && systemctl start elasticsearch ss -ntl curl http://server1:9200/ 看测试是否成功 tail /els/logs/myels.log 可以查看日志找错误 free -m 查看内存的大小，以便定虚拟机的初始化值 curl -XGET 'http://server1:9200/_cluster/health?pretty=true' 发起查询请求
集群配置： elasticsearch.yml配置文件： cluster.name: myels node.name: node1 path.data: /data/els/data path.logs: /data/els/logs network.host: 0.0.0.0 http.port: 9200 9200端口是客户端用的，9300是集群内部进行通信的 discovery.zen.ping.unicast.hosts: ["node1", "node2", "node3"] discovery.zen.minimum_master_nodes: 2 · node.attr.rack: r1 表示可以集群分片到不同的机架，以防止机架中交换机断网
RESTful API: crud操作（create、read、update、delete）
curl -X<VERB> '<PROTOCOL>://<HOST>:<PORT>/<PATH>?<QUERY_STRING>' -d '<BODY>'
<BODY>：json格式的请求主体；
<VERB> 请求方法
GET，POST，PUT，DELETE；GET 为默认的方法
特殊PATH：/_cat, /_search, /_cluster
<PATH>
/index_name/type/Document_ID/
curl -XGET 'http://10.1.0.67:9200/_cluster/health?pretty=true'
curl -XGET 'http://10.1.0.67:9200/_cluster/stats?pretty=true'
curl -XGET 'http://10.1.0.67:9200/_cat/nodes?pretty'
curl -XGET 'http://10.1.0.67:9200/_cat/health?pretty'
curl http://server1:9200/_cat/indices 查看索引信息
创建文档：
curl -XPUT
特殊PATH：/_cat, /_search, /_cluster
文档：
{"key1": "value1", "key2": value, ...}
ELS：分布式、开源、RESTful、近乎实时
集群：一个或多个节点的集合；
节点：运行的单个els实例；
索引：切成多个独立的shard；（以Lucene的视角，每个shard即为一个独立而完整的索引）
primary shard：r/w
replica shard: r
查询：
ELS：很多API
_cluster, _cat, _search
curl -X GET '<SCHEME://<HOST>:<PORT>/[INDEX/TYPE/]_search?q=KEYWORD&sort=DOMAIN:[asc|desc]&from=#&size=#&_source=DOMAIN_LIST'
/_search：搜索所有的索引和类型；
/INDEX_NAME/_search：搜索指定的单个索引；
/INDEX1,INDEX2/_search：搜索指定的多个索引；
/s*/_search：搜索所有以s开头的索引；
/INDEX_NAME/TYPE_NAME/_search：搜索指定的单个索引的指定类型；
简单字符串的语法格式
http://lucene.apache.org/core/6_6_0/queryparser/org/apache/lucene/queryparser/classic/package-summary.html#package.description
查询类型：Query DSL，简单字符串；
文本匹配的查询条件：
(1) q=KEYWORD, 相当于q=_all:KEYWORD
(2) q=DOMAIN:KEYWORD
{
"name" : "Docker in Action",
"publisher" : "wrox",
"datatime" : "2015-12-01",
"author" : "Blair"
}
_all: "Docker in Action Wrox 2015-12-01 Blair"
修改默认查询域：df属性
查询修饰符：
https://www.elastic.co/guide/en/elasticsearch/reference/current/search-uri-request.html
自定义分析器：
analyzer=
默认操作符：OR/AND
default_operator, 默认值为OR
返回字段：
fields=
注：5.X不支持；
结果排序：
sort=DOMAIN:[asc|desc]
搜索超时：
timeout=
查询结果窗口：
from=，默认为0；
size=, 默认为10；
Lucene的查询语法：
q=
KEYWORD
DOMAIN:KEYWORD
+DOMAIN:KEYWORD -DOMAIN:KEYWORD
els支持从多类型的查询：
Full text queries
安装elasticsearch-head插件： 可以极大的简化命令行查询的复杂度
5.X：
(1) 设置elasticsearch.yml配置文件：
http.cors.enabled: true
http.cors.allow-origin: "*"
(2) 安装head：
$ git clone https://github.com/mobz/elasticsearch-head.git
$ cd elasticsearch-head
$ npm install 出错了，待解决
$ npm run start
ELK：
E: elasticsearch
L: logstash，日志收集工具；
ELK Beats Platform：
PacketBeat：网络报文分析工具，统计收集报文信息；
Filebeat：是logstash forwarder的替换者，因此是一个日志收集工具；
Topbeat：用来收集系统基础数据，如cpu、内存、io等相关的统计信息；
Winlogbeat
Metricbeat
用户自定义beat：
logstash的安装：
wget https://mirrors.tuna.tsinghua.edu.cn/elasticstack/yum/elastic-5.x/5.6.8/logstash- 5.6.8.rpm yum install java-1.8.0-openjdk-devel rpm -ivh logstash-5.6.8.rpm rpm -ql logstash|grep logstash$ vim /etc/logstash/conf.d/example1.conf input{ stdin{} }
output{ stdout{ codec => rubydebug } } /usr/share/logstash/bin/logstash -f ./example1.conf -t 测试是否有语法错误 /usr/share/logstash/bin/logstash -f ./example1.conf 进行运行程序
input {
...
}
filter{
...
}
output {
...
}
简单示例配置：
input {
stdin {}
}
output {
stdout {
codec => rubydebug
}
}
示例2：从文件输入数据，经grok过滤器插件过滤之后输出至标准输出：
input {
file {
path => ["/var/log/httpd/access_log"]
start_position => "beginning"
}
}
filter {
grok {
match => {
"message" => "%{COMBINEDAPACHELOG}"
}
remove_field: "message"
}
}
output {
stdout {
codec => rubydebug
}
}
示例3：date filter插件示例：
filter {
grok {
match => {
"message" => "%{HTTPD_COMBINEDLOG}"
}
remove_field => "message"
}
date {
match => ["timestamp","dd/MMM/YYYY:H:m:s Z"]
remove_field => "timestamp"
}
}
插件：mutate（改变内容）
The mutate filter allows you to perform general mutations on fields. You can rename, remove, replace, and modify fields in your events.
示例4：mutate filter插件
filter {
grok {
match => {
"message" => "%{HTTPD_COMBINEDLOG}"
}
}
date {
match => ["timestamp","dd/MMM/YYYY:H:m:s Z"]
}
mutate {
rename => {
"agent" => "user_agent"
}
}
}
示例5：geoip插件
filter {
grok {
match => {
"message" => "%{HTTPD_COMBINEDLOG}"
}
}
date {
match => ["timestamp","dd/MMM/YYYY:H:m:s Z"]
}
mutate {
rename => {
"agent" => "user_agent"
}
}
geoip {
source => "clientip"
target => "geoip"
database => "/etc/logstash/maxmind/GeoLite2-City.mmdb"
}
}
echo '47.98.120.224 - - [31/May/2018:16:22:58 +0800] "GET / HTTP/1.1" 200 21 "-"
"curl/7.29.0"' >> /var/log/httpd/access_log 追加httpd日志，看是否可以查询到ip地址信息
示例3：使用Redis
(1) 从redis加载数据
input {
redis {
batch_count => 1
data_type => "list"
key => "logstash-list"
host => "192.168.0.2"
port => 6379
threads => 5
}
}
(2) 将数据存入redis
output {
redis {
#data_type => "channel"
#key => "logstash-%{+yyyy.MM.dd}"
host => ["192.168.43.66"] port => 6379 db => 8 data_type => "list" key => "logstash-%{+YYYY.MMM.dd}" } } /usr/share/logstash/bin/logstash -f ./example6.conf 运行程序将数据输出到redis中 注意：要刷新数据，在redis中找数据 redis中： yum install redis vim /etc/redis.conf bind 0.0.0.0 systemctl restart redis help @list 查看list命令 lrange logstash-2018.May.31 0 10 查看一定范围的数据 keys * 查看有无数据 select 8 切换到8号数据库 示例4：将数据写入els cluster
output {
elasticsearch {
hosts => ["http://node1:9200/","http://node2:9200/","http://node3:9200/"]
user => "ec18487808b6908009d3"
password => "efcec6a1e0"
index => "logstash-%{+YYYY.MM.dd}"
document_type => "apache_logs"
}
}
示例5：综合示例，启用geoip
input {
beats {
port => 5044
}
}
filter {
grok {
match => {
"message" => "%{COMBINEDAPACHELOG}"
}
remove_field => "message"
}
geoip {
source => "clientip"
target => "geoip"
database => "/etc/logstash/GeoLite2-City.mmdb"
}
}
output {
elasticsearch {
hosts => ["http://172.16.0.67:9200","http://172.16.0.68:9200","http://172.16.0.69:9200"]
index => "logstash-%{+YYYY.MM.dd}"
action => "index"
document_type => "apache_logs"
}
}
grok：
%{SYNTAX:SEMANTIC}
SYNTAX：预定义的模式名称；
SEMANTIC：给模式匹配到的文本所定义的键名；
1.2.3.4 GET /logo.jpg 203 0.12
%{IP:clientip} %{WORD:method} %{URIPATHPARAM:request} %{NUMBER:bytes} %{NUMBER:duration}
{ clientip: 1.2.3.4, method: GET, request: /logo.jpg, bytes: 203, duration: 0.12}
%{IPORHOST:client_ip} %{USER:ident} %{USER:auth} \[%{HTTPDATE:timestamp}\] "(?:%{WORD:verb} %{NOTSPACE:request}(?: HTTP/%{NUMBER:http_version})?|-)" %{HOST:domain} %{NUMBER:response} (?:%{NUMBER:bytes}|-) %{QS:referrer} %{QS:agent} "(%{WORD:x_forword}|-)" (%{URIHOST:upstream_host}|-) %{NUMBER:upstream_response} (%{WORD:upstream_cache_status}|-) %{QS:upstream_content_type} (%{BASE16FLOAT:upstream_response_time}) > (%{BASE16FLOAT:request_time})
"message" => "%{IPORHOST:clientip} \[%{HTTPDATE:time}\] \"%{WORD:verb} %{URIPATHPARAM:request} HTTP/%{NUMBER:httpversion}\" %{NUMBER:http_status_code} %{NUMBER:bytes} \"(?<http_referer>\S+)\" \"(?<http_user_agent>\S+)\" \"(?<http_x_forwarded_for>\S+)\""
filter {
grok {
match => {
"message" => "%{IPORHOST:clientip} \[%{HTTPDATE:time}\] \"%{WORD:verb} %{URIPATHPARAM:request} HTTP/%{NUMBER:httpversion}\" %{NUMBER:http_status_code} %{NUMBER:bytes} \"(?<http_referer>\S+)\" \"(?<http_user_agent>\S+)\" \"(?<http_x_forwarded_for>\S+)\""
}
remote_field: message
}
}
nginx.remote.ip
[nginx][remote][ip]
filter {
grok {
match => { "message" => ["%{IPORHOST:[nginx][access][remote_ip]} - %{DATA:[nginx][access][user_name]} \[%{HTTPDATE:[nginx
][access][time]}\] \"%{WORD:[nginx][access][method]} %{DATA:[nginx][access][url]} HTTP/%{NUMBER:[nginx][access][http_version]}\
" %{NUMBER:[nginx][access][response_code]} %{NUMBER:[nginx][access][body_sent][bytes]} \"%{DATA:[nginx][access][referrer]}\" \"
%{DATA:[nginx][access][agent]}\""] }
remove_field => "message"
}
date {
match => [ "[nginx][access][time]", "dd/MMM/YYYY:H:m:s Z" ]
remove_field => "[nginx][access][time]"
}
useragent {
source => "[nginx][access][agent]"
target => "[nginx][access][user_agent]"
remove_field => "[nginx][access][agent]"
}
geoip {
source => "[nginx][access][remote_ip]"
target => "geoip"
database => "/etc/logstash/GeoLite2-City.mmdb"
}
}
output {
elasticsearch {
hosts => ["node1:9200","node2:9200","node3:9200"]
index => "logstash-ngxaccesslog-%{+YYYY.MM.dd}"
}
}
注意：
1、输出的日志文件名必须以“logstash-”开头，方可将geoip.location的type自动设定为"geo_point"；
2、target => "geoip"
除了使用grok filter plugin实现日志输出json化之外，还可以直接配置服务输出为json格式；
示例：使用grok结构化nginx访问日志
filter {
grok {
match => {
"message" => "%{HTTPD_COMBINEDLOG} \"%{DATA:realclient}\""
}
remove_field => "message"
}
date {
match => ["timestamp","dd/MMM/YYYY:H:m:s Z"]
remove_field => "timestamp"
}
}
示例：使用grok结构化tomcat访问日志
filter {
grok {
match => {
"message" => "%{HTTPD_COMMONLOG}"
}
remove_field => "message"
}
date {
match => ["timestamp","dd/MMM/YYYY:H:m:s Z"]
remove_field => "timestamp"
}
}
Nginx日志Json化:
log_format json '{"@timestamp":"$time_iso8601",'
'"@source":"$server_addr",'
'"@nginx_fields":{'
'"client":"$remote_addr",'
'"size":$body_bytes_sent,'
'"responsetime":"$request_time",'
'"upstreamtime":"$upstream_response_time",'
'"upstreamaddr":"$upstream_addr",'
'"request_method":"$request_method",'
'"domain":"$host",'
'"url":"$uri",'
'"http_user_agent":"$http_user_agent",'
'"status":$status,'
'"x_forwarded_for":"$http_x_forwarded_for"'
'}'
'}';
access_log logs/access.log json;
Conditionals Sometimes you only want to filter or output an event under certain conditions. For that, you can use a conditional.
Conditionals in Logstash look and act the same way they do in programming languages. Conditionals support if, else if and else statements and can be nested.
The conditional syntax is:
if EXPRESSION {
...
} else if EXPRESSION {
...
} else {
...
}
What’s an expression? Comparison tests, boolean logic, and so on!
You can use the following comparison operators:
equality: ==, !=, <, >, <=, >=
regexp: =~, !~ (checks a pattern on the right against a string value on the left) inclusion: in, not in
The supported boolean operators are:
and, or, nand, xor
The supported unary operators are:
!
Expressions can be long and complex. Expressions can contain other expressions, you can negate expressions with !, and you can group them with parentheses (...).
filter {
if [type] == 'tomcat-accesslog' {
grok {}
}
if [type] == 'httpd-accesslog' {
grok {}
}
}
1、lucene索引组件 lucene由3部分组成： index：对应于db数据，每个索引为一个db type：对应于table数据，如每个应用的日志都是不同的，放在不同的table中 document：对应于row数据，是键值对组的存放 mapping：映射，对每个字段key的数据类型进行规定 2、es组件
在存储索引时采用节点集群存储，索引进行分片处理，以增加冗余度；分片具有主副。在搜索时 通过总线调度到存储即可，不用在文件中写死；es组件集成了lucene，是中间的一部分实现
3、es搜索组件的集群状态
es的集群状态由3中颜色进行表示： green：所有shard主副片口可以正常使用 yellow：存在某个或某些分片缺少主或副 red：存在某个或某些分片同时缺少主和副 如果发生网络分区，两个节点之间不能进行通信了，这时候就会造成脑裂，故而需要进行quorum 投票选择哪个作为正常节点正常工作，哪个下线等待。所以此集群需要奇数个节点
4、logstash插件讲解 logstash插件通过输入插件从指定数据源获取数据，根据输出插件将处理过的数据输出到指定目 标，中间是进行数据过滤的插件，对数据进行文档化处理等操作； https://www.elastic.co/guide/en/logstash/5.6/index.html 参考文档 logstash使用架构： logstash服务器-->elasticsearch logstash服务器/filebeat服务器-->redis服务器-->logstash服务器-->elasticsearch
5、logstash架构的实现实验 logstash服务器/filebeat服务器-->redis服务器-->logstash服务器-->elasticsearch （1）filebeat的配置 主机ip 192.168.43.61 wget https://mirrors.tuna.tsinghua.edu.cn/elasticstack/yum/elastic-5.x/5.6.8/filebeat-5.6.8- x86_64.rpm 先到清华的镜像站点下载filebeat软件 rpm -ivh filebeat-5.6.8-x86_64.rpm 安装软件 cd /etc/filebeat/ vim filebeat.yml paths: - /var/log/httpd/access_log #------------------------------- Redis output ---------------------------------- output.redis: enabled: true hosts: ["192.168.43.66:6379"] db: 6 datatype: list key: filebeat systemclt start filebeat 以上过程实现了filebeat将收集到的数据存入redis yum install httpd 日志来源 echo ‘HelloWorld’ > /var/www/html/index.html systemctl start httpd echo '223.5.5.5 - - [1/June/2018:14:03:58 +0800] "GET / HTTP/1.1" 200 21 "-" "curl/7.29.0"'
/var/log/httpd/access_log 创造公网ip的日志以提供日志来源 curl http://172.18.62.61 访问页面创造日志 （2）redis的配置 主机ip 192.168.43.66 yum install redis vim /etc/redis.conf bind 0.0.0.0 systemctl start redis redis-cli 连接redis
（3）logstash服务器配置 主机ip 192.168.43.61 wget https://mirrors.tuna.tsinghua.edu.cn/elasticstack/yum/elastic-5.x/5.6.8/logstash- 5.6.8.rpm yum install java-1.8.0-openjdk-devel rpm -ivh logstash-5.6.8.rpm rpm -ql logstash|grep logstash$ vim /etc/logstash/conf.d/example8.conf input{ redis { host => "192.168.43.66" port => 6379 db => 6 key => "filebeat" data_type => "list" threads => 6 } }
filter { grok { match => { "message" => "%{COMBINEDAPACHELOG}"} remove_field => "message" } date { match => [ "timestamp", "dd/MMM/YYYY:H:m:s Z" ] remove_field => "timestamp" } geoip { source => "clientip" target => "geoip" database => "/etc/logstash/maxmind/GeoLite2-City.mmdb" } }
output{ elasticsearch { hosts => ["http://server1:9200","http://server2:9200","http://server3:9200"] index => "logstash-%{+YYYY.MM.dd}" document_type => "apache_logs" } }
/usr/share/logstash/bin/logstash -f ./example1.conf -t 测试是否有语法错误 systemctl start logstash （4）elasticsearch的配置 主机ip 192.168.43.60/62/63 准备工作：关闭防火墙、配置chrony时间同步、用本地文件进行dns解析 https://mirrors.cnnic.cn 清华大学的elastic stack search的镜像网站，下载速度快 yum install java-1.8.0-openjdk-devel -y rpm -ivh elasticsearch-5.6.8.rpm java编写的 scp elasticsearch-5.6.8.rpm server2:/root/ 复制过去后进行rpm安装 scp elasticsearch-5.6.8.rpm server3:/root/ cd /etc/elasticsearch/ vim elasticsearch.yml cluster.name: myels node.name: server1 path.data: /els/data path.logs: /els/logs 需要在外面创建目录，设置属组和属主为elasticsearch用户 network.host: 192.168.43.60 discovery.zen.ping.unicast.hosts: ["server1","server2","server3"] discovery.zen.minimum_master_nodes: 1 2个节点正常就可以正常使用
vim jvm.options -Xms1g 注意初始化值和最大值要相同 -Xmx1g mkdir /els/{data,logs} -pv && chown -R elasticsearch.elasticsearch /els/* scp elasticsearch.yml jvm.options server2:/etc/elasticsearch/ vim elasticsearch.yml network.host: 192.168.43.63 node.name: server2 scp elasticsearch.yml jvm.options server3:/etc/elasticsearch/ vim elasticsearch.yml network.host: 192.168.43.62 node.name: server3 java -version systemctl daemon-reload && systemctl start elasticsearch ss -ntl curl http://server1:9200/ 看测试是否成功 tail /els/logs/myels.log 可以查看日志找错误 free -m 查看内存的大小，以便定虚拟机的初始化值 curl -XGET 'http://server1:9200/_cluster/health?pretty=true' 发起查询请求 （5）kibana的配置 wget https://mirrors.tuna.tsinghua.edu.cn/elasticstack/yum/elastic-5.x/5.6.8/kibana-5.6.8- x86_64.rpm 从清华镜像网站下载kibana图形界面工具 rpm -ivh kibana-5.6.8-x86_64.rpm 安装软件 cd /etc/kibana/ vim kibana.yml server.port: 5601 监听端口 server.host: "0.0.0.0" 允许任意主机访问 server.name: "redis" 主机名 elasticsearch.url: "http://server1:9200" 所连接的数据接口 elasticsearch.preserveHost: true kibana.index: ".kibana" 一些所以文件的放处 systemclt restart kibana http://172.18.62.66:5601 在浏览器页面访问
