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MCP Server With Docker Redis And Timescaledb

This project uses Docker to build an MCP server that integrates Redis and TimescaleDB, providing features such as fast deployment, automatic restart, and useful scripts.
2.5 points
20

What is the MCP server?

The MCP server is a model context protocol server built on the FastAPI framework, designed for efficient processing and management of data streams. It uses Redis to provide fast caching capabilities and achieves efficient data storage and query through TimescaleDB (a time-series database based on PostgreSQL).

How to use the MCP server?

The MCP server is deployed through Docker containers. Users can complete the installation and startup with just a few simple commands. The server configuration is managed through environment variables and supports custom settings to meet different needs.

Use cases

The MCP server is very suitable for application scenarios that require efficient processing of time-series data, such as IoT device data monitoring, real-time analysis systems, and applications that require fast caching and retrieval of data.

Main Features

FastAPI FrameworkUses FastAPI as the Web framework to provide high-performance API services and support asynchronous request processing.
Redis CacheIntegrates Redis to provide a fast data caching mechanism, significantly improving data access speed.
TimescaleDBA time-series database based on PostgreSQL that optimizes the storage and query performance of time-series data.
Docker ComposeUses Docker Compose to orchestrate multi-container applications, simplifying the deployment and management process.
Systemd ServiceSupports management through systemd services to ensure that the server automatically resumes operation after system restart.

Advantages and Limitations

Advantages
Fast deployment: Simplifies the installation and configuration process through Docker containerization.
High performance: Combines FastAPI and Redis to provide efficient API responses and data caching.
Scalability: TimescaleDB supports efficient management of large-scale time-series data.
Automation: Achieves automatic restart through systemd services, improving service reliability.
Limitations
Resource consumption: Running multiple containers (Redis, TimescaleDB) may require high system resources.
Learning curve: Users who are not familiar with Docker and container technologies may need a certain amount of learning time.
Configuration complexity: Advanced configuration may require modifying environment variables and Docker settings.

How to Use

Clone the Repository
Use the git command to clone the MCP server code repository to the local machine.
Configure Environment Variables
Copy the example environment file and modify the configuration as needed.
Build and Start Containers
Use Docker Compose to build images and start services.
Access the Service
Access the MCP server through a browser or API client.

Usage Examples

IoT Device MonitoringUse the MCP server to collect and store sensor data from multiple IoT devices, and query device status in real-time through the API.
Real-time Analysis DashboardBuild a real-time data analysis dashboard to obtain cached data from the MCP server and display real-time charts.

Frequently Asked Questions

How to modify the server port?
Where is the data stored?
How to back up the data?
What are the system requirements?

Related Resources

FastAPI Official Documentation
Official documentation and tutorials for the FastAPI framework.
TimescaleDB Documentation
Official documentation for the TimescaleDB time-series database.
Redis Documentation
Official documentation for the Redis in-memory database.
Docker Documentation
Official documentation for Docker container technology.
Installation
Copy the following command to your Client for configuration
Note: Your key is sensitive information, do not share it with anyone.
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