目录: https://github.com/dolyw/ProjectStudy/tree/master/Elasticsearch
DockerStudy
- dolyw：https://note.dolyw.com/docker/03-Elasticsearch.html
项目地址
- Github：https://github.com/dolyw/ProjectStudy/tree/master/Elasticsearch/02-SpringBoot-ES-Docker
- Gitee(码云)：https://gitee.com/dolyw/ProjectStudy/tree/master/Elasticsearch/02-SpringBoot-ES-Docker
Docker下Elasticsearch的使用
当然首先启动Docker，可以去Docker Hub: https://hub.docker.com/_/elasticsearch?tab=tags查询下Tag版本
可以先docker search elasticsearch查询一下，看下连接有没有问题
然后直接使用命令docker pull elasticsearch下载最新版本，可以加上冒号版本号下载对应版本docker pull elasticsearch:7.3.0，这里我们使用7.3.0版本
PS C:\> docker search elasticsearch
NAME DESCRIPTION STARS OFFICIAL AUTOMATED
elasticsearch Elasticsearch is a powerful open source sear… 3801 [OK]
nshou/elasticsearch-kibana Elasticsearch-7.1.1 Kibana-7.1.1 104 [OK]
itzg/elasticsearch Provides an easily configurable Elasticsearc… 67 [OK]
mobz/elasticsearch-head elasticsearch-head front-end and standalone … 47
elastichq/elasticsearch-hq Official Docker image for ElasticHQ: Elastic… 36 [OK]
...
PS C:\> docker pull elasticsearch:7.3.0
7.3.0: Pulling from library/elasticsearch
8ba884070f61: Pull complete
854c9f9b1064: Pull complete
44d43a907bb5: Pull complete
9311c5f24d75: Pull complete
91363c70bdb7: Pull complete
38b4cb8c47ad: Pull complete
c22bd5067efd: Pull complete
Digest: sha256:ba2ef018238cc05e9e44d72228002b4fabe202801951caaa265ce080deb97133
Status: Downloaded newer image for elasticsearch:7.3.0
docker.io/library/elasticsearch:7.3.0
PS C:\>
这样就下载完成了，先使用命令docker images查询Elasticsearch镜像ID
PS C:\> docker images
REPOSITORY TAG IMAGE ID CREATED SIZE
tomcat 8.5.43-jdk8-adoptopenjdk-openj9 689bdcef64fe 22 hours ago 339MB
elasticsearch 7.3.0 bdaab402b220 3 weeks ago 806MB
在启动前我们先在自己主机建立ES的配置文件elasticsearch.yml和一个data空目录
# 设置支持Elasticsearch-Head
http.cors.enabled: true
http.cors.allow-origin: "*"
# 设置集群Master配置信息
cluster.name: myEsCluster
# 节点的名字，一般为Master或者Slave
node.name: master
# 节点是否为Master，设置为true的话，说明此节点为Master节点
node.master: true
# 设置网络，如果是本机的话就是127.0.0.1，其他服务器配置对应的IP地址即可(0.0.0.0支持外网访问)
network.host: 0.0.0.0
# 设置对外服务的Http端口，默认为 9200，可以修改默认设置
http.port: 9200
# 设置节点间交互的TCP端口，默认是9300
transport.tcp.port: 9300
# 手动指定可以成为Master的所有节点的Name或者IP，这些配置将会在第一次选举中进行计算
cluster.initial_master_nodes: ["master"]
然后直接用下面命令启动Elasticsearch容器，加-d就表示后台运行，配置文件和空目录如下对应起来
docker run -e ES_JAVA_OPTS="-Xms256m -Xmx256m" -v D:/tools/docker/elasticsearch/elasticsearch.yml:/usr/share/elasticsearch/config/elasticsearch.yml -v D:/tools/docker/elasticsearch/data:/usr/share/elasticsearch/data --name es -p 9200:9200 -p 9300:9300 bdaab402b220
注：设置-e ES_JAVA_OPTS="-Xms256m -Xmx256m"是因为/etc/elasticsearch/jvm.options默认JVM最大最小内存是2G，读者启动容器后 可用docker stats命令查看
可能会需要确认是否共享磁盘，都确认就可以，等一会启动成功浏览器查看: http://127.0.0.1:9200，返回下面的字符，代表启动成功
{
"name" : "master",
"cluster_name" : "myEsCluster",
"cluster_uuid" : "5SZ13bMbSKOx1Nd5iIyruA",
"version" : {
"number" : "7.3.0",
"build_flavor" : "default",
"build_type" : "docker",
"build_hash" : "de777fa",
"build_date" : "2019-07-24T18:30:11.767338Z",
"build_snapshot" : false,
"lucene_version" : "8.1.0",
"minimum_wire_compatibility_version" : "6.8.0",
"minimum_index_compatibility_version" : "6.0.0-beta1"
},
"tagline" : "You Know, for Search"
}
Docker下Elasticsearch的集群
建立三个配置文件elasticsearch1.yml，elasticsearch2.yml，elasticsearch3.yml和三个data文件夹，data1，data2，data3
- elasticsearch1.yml
# 设置支持Elasticsearch-Head
http.cors.enabled: true
http.cors.allow-origin: "*"
# 设置集群Master配置信息
cluster.name: myEsCluster
# 节点的名字，一般为Master或者Slave
node.name: master
# 节点是否为Master，设置为true的话，说明此节点为Master节点
node.master: true
# 设置网络，如果是本机的话就是127.0.0.1，其他服务器配置对应的IP地址即可(0.0.0.0支持外网访问)
network.host: 0.0.0.0
# 设置对外服务的Http端口，默认为 9200，可以修改默认设置
http.port: 9500
# 设置节点间交互的TCP端口，默认是9300
transport.tcp.port: 9300
# 手动指定可以成为Master的所有节点的Name或者IP，这些配置将会在第一次选举中进行计算
cluster.initial_master_nodes: ["master"]
# 集群发现节点信息，一般为其他节点IP加交互端口，这里一般填主机IP
discovery.seed_hosts: ["192.168.2.58:9301", "192.168.2.58:9302"]
- elasticsearch2.yml
# 设置集群Slave配置信息
cluster.name: myEsCluster
# 节点的名字，一般为Master或者Slave
node.name: slave1
# 节点是否为Master，设置为true的话，说明此节点为master节点
node.master: false
# 设置对外服务的Http端口，默认为 9200，可以修改默认设置
http.port: 9600
# 设置节点间交互的TCP端口，默认是9300
transport.tcp.port: 9301
# 设置网络，如果是本机的话就是127.0.0.1，其他服务器配置对应的IP地址即可(0.0.0.0支持外网访问)
network.host: 0.0.0.0
# 集群发现节点信息，一般为其他节点IP加交互端口，这里一般填主机IP
discovery.seed_hosts: ["192.168.2.58:9300", "192.168.2.58:9302"]
- elasticsearch3.yml
# 设置集群Slave配置信息
cluster.name: myEsCluster
# 节点的名字，一般为Master或者Slave
node.name: slave2
# 节点是否为Master，设置为true的话，说明此节点为master节点
node.master: false
# 设置对外服务的Http端口，默认为 9200，可以修改默认设置
http.port: 9700
# 设置节点间交互的TCP端口，默认是9300
transport.tcp.port: 9302
# 设置网络，如果是本机的话就是127.0.0.1，其他服务器配置对应的IP地址即可(0.0.0.0支持外网访问)
network.host: 0.0.0.0
# 集群发现节点信息，一般为其他节点IP加交互端口，这里一般填主机IP
discovery.seed_hosts: ["192.168.2.58:9300", "192.168.2.58:9301"]
然后启动三个，一个Master，两个Slave
docker run -e ES_JAVA_OPTS="-Xms256m -Xmx256m" -d -v D:/tools/docker/elasticsearch/elasticsearch1.yml:/usr/share/elasticsearch/config/elasticsearch.yml -v D:/tools/docker/elasticsearch/data1:/usr/share/elasticsearch/data --name es1 -p 9500:9500 -p 9300:9300 bdaab402b220
docker run -e ES_JAVA_OPTS="-Xms256m -Xmx256m" -d -v D:/tools/docker/elasticsearch/elasticsearch2.yml:/usr/share/elasticsearch/config/elasticsearch.yml -v D:/tools/docker/elasticsearch/data2:/usr/share/elasticsearch/data --name es2 -p 9600:9600 -p 9301:9301 bdaab402b220
docker run -e ES_JAVA_OPTS="-Xms256m -Xmx256m" -d -v D:/tools/docker/elasticsearch/elasticsearch3.yml:/usr/share/elasticsearch/config/elasticsearch.yml -v D:/tools/docker/elasticsearch/data3:/usr/share/elasticsearch/data --name es3 -p 9700:9700 -p 9302:9302 bdaab402b220
等一会启动成功浏览器查看集群节点信息: http://127.0.0.1:9500/_cat/nodes?v
ip heap.percent ram.percent cpu load_1m load_5m load_15m node.role master name
172.17.0.2 34 95 3 0.69 0.50 0.20 dim * master
172.17.0.4 49 95 3 0.69 0.50 0.20 di - slave1
172.17.0.3 48 95 3 0.69 0.50 0.20 di - slave1
- Docker集群就OK了
Docker下Elasticsearch-Head的安装
可以先docker search elasticsearch查询一下，看下有没有问题，然后直接使用命令docker pull mobz/elasticsearch-head:5下载，加上冒号版本号下载对应版本，这里我们使用5版本
下载完成了，直接启动
docker run -d --name es-head -p 9100:9100 mobz/elasticsearch-head:5
等一会启动成功浏览器查看: http://127.0.0.1:9100，把连接地址改成http://localhost:9500，点击连接即可，连接成功，可以看到三个节点的集群信息，就这样Elasticsearch-Head就安装成功了
Docker下Elasticsearch的IK分词插件的安装
直接去Github的Releases下载自己ES对应的版本: https://github.com/medcl/elasticsearch-analysis-ik/releases
这里我们下载7.3: https://github.com/medcl/elasticsearch-analysis-ik/releases/download/v7.3.0/elasticsearch-analysis-ik-7.3.0.zip
下载下来放到我们对应的每个ES映射目录data下，解压为一个文件夹，启动Docker，启动ES容器，进去ES容器
docker exec -it es bash
进去data目录，查看文件，可以看到是和我们主机对应的目录，然后我们把解压的这个elasticsearch-analysis-ik-7.3.0文件夹移动到上一层的plugins目录下即可
PS C:\WINDOWS\system32> docker exec -it es bash
[root@a563ff91196d elasticsearch]# ls
LICENSE.txt NOTICE.txt README.textile bin config data jdk lib logs modules plugins
[root@a563ff91196d elasticsearch]# cd data
[root@a563ff91196d data]# ls
elasticsearch-analysis-ik-7.3.0 elasticsearch-analysis-ik-7.3.0.zip nodes
[root@a563ff91196d data]#
[root@a563ff91196d data]# ls
elasticsearch-analysis-ik-7.3.0 elasticsearch-analysis-ik-7.3.0.zip nodes
[root@a563ff91196d data]# mv elasticsearch-analysis-ik-7.3.0 ../plugins
[root@a563ff91196d data]# ls
elasticsearch-analysis-ik-7.3.0.zip nodes
[root@a563ff91196d data]# cd ..
[root@a563ff91196d elasticsearch]# ls
LICENSE.txt NOTICE.txt README.textile bin config data jdk lib logs modules plugins
[root@a563ff91196d elasticsearch]# cd plugins/
[root@a563ff91196d plugins]# ls
elasticsearch-analysis-ik-7.3.0
这样就OK了，我们再使用命令exit退出，再docker restart es重启容器
[root@a563ff91196d plugins]# ls
elasticsearch-analysis-ik-7.3.0
[root@a563ff91196d plugins]# exit
exit
PS C:\WINDOWS\system32> docker restart es
es
PS C:\WINDOWS\system32>
也可以docker logs -f es查看下启动日志
测试下IK分词插件OK了没
POST /_analyze
{
"text":"中华人民共和国国徽",
"analyzer":"ik_smart"
}
返回
{
"tokens": [
{
"token": "中华人民共和国",
"start_offset": 0,
"end_offset": 7,
"type": "CN_WORD",
"position": 0
},
{
"token": "国徽",
"start_offset": 7,
"end_offset": 9,
"type": "CN_WORD",
"position": 1
}
]
}
Docker下Elasticsearch的拼音分词插件的安装
和IK安装类似，直接去Github的Releases下载自己ES对应的版本: https://github.com/medcl/elasticsearch-analysis-pinyin/releases
这里我们下载7.3: https://github.com/medcl/elasticsearch-analysis-pinyin/releases/download/v7.3.0/elasticsearch-analysis-pinyin-7.3.0.zip
操作类似，下载下来放到我们对应的每个ES映射目录data下，解压为一个文件夹，启动Docker，启动ES容器，进去ES容器，然后我们把解压的这个elasticsearch-analysis-pinyin-7.3.0文件夹移动到上一层的plugins目录下即可，这样就OK了，我们再使用命令exit退出，再docker restart es重启容器
然后测试一下
POST /_analyze
{
"text":"中华人民共和国国徽",
"analyzer":"pinyin"
}
返回
{
"tokens": [
{
"token": "zhong",
"start_offset": 0,
"end_offset": 0,
"type": "word",
"position": 0
},
{
"token": "zhrmghggh",
"start_offset": 0,
"end_offset": 0,
"type": "word",
"position": 0
},
{
"token": "hua",
"start_offset": 0,
"end_offset": 0,
"type": "word",
"position": 1
},
{
"token": "ren",
"start_offset": 0,
"end_offset": 0,
"type": "word",
"position": 2
},
{
"token": "min",
"start_offset": 0,
"end_offset": 0,
"type": "word",
"position": 3
},
{
"token": "gong",
"start_offset": 0,
"end_offset": 0,
"type": "word",
"position": 4
},
{
"token": "he",
"start_offset": 0,
"end_offset": 0,
"type": "word",
"position": 5
},
{
"token": "guo",
"start_offset": 0,
"end_offset": 0,
"type": "word",
"position": 6
},
{
"token": "guo",
"start_offset": 0,
"end_offset": 0,
"type": "word",
"position": 7
},
{
"token": "hui",
"start_offset": 0,
"end_offset": 0,
"type": "word",
"position": 8
}
]
}
使用IK和拼音分词插件(详细使用可以查看Github的文档)
- 创建Index，拼音分词过滤
PUT /book
{
"settings": {
"analysis": {
"analyzer": {
"pinyin_analyzer": {
"tokenizer": "my_pinyin"
}
},
"tokenizer": {
"my_pinyin": {
"type": "pinyin",
"keep_separate_first_letter": false,
"keep_full_pinyin": true,
"keep_original": true,
"limit_first_letter_length": 16,
"lowercase": true,
"remove_duplicated_term": true
}
}
}
}
}
返回
{
"acknowledged": true,
"shards_acknowledged": true,
"index": "book"
}
- 创建Mapping，属性使用过滤，name开启拼音分词，content开启IK分词，describe开启拼音加IK分词
POST /book/_mapping
{
"properties": {
"name": {
"type": "keyword",
"fields": {
"pinyin": {
"type": "text",
"store": false,
"term_vector": "with_offsets",
"analyzer": "pinyin_analyzer",
"boost": 10
}
}
},
"content": {
"type": "text",
"analyzer": "ik_max_word",
"search_analyzer": "ik_smart"
},
"describe": {
"type": "text",
"analyzer": "ik_max_word",
"search_analyzer": "ik_smart",
"fields": {
"pinyin": {
"type": "text",
"store": false,
"term_vector": "with_offsets",
"analyzer": "pinyin_analyzer",
"boost": 10
}
}
},
"id": {
"type": "long"
}
}
}
返回
{
"acknowledged": true
}
这样Index以及属性分词就开启了