MONITOR DISK USAGE WITH PYTHON LANGUAGE

Monitor high disk usage in your Python application

When it comes to developing applications in the realm of Python, managing persistent data is a recurring necessity. This can take various forms, such as handling JSON, CSV, or text files on local disks, uploading files to cloud storage solutions like S3 or Google Cloud Storage, or storing data in databases like MongoDB or MySQL. Irrespective of the method chosen, the utilization of disk storage is a pivotal factor for Python applications and has a substantial impact on the user experience.

Hence, it's imperative to implement robust disk usage monitoring for Python applications, whether they are operating in a local environment or residing in the cloud. The significance of this lies in the fact that exceeding a certain disk usage threshold can lead to application crashes and unavailability, resulting in substantial revenue loss and a compromised user experience.

Enter Palzin Track, a potent event tracking tool seamlessly integrated with Python. Palzin Track simplifies the process of tracking crucial events in Python applications in real time. An exemplary use case for Palzin Track involves real-time tracking of disk usage, coupled with the ability to establish alert rules that promptly notify your team when disk usage surpasses predefined thresholds. This proactive approach ensures continuous performance awareness, enabling immediate corrective action when necessary.

Connect Palzin Track to Python

Setting up Palzin Track

1. Sign up for a free Palzin Track account.
2. Create your first project from the dashboard.
3. Head to settings and copy your API token.

Python code snippets

Use the following code to connect Palzin Track to your Python application and track disk usage: Please replace the YOUR_API_TOKEN with your API token and update the project and channel names.

Using Python with http.client

import http.client  


import json  


conn = http.client.HTTPSConnection("palzin.live")  


payload = json.dumps({  


 "project": "my-project",  


 "channel": "status",  


 "event": "High Disk Usage",  


 "description": "The disk usage is high.",  


 "icon": "💾",  


 "notify": True  


})  


headers = {  


 'Content-Type': 'application/json',  


 'Authorization': 'Bearer YOUR_API_TOKEN'  


}  


conn.request("POST", "/api/v1/log", payload, headers)  


res = conn.getresponse()  


data = res.read()  


print(data.decode("utf-8"))

Using Python with Requests

import requests  


import json  


url = "https://api.palzin.live/v1/log"  


payload = json.dumps({  


 "project": "my-project",  


 "channel": "status",  


 "event": "High Disk Usage",  


 "description": "The disk usage is high.",  


 "icon": "💾",  


 "notify": True  


})  


headers = {  


 'Content-Type': 'application/json',  


 'Authorization': 'Bearer YOUR_API_TOKEN'  


}  


response = requests.request("POST", url, headers=headers, data=payload)  


print(response.text)

Python integration details

In addition to real-time event tracking, Palzin Track provides powerful features such as cross-platform push notifications, event filtering, user and product journeys, charts, insights, and more. Via Palzin Track, you can get better insight into your Python application and track anything important all in one place and in real time.

We strive to make event tracking simple and accessible to every developer and team. Therefore, we have worked hard to create the next generation of event tracking tools. As a result, Palzin Track is flexible and easy to use, making it a great companion for your Python applications.

Other use-cases for Palzin Track

1. Monitor your CI/CD build status for your Python application
2. Monitor your CPU usage in your Python application
3. Monitor when database goes down in your Python application
4. Monitor when a user changes their email address in your Python application
5. Monitor failed logins in your Python application
6. Monitor failed payments for your Python application
7. Monitor memory usage in your Python application
8. Monitor MySQL downtime in your Python application
9. Monitor when a new feature is used in your Python application
10. Monitor your Postgres downtime in your Python application
11. Monitor Redis downtime in your Python application
12. Monitor suspicious activity in your Python application
13. Monitor when a user exceeds the usage limit for your Python service
14. Monitor when a user is being rate limited in your Python application
15. Get a notification when your Python code is done executing
16. Send push notifications to your phone or desktop using Python
17. Track canceled subscriptions in your Python application
18. Track your Python cron jobs
19. Track when a file is uploaded to your Python application
20. Track when a form is submitted to your Python application
21. Track payment events via Python
22. Track user sign in events in Python
23. Monitor user signup events via Python
24. Track waitlist signup events via Python