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Documentation Index

Fetch the complete documentation index at: https://docs.xpertai.cn/llms.txt

Use this file to discover all available pages before exploring further.

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Background

This article will briefly describe how to quickly build a complete Xpert analytics platform cluster using the docker run or docker-compose up command.

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Applicable Scenarios

It is recommended to use the Xpert analytics platform Docker containers to simplify the deployment process in SIT or DEV environments.

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Software Environment

SoftwareVersion
Docker20.0 or above
docker-compose2.10 or above

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Standard Deployment

Please download the installation script provided by us 
git clone https://github.com/meta-d/installer.git
Then, in the docker directory, first configure the environment variable file .env 
cd installer/docker
cp env.tmpl .env
  • Set the value of INSTALLATION_MODE to standalone
  • Other password fields can be reset
Finally, execute the following command to start the service: 
docker-compose -f docker-compose.yml up -d
Once the service is started, visit http://localhost/ to see the login page of the Xpert analytics platform.
DomainWhen deploying on a server, please modify the API_BASE_URL environment variable in the .env file from localhost to the domain name or IP address of the server. For example:http://localhost:3000 → http://my-domain.com:3000

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Detailed Configuration

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Docker Compose Network Mode Explanation

There are two network modes suitable for deploying Xpert analytics platform Docker:
  1. HOST mode suitable for deploying across multiple nodes. This mode is suitable for deploying different services on multiple node servers.
  2. Subnet Bridge mode suitable for deploying multiple service processes on a single node. This mode is suitable for deploying multiple services on a single node.
For demonstration purposes, this chapter only demonstrates the script written in Subnet Bridge mode.

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Docker Compose Script

Deploying the Xpert analytics platform requires 5 basic services, which are defined in the docker-compose.yml file:
  1. db: Database service, using the postgres database
  2. redis: Server cache service, using the redis database
  3. olap: OLAP engine service, using the Xpert-olap image
  4. api: Backend service, using the ocap-api image
  5. webapp: Frontend service, using the ocap-webapp image