Hello everyone, I am Su Lin. Today I will install es and kibana.
Deploy single point es
Create a network
Because we also need to deploy the kibana container, we need to interconnect the es and kibana containers. Here first create a network:
docker network create es-net
Mirror
We can use the docker command to pull or long-distance transfer, and we can choose one.
Pull the image
Official website: Docker Hub Container Image Library | App Containerization
Order :
docker pull elasticsearch:7.12.1
Load image
Here we use the image of elasticsearch version 7.12.1
A mirrored tar package is provided:
Link: https://pan.baidu.com/s/1gly5GdwN0ccs1nKWodPcZg
Extraction code: 3goa
Everyone uploads it to the virtual machine and then runs the command to load it:
# Import data docker load -i es.tar
Run
docker command, deploy single point es:
docker run -d --name es -e "ES_JAVA_OPTS=-Xms512m -Xmx512m" -e "discovery.type=single-node" -v es-data:/usr/share/elasticsearch/data -v es- plugins:/usr/share/elasticsearch/plugins --privileged --network es-net -p 9200:9200 -p 9300:9300 elasticsearch:7.12.1
Command explanation:
- -e “cluster.name=es-docker-cluster”: Set the cluster name
- -e “http.host=0.0.0.0”: The listening address can be accessed from the external network
- -e “ES_JAVA_OPTS=-Xms512m -Xmx512m”: memory size
- -e “discovery.type=single-node”: non-cluster mode
- -v es-data:/usr/share/elasticsearch/data: Mount the logical volume and bind the es data directory
- -v es-logs:/usr/share/elasticsearch/logs: Mount the logical volume and bind the es log directory
- -v es-plugins:/usr/share/elasticsearch/plugins: Mount the logical volume and bind the es plug-in directory
- –privileged: Grant logical volume access
- –network es-net: Join a network named es-net -p 9200:9200: Port mapping configuration
Started successfully
Enter: http://ip:9200 in the browser to see the response results of elasticsearch:
Deploy kibana
Kibana can provide us with a visual interface of elasticsearch to facilitate our learning.
Mirror
download :
docker pull kibana:7.12.1
Upload:
Deployment
Run the docker command to deploy kibana
docker run -d --name kibana -e ELASTICSEARCH_HOSTS=http://es:9200 --network=es-net -p 5601:5601 kibana:7.12.1
The versions of Kibana and elasticsearch must be the same.
Visit:
http://ip address:5601
Click Explore on my own
Click Dev Tools
Install IK word segmenter
Install ik plug-in online
# Enter inside the container docker exec -it elasticsearch /bin/bash # Download and install online ./bin/elasticsearch-plugin install https://github.com/medcl/elasticsearchanalysis-ik/releases/download/v7.12.1/elasticsearch-analysis-ik-7.12.1.zip #quit exit #Restart container docker restart elasticsearch
Install ik plug-in offline
View data volume directory
To install the plug-in, you need to know the location of the plugins directory of elasticsearch. We used data volume mounting, so we need to check the data volume directory of elasticsearch. Check it with the following command:
docker volume inspect es-plugins
It means that the plugins directory is mounted to: /var/lib/docker/volumes/es-plugins/_data.
Upload to the plug-in data volume of the es container
Restart container
# 4. Restart the container docker restartes
# View es log docker logs -f es
Test:
The IK word segmenter contains two modes:
- ik_smart: least segmentation
- ik_max_word: the finest segmentation
ik_max_word
{
"tokens" : [
{
"token" : "programmer",
"start_offset" : 0,
"end_offset" : 3,
"type" : "CN_WORD",
"position" : 0
},
{
"token" : "program",
"start_offset" : 0,
"end_offset" : 2,
"type" : "CN_WORD",
"position" : 1
},
{
"token" : "member",
"start_offset" : 2,
"end_offset" : 3,
"type" : "CN_CHAR",
"position" : 2
},
{
"token" : "su",
"start_offset" : 3,
"end_offset" : 4,
"type" : "CN_CHAR",
"position" : 3
},
{
"token" : "麟",
"start_offset" : 4,
"end_offset" : 5,
"type" : "CN_CHAR",
"position" : 4
},
{
"token" : "learning",
"start_offset" : 5,
"end_offset" : 7,
"type" : "CN_WORD",
"position": 5
},
{
"token" : "java",
"start_offset" : 7,
"end_offset" : 11,
"type" : "ENGLISH",
"position" : 6
},
{
"token" : "very simple",
"start_offset" : 11,
"end_offset" : 14,
"type" : "CN_WORD",
"position" : 7
},
{
"token" : "simple",
"start_offset" : 12,
"end_offset" : 14,
"type" : "CN_WORD",
"position" : 8
}
]
}
ik_smart
{
"tokens" : [
{
"token" : "programmer",
"start_offset" : 0,
"end_offset" : 3,
"type" : "CN_WORD",
"position" : 0
},
{
"token" : "su",
"start_offset" : 3,
"end_offset" : 4,
"type" : "CN_CHAR",
"position" : 1
},
{
"token" : "麟",
"start_offset" : 4,
"end_offset" : 5,
"type" : "CN_CHAR",
"position" : 2
},
{
"token" : "learning",
"start_offset" : 5,
"end_offset" : 7,
"type" : "CN_WORD",
"position" : 3
},
{
"token" : "java",
"start_offset" : 7,
"end_offset" : 11,
"type" : "ENGLISH",
"position" : 4
},
{
"token" : "very simple",
"start_offset" : 11,
"end_offset" : 14,
"type" : "CN_WORD",
"position": 5
}
]
}
Expanded word dictionary
With the development of the Internet, “word-making movements” have become more and more frequent. Many new words appeared that did not exist in the original vocabulary list. For example: “Aoligei” etc.
Therefore, our vocabulary also needs to be constantly updated. The IK word segmenter provides the function of expanding vocabulary.
1) Open the IK word segmenter config directory:
2) Add the following to the IKAnalyzer.cfg.xml configuration file:
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE properties SYSTEM "http://java.sun.com/dtd/properties.dtd">
<properties>
<comment>IK Analyzer extended configuration</comment>
<!--Users can configure their own extended dictionary here *** Add extended dictionary -->
<entry key="ext_dict">ext.dic</entry>
</properties>
3) Create a new ext.dic. You can copy a configuration file in the config directory to modify it.
olige big fool Yang Ke
4) Restart elasticsearch
docker restart es
# View log docker logs -f elasticsearch
5) Test effect:
{
"tokens" : [
{
"token" : "programmer",
"start_offset" : 0,
"end_offset" : 3,
"type" : "CN_WORD",
"position" : 0
},
{
"token" : "su",
"start_offset" : 3,
"end_offset" : 4,
"type" : "CN_CHAR",
"position" : 1
},
{
"token" : "麟",
"start_offset" : 4,
"end_offset" : 5,
"type" : "CN_CHAR",
"position" : 2
},
{
"token" : "big",
"start_offset" : 5,
"end_offset" : 6,
"type" : "CN_CHAR",
"position" : 3
},
{
"token" : "儿",
"start_offset" : 6,
"end_offset" : 7,
"type" : "CN_CHAR",
"position" : 4
},
{
"token" : "Yang Ke",
"start_offset" : 7,
"end_offset" : 9,
"type" : "CN_WORD",
"position": 5
},
{
"token" : "love to eat",
"start_offset" : 9,
"end_offset" : 11,
"type" : "CN_WORD",
"position" : 6
},
{
"token" : "oligi",
"start_offset" : 11,
"end_offset" : 14,
"type" : "CN_WORD",
"position" : 7
}
]
}
Stop word dictionary
In Internet projects, the transmission speed between networks is very fast, so many languages are not allowed to be transmitted on the Internet, such as: sensitive words about religion, politics, etc., then we should also ignore the current vocabulary when searching.
The IK word segmenter also provides a powerful stop word function, allowing us to directly ignore the contents of the current stop word list during indexing.
1) Add the contents of the IKAnalyzer.cfg.xml configuration file:
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE properties SYSTEM "http://java.sun.com/dtd/properties.dtd">
<properties>
<comment>IK Analyzer extended configuration</comment>
<!--Users can configure their own extended dictionary here-->
<entry key="ext_dict">ext.dic</entry>
<!--Users can configure their own extended stop word dictionary here *** Add stop word dictionary-->
<entry key="ext_stopwords">stopword.dic</entry>
</properties>
3) Add stop words in stopword.dic
My son
4) Restart elasticsearch
# Restart service docker restart elasticsearch docker restart kibana # View log docker logs -f elasticsearch
5) Test effect:
I won’t demonstrate it here, you can try it yourself.
That’s it for this issue, see you in the next one!