Log Analysis SQLite

🚀 Log Analysis with SQLite MCP Server

This project provides tools to create an SQLite database from compressed log files and interact with it using the Model Context Protocol (MCP) SQLite server.

🚀 Quick Start

📦 Installation

python3 -m venv venv
source venv/bin/activate
pip3 install -r requirements.txt

Place the log files in a folder as .gz files, then run:

python3 create_log_db.py 

🔧 MCP SQLite Server Configuration

To configure the MCP SQLite server in Cursor:

• Go to Cursor settings
• MCP
• Add a new MCP server
• Name: SQLlite
• Set the type to Command line
• Put the following content in the command box:

npx -y @smithery/cli@latest run mcp-server-sqlite-npx --config "{\"databasePath\":\"/path/to/thedatbase/logs.db\"}"

📚 Documentation

📄 Project Contents

• create_log_db.py: A script to extract and parse log files into an SQLite database.
• query_logs.py: A script to directly query the SQLite database.
• logs.db: An SQLite database containing parsed log data.

🗃️ Database Structure

The database contains the following tables:

logs Table

• id: A unique identifier for each log entry.
• timestamp: The timestamp of the log entry.
• thread: The thread that generated the log.
• level: The log level (INFO, WARN, ERROR, DEBUG).
• module: The module that generated the log.
• message: The content of the log message.
• source_file: The source log file.
• raw_log: The original log entry.

stack_traces Table

• id: A unique identifier for each stack trace.
• log_id: References the associated log entry.
• stack_trace: The full stack trace text.

parsing_errors Table

• id: A unique identifier for each parsing error.
• line: The line that could not be parsed.
• source_file: The source log file.
• error_message: An error message explaining the reason for the parsing failure.
• timestamp: The time when the parsing error occurred.