PySimpleGUI App for Monitoring Cluster CPU Load: Code Review

Code Review: PySimpleGUI app to monitor cluster CPU load

In this tutorial, we will walk through a code review of a PySimpleGUI application used to monitor the CPU load of a cluster. Code reviews are important to ensure the correctness, readability, and maintainability of code, which is crucial for the long-term success of any software project.

PySimpleGUI is a Python library that provides a simple and intuitive way to create desktop GUI applications. It is based on the Tkinter library and makes it easy to create graphical interfaces for your Python applications.

The code we will review in this tutorial is a PySimpleGUI application that displays the CPU load of a cluster. The application fetches the CPU load data from each node in the cluster and displays it in a table format. The user can also set a threshold for the CPU load and receive alerts when the CPU load exceeds this threshold.

Let’s start by reviewing the code:

import PySimpleGUI as sg
import psutil

# Get the CPU load of all cores
cpu_percent = psutil.cpu_percent(percpu=True)

# Create the layout for the GUI
layout = [
    [sg.Text('Cluster CPU Load')],
    [sg.Table(values=cpu_percent, headings=['CPU Core', 'Load (%)'], auto_size_columns=True, justification='center')],
    [sg.Text('Set threshold for CPU load (%)'), sg.InputText()],
    [sg.Button('Set Threshold'), sg.Button('Exit')]
]

# Create the window
window = sg.Window('Cluster Monitor', layout)

while True:
    event, values = window.read()

    if event == sg.WIN_CLOSED or event == 'Exit':
        break
    elif event == 'Set Threshold':
        threshold = int(values[0])
        alert = [i for i, val in enumerate(cpu_percent) if val > threshold]
        if alert:
            sg.popup(f'Warning: CPU load in core(s) {alert} exceeds threshold ({threshold}%)')

window.close()

Now, let’s break down the code and discuss its key components:

1. Import statements: The code imports the necessary libraries, including PySimpleGUI and psutil. Psutil is a Python library for retrieving system information, including CPU load data.

2. Fetch CPU load data: The code uses the psutil.cpu_percent() function to retrieve the CPU load of all cores in the cluster. The percpu=True argument specifies that we want the CPU load for each individual core.

3. Create the GUI layout: The code defines the layout for the PySimpleGUI window, which includes a text label, a table to display the CPU load data, an input box for setting the threshold, and buttons for setting the threshold and closing the application.

4. Main event loop: The code enters a while loop to handle events from the PySimpleGUI window. It listens for user interactions, such as clicking buttons or closing the window.

5. Event handling: The code checks for different events, such as closing the window (WIN_CLOSED) or clicking the "Exit" button. If the user clicks the "Set Threshold" button, the code retrieves the threshold value from the input box and compares it to the CPU load data. If any core’s CPU load exceeds the threshold, a warning message is displayed using the sg.popup() function.

6. Closing the window: Finally, the code closes the PySimpleGUI window when the user clicks the "Exit" button or closes the window.

Overall, the code is well-structured and easy to understand. However, there are a few areas that can be improved:

1. Error handling: The code does not include error handling for invalid input or exceptions that may occur during the execution. It is essential to handle errors gracefully to prevent the application from crashing.

2. Code modularity: The code could be further modularized by separating the event-handling logic into separate functions. This would make the code more organized and easier to maintain.

3. UI design: The GUI layout could be enhanced by adding more features, such as graphs or charts to visualize the CPU load data. Improving the aesthetics of the interface can make the application more user-friendly.

In conclusion, reviewing code is an essential practice to ensure the quality of software applications. By following best practices, such as proper error handling, code modularity, and UI design, we can create robust and effective applications like the PySimpleGUI app for monitoring cluster CPU load.