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https://github.com/larsgeb/usagedb
https://github.com/larsgeb/usagedb
Last synced: 6 days ago
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- Host: GitHub
- URL: https://github.com/larsgeb/usagedb
- Owner: larsgeb
- Created: 2023-10-04T12:17:54.000Z (about 1 year ago)
- Default Branch: master
- Last Pushed: 2023-10-04T12:35:59.000Z (about 1 year ago)
- Last Synced: 2024-10-09T08:08:16.662Z (28 days ago)
- Language: HTML
- Size: 13.7 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
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README
# Usage Data Web Application
This web application allows you to visualize and analyze system usage data collected from various users on a single machine. It utilizes Python, Flask, SQLite, and Chart.js to display CPU and memory usage data in a user-friendly format.
## Installation
1. Clone the repository to your local machine:
```bash
git clone https://github.com/larsgeb/usage-data-web-app.git
```
Install the required Python packages using pip:
```bash
Copy code
pip install -r requirements.txt
```
## Configuration
The usage data is stored in an SQLite database located by default at `/var/usagedb/usage_data.db`. Make sure this database exists:
```bash
sqlite3 usage_data.db < create_schema.sql
```
You can set up a CRONTAB job to periodically log system usage data to the SQLite database.
```bash
* * * * * root /var/usagedb/collect_and_plot.sh
```
Customize the configuration.py file:
- `machine_id`: The identifier for your machine (e.g., "swppc").
- `physical_cores`: The number of physical CPU cores on your machine.
- `total_system_memory`: The total system memory in GB on your machine.
- `excluded_users`: A list of users to exclude from data analysis (e.g., ['syslog', 'cups-pk-helper']).
- `local_database_path`: The path to the local SQLite database.
## Usage
Start the Flask application in a tmux shell:
```bash
export FLASK_APP=app.py
flask run --port 4999 --debug
```
Open a web browser and navigate to http://localhost:4999 to access the usage data dashboard.
## Technologies Used
This usage data dashboard is built using a combination of technologies to collect, process, and display system usage data effectively. The core technologies and libraries used include:
- **Python**: The primary programming language used for developing the web application and data collection scripts.
- **Flask**: A lightweight and powerful web framework for Python used to build the web application.
- **SQLite**: A self-contained, serverless, and zero-configuration database engine used to store and retrieve system usage data.
- **Chart.js**: A popular JavaScript library for creating interactive and visually appealing charts and graphs. It is used to visualize CPU and memory usage data.
- **Pandas**: A Python library for data manipulation and analysis. It is used to process and structure the collected data.
- **CRONTAB**: A time-based job scheduler in Unix-like operating systems used to automate the collection of system usage data at regular intervals.
- **psutil**: A Python cross-platform library that provides an interface for retrieving information on system utilization (CPU, memory, disks, network, sensors) and running processes.
- **subprocess**: A Python module used for spawning new processes, connecting to their input/output/error pipes, and obtaining their return codes.
These technologies work together to provide a user-friendly interface for monitoring and analyzing system resource utilization over time.